Magic Quadrant for Observability Platforms
7 July 2025 - ID G00821166 - 61 min read
By Gregg Siegfried, Matt Crossley, and 3 more
Observability platforms are fundamentally changing how organizations manage system health, driven by innovations in analytics, cost optimization, and the emergence of AI observability. Heads of I&O can use this research to evaluate and navigate the evolving landscape of vendors and solutions.
Market Definition/Description
This document was revised on 17 July 2025. The document you are viewing is the corrected version. For more information, see the Corrections page on gartner.com.
Gartner defines observability platforms as products used to understand the health, performance and behavior of applications, services and infrastructure. They do this by ingesting telemetry (operational data) from a variety of sources including, but not limited to, logs, metrics, events and traces. Observability platforms enable analysis of the ingested telemetry, either via human operator or machine intelligence, to determine changes in system behavior that impact end-user experience, such as outages or performance degradation. This allows early, and even preemptive, problem remediation. Observability platforms are used by IT operations, site reliability engineers, cloud and platform teams, application developers and product owners.
Modern businesses rely heavily on critical digital applications and services, which are revenue-generating, client-facing and important to the efficient operation of the business. Outages, performance degradation and unreliability directly impact top-line revenue, client sentiment and brand perception.
Observability platforms are used by organizations to understand and improve the availability, performance and resilience of these critical applications and services. Investment in and successful deployment of observability platforms leads to revenue loss avoidance and enables faster product development cycles and improvements in brand perception.
Example use-case scenarios or business problems addressed by observability platforms include:
- IT operations: IT operations teams responsible for live production environments are tasked with ensuring that applications and services are available, responsive and performant at all times — and especially during periods of high demand. Observability platforms allow these teams to be alerted when issues are detected, and make it possible to interrogate the data to identify the underlying cause.
- Platform engineering: Platform engineers’ use of observability platforms resembles that of IT operations as well as software development. Observability platforms help these teams ensure that production environments consistently meet service-level objectives, in addition to supporting data-driven continuous improvement and platform evolution.
- Software development: Development teams use observability platforms as an integrated part of the CI/CD pipeline, providing rapid feedback on code deployments. This enables faster delivery of new features, as well as improved product resilience.
- Business analyst: Business analysts may use observability platforms to understand and analyze key business metrics. These metrics are often specific to the organization and client (for example., a retailer measuring the cost of abandoned shopping carts and average customer spend).
Mandatory Features
At a minimum, observability platforms must:
- Ingest, store and analyze operational telemetry feeds, including (but not limited to) metrics, event, log and trace data.
- Identify and analyze changes in application, service and infrastructure behavior to determine the causes of outages, performance degradation and quantify their impact on end-user experience.
- Enrich telemetry by providing contextualization, such as topological dependency or service mapping.
- Support the modeling or mapping of relationships between monitored services and their role in business transactions.
- Collect telemetry from public cloud providers (for example, Amazon Web Services, Microsoft Azure and Oracle Cloud Infrastructure).
- Support interactive exploration and analysis of multiple telemetry types (including traces, metrics and logs) to generate insights about user and application behavior.
Common Features
The common features for this market include:
- Monitoring of digital experience of applications and services delivered via browser, mobile app and API.
- Integration with other operations, service management and software development technologies, such as IT service management (ITSM), configuration management database (CMDB), event and incident response management, orchestration and automation, and DevOps tools.
- Providing insights through the use of advanced analytics and machine learning that are otherwise not possible or feasible to derive through manual interrogation and analysis of data.
- Automated discovery and mapping of related infrastructure, network and application components and services.
- Cost management that supports measuring and optimizing application workload cost, as well as measuring and optimizing observability platform utilization or spend.
- Business process and activity monitoring reflecting user journeys such as login to check-out, funnel analysis to track conversion rates, customer onboarding or loan application.
- AI observability capabilities including the ability to analyze the performance, cost, capacity and compliance of large language models and associated generative AI workloads.
- Automation capabilities that support initiating changes to application and infrastructure code and configuration to optimize workload cost, capacity or performance, or to take corrective action to remediate failure or degradation.
- Application security functionality, such as the identification of known vulnerabilities in monitored applications and the ability to block attempts to exploit them.
Magic Quadrant
Vendor Strengths and Cautions
Amazon Web Services
Amazon Web Services (AWS) is a Challenger in this Magic Quadrant. Its observability solution is centered on Amazon CloudWatch, which is a suite of tools for metrics, logs and events, and other telemetry types. Additional AWS components include AWS X-Ray for distributed tracing, Amazon OpenSearch Service for log analysis, Amazon Managed Service for Prometheus and Amazon Managed Grafana. This set of services is part of AWS Cloud Operations, which also includes governance and financial management. AWS customers and operations are geographically distributed. Its clients are organizations of all sizes. AWS releases and announces product updates regularly.
- AI innovation: Amazon announced Amazon CloudWatch investigations to help SREs, IT operations and cloud engineering teams rapidly diagnose, troubleshoot and remediate operational issues. Additionally, AWS launched natural language query generation for Amazon CloudWatch, AWS CloudTrail, Amazon OpenSearch Service and AWS Config, which will enable a wider audience to interrogate and analyze performance issues.
- Customer experience: AWS provides one of the most comprehensive customer support experiences in the market, with strong custom onboarding support for clients of all sizes, global 24/7 support, vibrant community forums and an industry-leading certification program.
- Ecosystem: The native integration of AWS observability tools such as CloudWatch provides seamless telemetry collection from AWS services with minimal setup. This enables simple configuration through common UIs, unified management and billing via the AWS console, and consistent security through IAM.
- Multicloud workloads: Amazon CloudWatch is designed and optimized for integration with AWS services. AWS does provide a limited prebuilt integration for ingestion of metrics from Microsoft Azure, other data sources will need to use the OpenTelemetry (OTel) agent, CloudWatch agent or custom configuration. Users will also need to carefully assess charges, such as egress of metrics from other environments, as well as the cost of ingestion into AWS.
- Marketing strategy: Unlike many of the vendors in this Magic Quadrant, observability is not the leading product for AWS. As such, AWS marketing efforts specifically for observability lag the Leaders in this research.
- Costs: Client feedback, on inquiry as well as on Peer Insights, frequently mention high costs associated with Amazon CloudWatch and other tools. While cost challenges are a common issue in observability, the integrated nature of AWS — where services can easily push metrics and logs into CloudWatch — increases the likelihood of unexpected spend. Clients should ensure they are using the appropriate admin tools, such as AWS Cost Explorer and AWS Budgets, and explore the setting for log ingestion frequency and tracing to reduce ingestion costs.
Apica
Apica is a Visionary in this Magic Quadrant. Apica Ascent is its observability platform and includes telemetry pipeline capabilities in its comprehensive data management portfolio. Its operations are focused in the U.S. and EMEA, and its customers are concentrated in North America. Founded in 2005, Apica originally specialized in synthetic monitoring and was well-known for capabilities such as support for MFA in synthetic workflows. The company pivoted into observability via the acquisitions of LogIQ in 2023 and Circonus in 2024, and is migrating its synthetic monitoring capabilities into the Ascent platform.
- Ease of adoption: Apica Ascent does not distribute or require a custom agent to onboard data. Instead, customers can use any or all of a variety of collection technology, including the OpenTelemetry collector, Fluent Bit, Logstash and even the Datadog agent. Flow, the Ascent telemetry pipeline solution, offers additional options to shape and ingest incoming telemetry.
- Agent fleet management: In addition to data source flexibility, Apica’s agent management subsystem, called Fleet, supports automating the installation and configuration of these telemetry collectors. Based on the industry standard OpAMP protocol, Fleet is class-leading and merits evaluation even if Ascent’s observability capabilities are not in use.
- Bring your own storage: Apica Ascent allows customers of its SaaS product to provide their own object storage for their data. This gives Ascent users complete control over the location of their data, which can simplify compliance and supports data sovereignty requirements.
- Product completeness: Although capable of supporting advanced use cases such as AI observability, Apica Ascent is still evolving and lacks features such as native support for service-level objective (SLO) management and real-user monitoring (RUM).
- Limited mind share: Apica has been around as a company for 20 years, but it is not well-known as an observability platform provider and is not yet a common choice among Gartner clients.
- Relative size: Apica is significantly smaller in size compared to most other companies included in this research. In a highly competitive market, this relative scale difference is notable.
BMC Helix
BMC Helix is a Niche Player in this Magic Quadrant. The BMC Helix Observability & AIOps suite delivers a range of IT operations and observability capabilities and consists of several products, including BMC Helix Discovery and other components. BMC Helix has a significant presence in the adjacent IT service management market with its BMC Helix ITSM (formerly Remedy) product. BMC Helix’s operations are geographically diversified, with a global footprint and support for clients of all sizes and sectors. In October 2024, BMC announced the creation of two independent companies, splitting itself into two separate operating organizations; one retains the name BMC, and the other is now called BMC Helix. For purposes of the observability platform market, this research focuses on BMC Helix products.
- Innovation: BMC Helix has made a number of investments and enhancements to its product suite by embedding AI throughout its products. This includes the BMC HelixGPT, a generative AI solution for assisting with investigation of incidents.
- Global presence: BMC Helix’s customers are geographically distributed, supported by a broad set of local offices, partners in each major region and worldwide hosting choices.
- Service operations: Although designed to coexist with third-party tools, BMC Helix’s approach integrates its own ITSM, discovery and CMDB tools with the observability solutions, opening the potential for significant tool and vendor consolidation.
- Strategic direction uncertainty: Clients should be aware of operational changes during BMC Helix’s ongoing transition into an independent company. This will include potential shifts in account relationships, customer support models and a period of adjustment as both entities solidify their operations.
- Market execution: BMC Helix marketing has focused on building awareness among existing customers and stakeholders rather than the observability market at large. This may explain why Gartner clients infrequently mention BMC Helix and it does not appear often on competitive shortlists.
- Product completeness: BMC Helix’s solution lacks some features present among leaders in the market, including limited cost-control tools and no support for eBPF. While workarounds can be implemented which can provide some of the missing functionality, it may require additional spending or affect time to value.
Chronosphere
Chronosphere is a Leader in this Magic Quadrant. Its observability platform comprises the Chronosphere Observability Platform and the Chronosphere Telemetry Pipeline. Its operations are mainly focused in the U.S. and EMEA, and its customers are concentrated in North America. Recent product enhancements include Differential Diagnosis (DDx) for Traces, a capability that helps operators identify the cause of degradations or downtime more quickly. DDx support for metrics was released in May 2025, and DDx for logs is on the roadmap along with business-impact analysis.
- Cost optimization: The Chronosphere control plane enables customers to closely manage the ingestion, storage and retention of incoming telemetry using granular policy controls. Chronosphere’s ingestion controls have inspired similar capabilities among other observability platform vendors.
- Mostly agentless: Chronosphere employs the core maintainers of the popular, open-source telemetry processor and forwarder called Fluent Bit. But the Chronosphere observability platform requires no agents and relies largely on open protocols — such as OpenTelemetry and Prometheus — to ingest telemetry into its platform. Customers can choose the mechanism that is most convenient for a given workload.
- Highly available: Customers of Chronosphere are provisioned into their own tenant with private storage. This reduces the chance of resource contention between customers and offers an additional layer of security. This has resulted in Chronosphere consistently meeting its 99.9% availability service-level agreement (SLA).
- Digital experience monitoring: Chronosphere currently offers digital experience monitoring (DEM) via partnerships with Checkly for synthetic monitoring and Sentry for real-user monitoring (RUM). DEM telemetry can be ingested, analyzed and visualized using Chronosphere, just as with any other metrics, events, logs and traces (MELT) telemetry. But generating it and getting it to Chronosphere is a customer responsibility that can be facilitated through the aforementioned partners.
- Permissions structure: Chronosphere includes a coarse role and permission structure that does not support object-based separation of duties. Organizations with these sorts of requirements would require multiple Chronosphere tenants or the use of a GitOps-based mechanism.
- Nominal AI: Unlike many vendors in this research, Chronosphere has not emphasized AI capabilities in its observability platform. To date, this has not materially reduced the quality of insights and actions available from the platform, but organizations concerned about the presence of specific AI-based capabilities may choose to evaluate other solutions.
Coralogix
Coralogix is a Visionary in this Magic Quadrant. The Coralogix platform supports observability and security use cases and is based on a data pipeline architecture that the company brands as Streama. Its operations are focused in the U.S. and EMEA, and its customers are found mainly in North America and EMEA. Recent product releases have included the Coralogix AI Center (based on the acquisition of Aporia) as well as an eBPF-based telemetry collection agent. Its roadmap for the platform includes enhancements to continuous profiling and the AI Center, as well as the introduction of agentic AI.
- Storage optimization: The Coralogix TCO Optimizer allows customers to balance cost and performance by supporting policy-driven tiering and retention of log and trace telemetry based on access and usage requirements. This includes use of a customer’s own S3 bucket.
- AI enablement: The Coralogix AI Center comprises tools that monitor the health, performance and security of LLM-based applications. Measurements available include token usage, errors, response quality issues and cost. The current version supports both OpenAI and Amazon Bedrock. Coralogix also includes an onboard GenAI assistant called Cora.
- Customer support: Coralogix prioritizes customer support and offers 24/7, in-product support with a median response time of 17 seconds and a median resolution time of 1 hour. New customers are assisted with onboarding by a customer success team that may include solution architects, implementation engineers, customer success architects and technical project managers.
- Learning curve: The Coralogix platform is a highly capable observability tool, but the high degree of capability brings complexity with it. Some customers have reported a steep learning curve and an overwhelming number of options as they familiarize themselves with the product.
- Fleet management: Coralogix does not offer centralized control over the deployment, configuration, and life cycle of agents or collectors. This may lead to increased operational overhead and hamper scalability for larger implementations.
- Integrated development environment (IDE) integration: At present, Coralogix lacks a mechanism that allows developers to interact with its observability platform from an IDE for diagnosing production problems. Organizations whose workflow requires that capability may wish to evaluate other solutions.
Datadog
Datadog is a Leader in this Magic Quadrant. Its observability platform is a part of a broader suite of monitoring and security capabilities. Datadog maintains a globally diverse focus, continuing to expand its points of presence beyond North America and Europe. In 2023, it established a data center in Japan, and it has announced plans for Australia in 2025. In 2024, Datadog introduced new capabilities, including LLM Observability, aimed at supporting generative AI workloads, and On-Call, a tool for managing incident response. The company recently made several notable acquisitions: Quickwit (log search optimization), Metaplane (data observability), and Eppo (feature flagging and experimentation). These additions broaden Datadog’s coverage across observability, data quality and product performance measurement.
- SLO management: Datadog provides extensive capabilities for managing service-level objectives across data types, including historic data replay, error budget alerting and best-practice recommendations.
- Extended Berkeley Packet Filter (eBPF) use: The platform leverages eBPF Linux kernel technology extensively for a range of use cases, such as application performance monitoring (APM) and cloud workload protection, providing deep visibility into system and application behavior without the need for instrumentation and with minimal overhead.
- Product analytics: The platform leverages RUM, funnel analysis, session replay and heat maps to address questions that extend beyond traditional application health and performance. These capabilities are positioned to support decision making related to product adoption, user behavior and the prioritization of new-feature development.
- Licensing model: Determining requirements and negotiating contracts can be challenging due to the extensive and growing suite of product lines offered by Datadog. Limited flexibility across these product lines adds to the complexity of customer budget forecasting.
- Cost: The cost of Datadog remains a concern among Gartner clients — specifically, log ingestion and retention, and custom metric ingestion at scale. Datadog has responded to these challenges with features such as Flex Logs storage and its “without limits” model.
- Vendor lock-in: Datadog’s ease of use and short time to value can be quite attractive, but its tightly integrated ecosystem can make the cost and complexity of switching away or integrating with non-Datadog tools a challenge for customers.
Dynatrace
Dynatrace is a Leader in this Magic Quadrant. Dynatrace’s unified observability and security platform comprises multiple components, including Infrastructure and Application Observability, Application Security and Threat Observability, Digital Experience, Automations and Business Observability. The company has recently added cost optimization and AI/LLM observability to its product offering. Dynatrace has clients in all major geographies, including LATAM and APAC. Its customers tend to be large enterprises and technology-centric companies. The company recently acquired Metis, an AI-driven database observability platform.
- Portfolio breadth: Dynatrace offers a wide array of solutions for observability and security, making it particularly attractive for larger enterprises. This includes observability for modern architectures such as Kubernetes, containers, cloud functions and large language models (LLMs), as well as monitoring for legacy enterprise solutions, such as mainframe and SAP monitoring.
- AI-powered automation and root cause analysis: A core strength of Dynatrace is its AI engine, Davis, which provides automated root cause analysis and predictive modeling. It can automatically discover and map complex application environments, identify performance anomalies and pinpoint the precise cause of problems in real time, significantly reducing manual effort and mean time to repair (MTTR). This automation extends to baselining, detecting anomalies and providing actionable insights.
- Scalability and enterprise focus: Dynatrace is designed to handle large, complex and dynamic enterprise environments, including microservices, containers and multicloud architectures. Dynatrace can scale to monitor tens of thousands of hosts and millions of dependencies, making it suitable for large organizations with extensive IT landscapes. Its robust architecture and automated approach help manage the complexity and scale inherent in modern enterprises.
- License model: Dynatrace Platform Subscription (DPS) contracts include many line items not present in previous licensing models. While providing additional capabilities, they also increase the difficulty for procurement to understand and predict observability costs. Clients should ensure that teams are fully aware of the cost-control mechanisms for reporting, alerting and prediction in the Account Management interface.
- Platform complexity: Due to the sheer number of features and the depth of data available in Dynatrace, new users may require onboarding assistance. To maximize return on investment, organizations must prioritize training and active user adoption. Clients should look to use either vendor or trusted third-party services to assist with initial deployments.
- Suitability for SMBs: Dynatrace is a fully featured observability platform primarily targeted toward larger enterprises. While alternative channels such as public cloud marketplaces make it more accessible, small and midsize businesses may find that cost justification limits Dynatrace use to monitoring the most business-critical systems.
Elastic
Elastic is a Leader in this Magic Quadrant. The company offers a product portfolio available across multiple deployment models: self-hosted, cloud-hosted, and fully managed SaaS with a serverless architecture. Elastic Observability is built on a foundation of its popular Search AI Platform, which also underpins its search and security products. Elastic Cloud is a managed service available on most major cloud providers. Its Serverless option is a fully managed, usage-based offering delivered within Elastic Cloud. Headquartered in North America, Elastic’s customers are primarily in the Americas and EMEA. Elastic’s roadmap includes enhancements to its GenAI capabilities and further standardization on OpenTelemetry.
- AI augmentation: Elastic’s AI assistant helps users identify issues and find solutions quickly by querying large volumes of data in a natural language format, which enables wider access to insights without needing to learn SQL-like language.
- Product: Elastic’s platform provides market-leading capabilities for observability, including strong support for SLOs, fleet management and strong analytics capabilities, which will increase the productivity of IT operations and SRE teams.
- Vision: Elastic’s vision as an open-source platform for not only observability telemetry but also security and enterprise search is differentiated from other vendors in the market.
- Market awareness and reach: While its search and security products are relatively well-known, awareness and adoption of Elastic Observability is still comparatively low. Elastic is building sales and marketing traction for its observability offering.
- Required expertise: While Elastic Cloud removes the significant burden of infrastructure management associated with self-managed deployments, unlocking the platform’s full potential still requires a considerable level of in-house technical expertise.
- Pricing estimate: Elastic Cloud’s pricing model (based on RAM, storage, data transfer and feature tiers) makes estimating and forecasting usage difficult as data volumes grow. This challenge is somewhat mitigated by the platform’s pricing calculators and data tiering capabilities.
Grafana Labs
Grafana Labs is a Leader in this Magic Quadrant. Grafana Labs was founded on the popular open-source project Grafana. The company has since launched other open-source projects, such as Loki, Tempo, Mimir, Beyla and Faro, and employs a substantial number of Prometheus and OpenTelemetry maintainers. Grafana Cloud is Grafana Labs’ observability platform. Its customers are global, but centered in North America and EMEA. Recent updates to Grafana Cloud include unified incident response management and cloud provider observability. Enhanced root cause analysis and simplified cost management are on Grafana Cloud’s future roadmap.
- Cost management: Grafana Cloud’s Adaptive Telemetry features — currently supported for logs and metrics — enable customers to control costs by reducing the ingestion of unused or unimportant telemetry. Customers manage system recommendations and can choose to apply them, pause them or configure exceptions. Adaptive Telemetry for traces is on the roadmap.
- Geographic and CSP footprint: Grafana Cloud is hosted in 25 AWS, Microsoft Azure and Google Cloud Platform (GCP) cloud regions globally at the time this research was published. This extensive footprint enables customers to choose a location based on their latency requirements and data sovereignty needs.
- Customer experience: Grafana Labs offers high-touch, guided onboarding that includes activation and migration planning, architecture support and training. Beyond onboarding, a variety of support options and communities are available for customers to interact with each other as well as with the company.
- Learning curve: Users that lack Prometheus experience may find the syntax of configuration files and available options challenging. Organizations should anticipate a need for training to ensure their teams can maximize the value of the platform’s emerging capabilities. Grafana Labs is addressing this by offering an increasing number of out-of-the-box templates for popular use cases.
- Documentation: The open-source foundation of Grafana Cloud is apparent from the documentation. The components of the platform are documented, which is important. However, the docs appear incomplete in places and do not offer much to the beginner.
- Integration and compatibility: Grafana Labs and the Grafana community have created a large number of data source plugins and prebuilt dashboards; indeed, many advanced features and integrations rely on community-driven plugins. Operations teams must vet and manage the third-party components they incorporate into their Grafana Cloud environments in accordance with their risk management policies.
Honeycomb
Honeycomb is a Visionary in this Magic Quadrant. Its observability platform is built with a focus on high-cardinality telemetry. It embraces open standards and is targeted toward engineering teams with real-time, exploratory observability. Although Honeycomb’s primary customer base is in North America, it continues to build on its EU SaaS offering (launched in 2024) with further initiatives also targeting APAC and LATAM. In 2024, Honeycomb made significant additions to its capabilities with the launch of Honeycomb Telemetry Pipeline (HTP), Honeycomb Log Analytics, and Honeycomb for Frontend Observability. In early 2025, Honeycomb made its first acquisition, acquiring Grit, an open-source codebase analysis and autoinstrumentation engine.
- Telemetry management: HTP enables enterprises to efficiently route, filter and sample telemetry data across multiple sources and destinations, helping them scale observability while maintaining control over telemetry life cycle and associated costs.
- Fleet management: Honeycomb leverages OpAMP to provide fleet management for OpenTelemetry Collectors. This delivers the ability to centrally manage, configure and monitor large-scale OpenTelemetry deployments while minimizing operational overhead.
- Innovation: In April 2025, Honeycomb announced the acquisition of Grit, an AI codebase analysis engine. Grit’s technology allows software development teams to more readily implement OpenTelemetry in their code, thereby improving observability on existing codebases.
- Licensing model: Honeycomb’s differentiated event-based pricing encourages the ingestion of high-cardinality telemetry without incurring penalties. However, it can initially make usage estimation and forecasting challenging.
- Limited channel ecosystem: Honeycomb primarily operates through a direct sales model, with minimal reliance on channel partners. As a result, enterprises seeking localized expertise or third-party implementation and integration services may find partner options limited.
- Adoption of AI/ML capabilities: While Honeycomb has emphasized telemetry exploration through features like BubbleUp anomaly detection, its adoption of generative AI has been relatively limited, with Query Assistant being the primary offering. This has left Honeycomb trailing competitors — a gap it aims to close through the recent acquisition of Grit.
IBM
IBM is a Leader in this Magic Quadrant. IBM’s Instana Observability platform is offered as both a SaaS and self-hosted solution and uses a single-agent architecture. IBM’s operations are geographically diversified, and its clients tend to be large enterprises. IBM’s broader observability portfolio extends from mainframe monitoring to network monitoring via SevOne, and to modern cloud architectures such as containers and Kubernetes. IBM recently acquired HashiCorp, which sits alongside Instana as part of IBM’s automation portfolio. In addition, IBM acquired Kubecost, providing additional capabilities for Kubernetes cost monitoring and optimization.
- Market execution: IBM has a significant presence within enterprises globally, with sales and support in all major markets. Additionally, within IBM, Instana is part of the same software group that contains Apptio and HashiCorp, making a compelling enterprise bundle for IT operations, automation and FinOps.
- Geographic strategy: IBM’s sales and support teams and its large partner network provide localized customer support in all regions. IBM has also expanded its data center and cloud provider support to include more regions and deployment options, and its tools support multiple languages.
- Pricing model: In an era where clients are increasingly focused on cost, Instana’s pricing model, based on a per-host metric, is simple to understand, scales well with volume and is differentiated in the marketplace.
- Offering (product) strategy: IBM introduced fewer new and innovative AI features in 2024 compared to other Leaders evaluated in this research. While Instana is exploring the use of agentic capabilities and expanding its AI observability offerings, significant opportunity remains for IBM to strengthen its position in this area.
- Market perception: When discussing IBM Instana with clients during inquiry as a possible observability platform, many are inclined to disregard it as they perceive it to be only for large enterprises. Small or midsize enterprises are significantly less likely to consider IBM, unless they are already using IBM solutions, and sales opportunities rely on cross-selling from the existing client base.
- Customer experience: While online forums exist for users to connect, IBM Instana lacks the same level of engagement that exists in other vendor’s user communities.
ITRS
ITRS is a Niche Player in this Magic Quadrant. ITRS Analytics features a wide range of capabilities as part of the ITRS portfolio for observability, which includes the collection pipeline, analytics platform and customer-access layer. Its operations are focused in North America, Europe and APAC. ITRS recently introduced a collection pipeline to simplify getting telemetry into the product. Examples of capabilities that are on the roadmap include SLO support, agent fleet management and security threat detection.
- Pricing model: ITRS’s recently revamped pricing strategy provides bundling options for essential and enterprise tiers. As it migrates from multiple products to a single platform solution, ITRS has simplified options into two tiers, targeting larger and more complex IT environments.
- Real-time alerts: Based on its heritage in financial services, ITRS Analytics supports extremely fast ingestion and alerting. Its claim is less than one second, which may not apply to all situations, but still may be faster than many competitors.
- Granular RBAC: ITRS’s RBAC capability allows for fine-grained, auditable control at the entity level. Access can be restricted based on attributes or conditions. Authorization controls support fine-grained compartmentalizing of commands. This level of control is not commonly available from products in this market.
- Pricing transparency: Although ITRS has revamped its pricing and begun offering tiered bundling options, a publicly available price list is not available. Prospective customers must contact the vendor directly, making it difficult to estimate costs.
- GenAI missing: ITRS does not currently include any kind of generative AI or large language model support. Although capabilities are on the roadmap, this places the company behind competitors that are already moving from chat-based assistants to agentic AI.
- Agent management: ITRS does not have a centralized deployment system for its monitoring agents. Instead, agent installation depends on external tools like Puppet or Chef. While configuration changes can be managed centrally after deployment, the need to handle multiple specialized agents, such as NetProbe, ITRS Infrastructure Agent, and RUM collectors, adds complexity to setup and maintenance.
LogicMonitor
LogicMonitor is a Challenger in this Magic Quadrant. LogicMonitor’s collector-based LM Envision platform delivers hybrid observability. It ingests metrics, logs, traces and events across cloud, on-premises, SaaS, and containerized environments, and features GenAI-powered root cause analysis, alert clustering, and AI infrastructure observability, including Nvidia GPU and OpenAI monitoring. Its customers are mainly from North America and EMEA, with an expanding presence in APAC. LogicMonitor has made several releases in the last year, including tools such as Edwin AI for cross-domain event ingestion and event correlation; LM Co-Pilot, the GenAI chatbot; and LM Cost Optimization for managing cloud costs.
- Customer retention: LogicMonitor’s net retention for FY24 was recorded at 108%. The company saw strong expansion with existing customers in the enterprise and MSP segments. The contributing factors to this high retention rate are platform stickiness, rapid time to value and increasing multiproduct adoption.
- MSP and partner program: LogicMonitor brings in 80% of its revenue from partner programs. Its programs support SIs, VARs, MSPs and technology alliance partners, and it continues to build out its partner capabilities through strategic investment in resources, enablement and joint GTM programs to drive scalable growth.
- Robust hybrid coverage: LogicMonitor manages hybrid IT environments (on-premises, cloud, edge) using an agentless collection method. This approach effectively supports legacy and modern systems, helping organizations transition from or maintain hybrid environments.
- Immature SLI/SLO support: LogicMonitor currently trails other observability platforms in the maturity of its SLI/SLO capabilities. The platform lacks a streamlined approach for defining, establishing, and tracking SLIs SLOs, and error budgets — features that are typically sought by SRE practices.
- DevOps integration: LogicMonitor’s DevOps toolchain integration capabilities are limited and lack full development life cycle support, and additional tooling will be needed for CI/CD pipeline-specific capabilities. Its main focus is around observability and incident response, but it does have some integrations such as Ops Notes to track changes, providing some context to issues that have occurred.
- Real-user monitoring (RUM): LogicMonitor currently does not offer RUM, which may limit visibility into front-end performance and user experience. Organizations needing detailed insights into client-side behavior or user journeys may need to integrate a separate RUM solution or consider platforms with built-in RUM capabilities.
Microsoft
Microsoft is a Challenger in this Magic Quadrant. Azure Monitor is its observability platform and the Microsoft-Azure-native monitoring solution. The company has substantial geographic diversity, and its clients tend to be midsize to large enterprises. Recent updates to Azure Monitor include enhanced Azure Kubernetes Service monitoring and Log Analytics cross-regional workspace replication. Microsoft’s observability roadmap includes extended AI-driven observability and deeper integration with Microsoft Fabric and Microsoft Sentinel.
- Log analysis: Azure Monitor Log Analytics is a capable, mature log-management solution that continues to improve. Two updates introduced over the last year include support for a low-cost, third storage tier called Auxiliary Logs and Simple Mode that offers a low/no-code option to obtain insights from log data without learning the Kusto Query Language (KQL).
- AI-enhanced: Azure Monitor Application Insights includes AI-enabled code optimizations that identify potential performance bottlenecks based on live telemetry and recommend the code changes necessary to resolve them. This is in addition to the existing Microsoft Copilot in Azure.
- Integrated security monitoring: Microsoft’s security monitoring products, Sentinel and Defender, are built on and well-integrated with Azure Monitor, allowing the entire suite to support a unified analytics, reporting and incident response capability across operations and security functions.
- SLO management: Azure Monitor does not yet include a first-class SLO creation and monitoring capability, and lags competitors in this respect. In its absence, SLO management in Azure Monitor is possible but extremely cumbersome.
- Inconsistent OpenTelemetry support: Azure Monitor can ingest OpenTelemetry data via an exporter, but support for ingestion of the OpenTelemetry Protocol (OTLP) directly via a collector interface is absent. Client-side SDKs are available in the Azure Monitor OpenTelemetry Distro, and although Application Insights supports the analysis of OTel data, getting the data from applications into Azure Monitor can be quite complicated.
- Feature preview: Microsoft’s practice is to release Azure features in a “preview” state. Some features are in public preview for a year or more. Although these capabilities are available to all customers, support is limited and Microsoft does not recommend preview items for production use. Many Azure Monitor features have been kept in extended public preview.
New Relic
New Relic is a Leader in this Magic Quadrant. Its observability platform spans APM, AI monitoring, DEM, infrastructure and security monitoring, and log management. Its customer base is globally distributed, primarily comprising medium to large enterprises across various industries. With data centers in North America and Europe, New Relic further expanded its global footprint in 2024 by significantly growing its innovation center in India. New Relic’s roadmap emphasizes strategic investments in agentic AI integration, highlighted by recent partnerships with ServiceNow and GitHub Copilot.
- AI vision: New Relic sets itself apart in the observability space with a forward-looking vision for agentic orchestration. By introducing a standardized API for agent integration and a growing library of specialized agents, the platform encourages intelligent, cross-platform automation. Early partnerships with GitHub and ServiceNow highlight the potential for AI-driven workflows.
- Product: New Relic has made significant enhancements in its product portfolio in the last year, including additional eBPF functionality, LLM observability, additional cost controls and improvements to its generative AI interface.
- Customer experience: Clients report, both via Peer Insights and during client inquiry, high levels of satisfaction with New Relic for services and support.
- Sales execution: While New Relic continues to achieve high client renewal rates, its market growth since its acquisition by Francisco Partners and TPG and subsequent privatization appears to have proceeded at a more moderate pace compared to several leading competitors in the observability sector.
- Telemetry cost control: Consumption pricing for New Relic, based on the number of users and the volume of telemetry ingested can result in larger than expected costs for clients. New Relic’s recent introduction of a cost-optimizer capability is designed to assist customers experiencing telemetry spikes.
- Business metrics: New Relic’s Pathpoint provides a view into business-process metrics and “business observability.” However, few clients seem to be aware of these features to help them gain insights into their critical business processes.
Oracle
Oracle is a Challenger in this Magic Quadrant. Oracle’s Observability and Management (O&M) platform comprises a range of services, including Application Performance Monitoring, Log Analytics and Database Management, Ops Insights, and Vulnerability Detection and Patch Management. It is positioned to support monitoring of workloads across hyperscale cloud providers, private clouds and traditional data centers. Oracle has points of presence across all regions and a diverse client base spanning industries — including government agencies and organizations ranging from midsize to large enterprises.
Recently, Oracle introduced a GenAI-powered observability assistant designed to support query generation, troubleshooting and product guidance. Additional new features include Database Vulnerability Detection and an LLM observability capability. Oracle’s roadmap builds on these advancements, with a strong focus on AI, both to enhance observability and monitor GenAI workloads and their underlying infrastructure.
- Oracle database vulnerability detection: Oracle’s observability platform offers built-in database vulnerability detection to help organizations identify security risks, misconfigurations and compliance issues across Oracle databases.
- Sovereign cloud: For enterprises prioritizing data sovereignty, geopatriation, regional compliance or residency requirements, Oracle’s observability platform is available in 25 countries. It meets a wide range of certifications, including FedRAMP and GDPR. Organizations can choose from deployment options such as the EU Sovereign Cloud, U.S. Government Cloud or an OCI Dedicated Region hosted directly within their own data center.
- Onboarding and support: For organizations with skill or resource limitations, Oracle’s Observability platform benefits from a comprehensive support framework. This includes an extensive ecosystem of certified channel partners and system integrators, Oracle Observability Professional Certification, certifications for SREs and DevOps teams, and client engagement programs.
- Limited generative AI integration: Aside from a basic support assistant, Oracle has been relatively slow to embed generative AI capabilities into its observability platform. Other vendors in the evaluation have more deeply integrated generative AI for a broader set of use cases. Oracle plans to address this shortfall with an “‘observability assistant” in 2025.
- Business-centric observability: For teams focused on the resilience of critical business services, Oracle’s O&M platform lacks capabilities for measuring business outcomes such as SLOs and error budget management, which are foundational for SRE practices.
- OCI-centric strategy: Oracle O&M is tightly woven into its OCI strategy, offering greater advantages for OCI-centric users. However, organizations prioritizing a distinct, stand-alone observability platform should evaluate whether this integrated model aligns with their broader vision for observability.
ScienceLogic
ScienceLogic is a Visionary in this Magic Quadrant. The ScienceLogic AI platform comprises four products: SL1 for observability, PowerFlow for agentic automation, RestorePoint for security and network compliance, and Skylar AI for unsupervised reasoning. SL1 may be deployed as a self-hosted solution or in an AWS/Azure environment. ScienceLogic’s clients are largely in North America and EMEA. The company’s roadmap includes enhancements to its generative AI (GenAI)-powered analytics and intelligent automation capabilities.
- AI innovation: ScienceLogic’s strategic emphasis and key market differentiators are its event intelligence and AI capabilities, particularly the Skylar AI suite, with the goal of enabling intelligent, automated operations.
- Execution and presence in market: ScienceLogic has demonstrated effective market execution, achieving a solid footprint in an increasingly competitive IT operations management landscape through direct sales, a network of global system integrators (GSIs) and a broad ecosystem of channel partners.
- Customer experience: The company provides strong support and customer success channels, including community discussions, learning services and dedicated customer success managers (CSMs). It also has a “SL360 program” aimed at helping customers realize long-term business value from their investment in the product.
- SLO management: ScienceLogic lacks native support for defining, tracking and visualizing service-level objectives (SLOs). The absence of a dedicated SLO capability makes it challenging for SRE teams to monitor reliability goals and manage error budgets.
- Application development support: With its roots in infrastructure and network monitoring, ScienceLogic lags competitors in some application-centric capabilities. Notably, it lacks code-level insights and has fewer integrations with common developer toolsets for DevOps workflows than other evaluated platforms.
- Geographic coverage limitations: With primary operations and a strong presence in North America and EMEA, ScienceLogic could face challenges when approaching markets in other regions. The company has less of a direct LATAM focus, which represents a potential missed opportunity.
SolarWinds
SolarWinds is a Niche Player in this Magic Quadrant. SolarWinds Observability is its SaaS observability platform. The company’s operations are geographically diverse, and its clients include organizations of all sizes. Recent product updates have included Kubernetes autoinstrumentation and improved support for AWS. SolarWinds’ roadmap currently includes an integrated incident response system, alert suppression engine, automated team workflows and run book triggering, as well as agentic-AI-based workflows.
- Product: SolarWinds Observability enhances its capabilities by leveraging eBPF technology, specifically providing performance insights into Kubernetes environments. This, in turn, provides detailed visibility into Kubernetes clusters, enabling faster identification of issues than traditional monitoring methods.
- Pricing model and transparency: To support customers transitioning between the cloud and data centers, SolarWinds offers deployment flexibility, which allows customers to mix deployment options while maintaining a competitive price point.
- Squadcast acquisition: SolarWinds’ recent acquisition of Squadcast offers several benefits, such as incident response and the ability to reduce MTTR, third-party tool integration support and CI/CD pipelines integration support. It also brings AI-driven incident response capabilities such as deduplication to reduce alert fatigue and improve operational efficiency and resilience.
- Limited AI/ML capabilities: While additional investment in AI/ML is on its roadmap, the SolarWinds platform today includes foundational capabilities such as dynamic thresholds, anomaly detection and GenAI-powered query assist. However, relative to its competitors, the SolarWinds AI/ML capabilities are limited.
- Agent management: The SolarWinds “unified agent” combines its infrastructure, network and database monitoring agents using a plugin-based framework. However, APM and DEM both require additional agents to be managed.
- DevOps toolchain integration: SolarWinds’ support for DevOps toolchains is rudimentary. It mainly supports agent packages implemented into CI/CD workflows used to build host images and Kubernetes collectors added using infrastructure as code (IaC), but it lacks support for bidirectional communication and complexity in configuration with DevOps toolchains.
Splunk
Splunk, a Cisco company, is a Leader in this Magic Quadrant. Cisco completed the acquisition of Splunk in March 2024, and Splunk is now the business unit responsible for observability. Its observability solution is centered on Splunk Observability Cloud, which provides infrastructure monitoring, APM, on-call, synthetics, log analytics and RUM. The solution includes Splunk Platform, Splunk IT Service Intelligence (ITSI), and AppDynamics. Its operations are geographically diversified, and its customers tend to be large enterprises.
- Operations: Cisco and Splunk have a broad global presence, with sales and support in all major geographic areas. Additionally, both companies bring a much wider ecosystem of sales and implementation partners, meaning that customers are well-served at all stages of deployment.
- Overall viability: Cisco has a strong track record in observability and monitoring, mainly through its acquisitions such as AppDynamics and ThousandEyes. The combination of Splunk and Cisco has deep expertise and a strong client base in many industry verticals, including banking, tech and public sector.
- Innovation: Cisco and Splunk have made extensive investments in AI across its entire portfolio, and is rolling out its Cisco AI Assistant to operate with its observability solutions. This generative AI tool will help users quickly build queries, interpret results and gain insights more quickly from observability telemetry.
- Portfolio complexity: Splunk’s observability portfolio, grown through multiple acquisitions, currently exhibits limited integration across its diverse products. This creates complexity, hindering workflows and decreasing operator efficiency.
- Market execution: Many Splunk clients are unaware of the existence of Splunk Observability Cloud, despite the prevalence of Splunk’s logging and security solutions in the market.
- Cost controls: While Splunk’s metrics pipeline management offers new capabilities for managing large metric volumes, clients consistently face difficulties in controlling overall Splunk environment costs, especially when utilizing Splunk Cloud and ITSI.
Sumo Logic
Sumo Logic is a Niche Player in this Magic Quadrant. Its approach is centered around large-scale log analytics and includes security use cases such as threat detection and response, in addition to observability. While North America remains its primary target market, Sumo Logic also serves a global client base, with instances hosted across multiple AWS regions and notable recent investments in APAC. In 2024, it announced Mo Copilot, an AI-based assistant, an area it expects to build on. Sumo Logic plans to release AI-driven investigation capabilities in late 2025.
- Log analytics at scale: For clients whose primary focus is log analytics, Sumo Logic’s platform offers targeted capabilities such as Log Search, LogReduce and LogCompare, which facilitate log ingestion and analytics at scale. This is complemented by its Flex Licensing, which is based on analytics rather than the volume of data ingested.
- Open-source adoption: Sumo Logic makes extensive use of open-source technologies. This includes telemetry ingestion via OpenTelemetry collectors and OTLP, along with support for Telegraf and Fluent Bit. Additionally, it leverages OpenSLO for SLO management and OpAMP for OpenTelemetry collector fleet management.
- Observability and security: For teams embracing DevSecOps or organizations aiming to unify security and IT operations, Sumo Logic offers a platform that delivers both use cases within a single solution.
- LLM observability: A notable gap in Sumo Logic’s current offering is dedicated support for observing large language models (LLMs). While it offers integration to AWS Bedrock and Google Cloud Vertex AI for metric and log collection, it does not provide LLM-specific observability. Capabilities for analyzing token usage, cost, bias, drift and hallucination are becoming increasingly common across observability platforms.
- GenAI-based capabilities: Compared to other observability platforms, Sumo Logic was relatively late in introducing an LLM-powered assistant. Its Mo Copilot, launched in December 2024, remains in the early stages of maturity and currently functions primarily as a query assistant for data exploration and visualization.
- Code debugging: For DevOps teams that need to trace application errors back to their source, the lack of integration between Sumo Logic and integrated development environments (IDEs) can be a limitation.
Vendors Added and Dropped
We review and adjust our inclusion criteria for Magic Quadrants as markets change. As a result of these adjustments, the mix of vendors in any Magic Quadrant may change over time. A vendor's appearance in a Magic Quadrant one year and not the next does not necessarily indicate that we have changed our opinion of that vendor. It may be a reflection of a change in the market and, therefore, changed evaluation criteria, or of a change of focus by that vendor.
Added
The following vendors met the inclusion criteria and have been added to the Magic Quadrant:
- Apica
- Coralogix
- ITRS
- Sciencelogic
- SolarWinds
Dropped
- Logz.io was dropped because it did not meet the CII threshold for this market.
- ServiceNow was dropped because it did not meet the inclusion criteria for this research.
Inclusion and Exclusion Criteria
- Provide generally available capabilities as of 27 March 2025. General availability means the product or service is widely available to all customers for purchase through normal sales channels.
- Sell the observability platform solution directly to paying customers without requiring them to engage professional services help. The vendor must provide at least first-line support for these capabilities, including any bundled open-source software. This includes, but is not limited to, comprehensive product documentation, installation guidance and reference examples.
- Demonstrate an active product roadmap and go-to-market and selling strategies for their solutions.
- Have phone, email and/or web customer support. They must offer contract, console/portal, technical documentation and customer support in English (either as the product’s default language or as an optional localization).
Capabilities Inclusion Criteria
- Observability platform offerings must offer native support for all mandatory features and four of the nine common features described in Gartner’s market definition.
- Observability platform offerings must be delivered via software-as-a-service (SaaS). Vendors may also provide self-hosted alternatives for clients that require them, but the self-hosted options are outside the scope of this research.
Performance Threshold Achievement
- The observability platform offering must have at least 50 paying, production (non-beta-test) customers in at least two or more geographic regions (APAC, EMEA, LATAM or North America), excluding sales to managed service providers (MSPs).
- The observability platform offering must have generated at least $75 million in annual generally accepted accounting principles (GAAP) revenue during the 12 calendar months prior to March 2025.
Or
- The observability platform offering must have generated a minimum of $10 million in annual revenue, combined with a growth rate of at least 25% in the previous 12 calendar months, compared to its previously completed 12-month period.
In addition, the vendor must rank among the top organizations using the customer interest indicator (CII) defined by Gartner for this Magic Quadrant. CII was calculated using a weighted mix of internal and external inputs that reflect Gartner client interest, vendor customer engagement and vendor customer sentiment.
Honorable Mentions
Gartner is tracking more than 40 vendors in the observability platforms market. This research focuses on 20 vendors that met our inclusion criteria. However, the exclusion of a particular vendor does not necessarily mean that it should not be considered or that it does not have viability and capabilities that may be a fit for a customer’s unique requirements.
Dash0: Founded by one of the founders of Instana (acquired by IBM), Dash0 is a modern, OpenTelemetry-native observability platform based on ClickHouse. Although this is not a unique foundation from which to create an observability platform, Dash0 has carried it off particularly well, with a simple pricing plan and developer-centric user interface. Dash0 embraces open standards; in addition to OTel, Prometheus Query Language (PromQL) is used for queries and Perses for dashboards. Dash0 did not meet the performance threshold inclusion criteria this year.
groundcover: Organizations that want to maintain control of the locations of their telemetry might wish to examine groundcover. Although a SaaS management plane is available, with groundcover, all of your telemetry is collected and stored in private instances of ClickHouse and VictoriaMetrics that remain inside your perimeter at all times. This in-cloud or bring-your-own-cloud (BYOC) architecture is one that we may begin to see more often. groundcover did not meet the performance threshold inclusion criteria this year.
HPE OpsRamp: Historically a SaaS-based IT operations management (ITOM) product, OpsRamp has regularly included unique capabilities that distinguish it from its competitors. Initially this was the event intelligence solution capability, and as OpsRamp has continued to evolve, its bets have continued to anticipate the market. HPE acquired the company in 2023 and it (along with another acquisition, Morpheus Data) has become the focal point for operations capabilities in HPE Greenlake. HPE OpsRamp did not meet the awareness inclusion threshold this year.
Kloudfuse: Kloudfuse is a self-managed observability platform that customers can deploy in their own public or private cloud resources. SaaS is not available, although the company offers managed-control plane services that can reduce operational requirements while data remains in the customer’s environment. Kloudfuse is organized around a data lake architecture and is intended to be an organization’s unified observability platform. We like what we’re seeing from Kloudfuse, but it did not meet the capabilities inclusion criteria because SaaS product delivery is not currently available.
Observe: One of the first observability vendors to base its platform on a data lake built on the Snowflake cloud data platform, Observe was identified as a Gartner Cool Vendor in 2021. Since then, Observe’s analytics-based observability platform has demonstrated strong growth and enterprise adoption and clearly merits inclusion here. Unfortunately, Magic Quadrant research is limited to a maximum of 20, and due to the increasingly competitive landscape, Observe did not meet the awareness inclusion threshold this year.
Evaluation Criteria
Ability to Execute
Gartner analysts evaluate vendors on the quality and efficacy of the processes, systems, methods or procedures that enable provider performance to be competitive, efficient and effective, and to positively impact revenue, retention and reputation. Ultimately, vendors are judged on their ability and success in capitalizing on their vision.
Product: This looks at the core observability technologies that compete in the observability platform market, including current product capabilities, quality and feature sets. Additional consideration is given to the vendor’s scalability, availability and integration, as well as its security features.
Overall viability: This criterion includes an assessment of the organization’s overall financial health, as well as the financial and practical success of the business unit. Considerations include profitability, geographic distribution of revenue and R&D spending.
Sales execution/pricing: This covers the assessment of a vendor’s success in the market. Vendors’ pricing models and proposals are compared for value and complexity, as well as pricing transparency. Considerations include pricing and discounting, new versus repeat business, and competitive dynamics, including awareness of competitors.
Market responsiveness: This criterion looks at a vendor’s ability to respond and change direction, based on the evolution of customer needs and changes in market dynamics. Considerations include response to competitors and ability to listen and respond to customer feedback.
Marketing execution: This looks at the clarity, quality, creativity and efficacy of programs designed to deliver the vendor’s message in order to influence the market, promote the brand, increase awareness of products and establish a positive identification in the minds of customers.
Customer experience: This covers the products and services and/or programs that enable customers to achieve anticipated results with the products evaluated. This may also include ancillary services, customer support programs and availability of user groups. Considerations include postsales support, programs for high-touch or VIP customers, and specific delivery partners in a given region.
Operations: This criterion looks at the ability of the vendor to meet goals and commitments. Factors include quality of the organizational structure, skills and relationships, and their ability to meet service-level agreements. Considerations include partnerships with cloud providers, outages that affect customers and SLA-adherence.
Ability to Execute Evaluation Criteria
| Evaluation Criteria | Weighting |
|---|---|
Product or Service | High |
Overall Viability | Medium |
Sales Execution/Pricing | Medium |
Market Responsiveness/Record | High |
Marketing Execution | Medium |
Customer Experience | High |
Operations | Low |
Source: Gartner (July 2025)
Completeness of Vision
Gartner analysts evaluate vendors on their ability to understand current market opportunities and create and articulate their vision for future market direction, innovation, customer requirements and competitive forces. Ultimately, vendors are rated on their vision for the future, and how well that maps to Gartner’s position.
Market understanding: This criterion considers a vendor’s ability to understand customer needs and translate them into products. Vendors that show a clear vision of their market listen, understand customer demands, and can shape or enhance market changes with their added vision. Consideration is given to understanding the rapidly evolving observability landscape and how it is distinguished from APM.
Marketing strategy: This criterion looks for clear, differentiated messaging consistently communicated internally and externalized through social media, advertising, customer programs and positioning statements. Consideration is given to new market outreach, innovative marketing initiatives and true differentiation.
Sales strategy: This criterion considers whether the vendor has a sound strategy for selling that uses the appropriate networks, including direct and indirect sales, marketing, service, communication and partners that extend the scope and depth of market reach, expertise, technologies and the vendor’s customer base. Consideration is given to channel strategy and understanding the buyers and influencers involved in selection of observability platform products.
Offering (product) strategy: This criterion evaluates whether a vendor’s approach to product development and delivery emphasizes market differentiation, functionality, methodology and features that cover current and future requirements. Consideration is given to quality and cadence of vendors’ product roadmap and investment priorities into adjacent market segments within the ITOM landscape.
Business model: This criterion looks at the design, logic and execution of the vendor’s business proposition to achieve continued success. Consideration is given to vendors’ business, value proposition, ability to anticipate shifts in licensing/pricing models and relationship with open-source communities.
Vertical/industry strategy: As observability platforms tend not to be industry-specific, evaluating these in detail is not a key element of this research. Where vertical or industry differentiation is relevant, questions are included in other criteria categories.
Innovation: This criterion looks at direct, related, complementary and synergistic layouts of resources, and expertise or capital for investment, consolidation, defensive or preemptive purposes. Consideration is given to the level of investment in product development in new areas related or adjacent to observability, third-party and partner relationships and integrations, and use of AI/ML and other novel capabilities.
Geographic strategy: This criterion looks at the provider’s strategy to direct resources, skills and offerings to meet the specific needs of geographies outside its “home” or native geography, either directly or through partners, channels and subsidiaries, as appropriate for that geography and market. Additional consideration is given to the number of employees allocated to different regions, locations of SaaS delivery platforms, tailoring of go-to-market or product strategy to address regional differences, and the depth and scope of partners available in countries with existing and new customers.
Completeness of Vision Evaluation Criteria
| Evaluation Criteria | Weighting |
|---|---|
Market Understanding | High |
Marketing Strategy | Medium |
Sales Strategy | Medium |
Offering (Product) Strategy | High |
Business Model | High |
Vertical/Industry Strategy | NotRated |
Innovation | High |
Geographic Strategy | Medium |
Source: Gartner (July 2025)
Quadrant Descriptions
Leaders
Leaders provide observability platform products that are a strong functional match to general market requirements, and they are among the most successful in building and expanding their customer base. They have comprehensive portfolios that offer superior analytics and visibility, and have broad integration with other ITOM technologies. Leaders demonstrate evidence of superior vision and execution for emerging and anticipated market requirements, as well as a consistent track record of innovation and customer experience.
Challengers
Challengers demonstrate broad market reach and large observability platform deployments. Vendors in this quadrant typically have strong execution capabilities and a significant sales and brand presence garnered from the company as a whole, if not directly from its observability-related activities. Some vendors previously may have been among the top performers in the market and, thus, offer broad product portfolios. Challengers may be transforming their product offerings and market focus. In some cases, their offerings are positioned as elements of a larger solution that may even extend beyond the boundaries of ITOM.
Visionaries
Visionaries provide observability platform products and have built a compelling plan to competitively address observability platform market requirements, but with a product portfolio that may still be a work in progress. They have a lower ability to execute than the Leaders. This is typically due to a lower ability to respond to market conditions, bring together the necessary product and platform requirements, and effectively gain and expand market share.
Niche Players
Niche Players comprise primarily, but not exclusively, vendors with observability platform solutions that cater to specific audiences or offer limited use-case support. Because they do not demonstrate equal depth across all core capabilities, they typically do not meet the observability needs of the broader market, or they may do so within specific verticals or market segments or geographic regions only. In addition, Niche Players may have a more limited ability to invest in the necessary functional and sales and marketing capabilities to expand beyond their current focus. Inclusion in this quadrant does not reflect negatively on Niche Players’ value in the markets in which they choose to compete.
Context
Observability Platforms: VUCA Microcosm vs. Oasis
As we called out in the conference presentation that summarized last year’s edition, the roots of this research go back to 1997. As I enter my fourth year leading this Magic Quadrant in 2025, I reflect on the significant changes since 2022, especially given the volatile, uncertain, complex and ambiguous (VUCA) global environment Gartner and others describe.
The observability platforms market (and the application performance monitoring market that preceded it) has been in near-constant upheaval over the last few years. This may amplify the value of this Magic Quadrant to Gartner clients, but let’s not fool ourselves. This is just one of many fiercely competitive software tools markets, right? If not, what is different about observability platforms? One theory is that the complexity and scale of cloud-native workloads has shattered longstanding, underlying assumptions of visibility and resource management. Let’s examine this in more detail, in the context of our current conditions.
Volatility: Acquisitions represent the most visible aspects of volatility. The effects of Cisco’s acquisition of Splunk are just now becoming evident, but there are others — Broadcom acquiring VMware and New Relic, Riverbed, Sumo Logic and SolarWinds going private or changing hands. Startups have been acquired (or acquihired) by the likes of Datadog, Dynatrace and Grafana Labs. Examples of these are Quickwit, Metaplane, Rookout, Runecast, Asserts, and Pyroscope. The baseline capabilities required to compete in this market are advancing rapidly, along with new startups serving as innovation triggers as well as acquisition fodder. AI observability startups are the most likely next such candidates.
Uncertainty: For customers of observability platform products, ongoing cost of ownership represents the principal area of uncertainty. For buyers or prospects, it is that, plus time to value and implementation effort. As vendors transition from a host-based to a consumption-based commercial model (or increasingly, keep elements of both), buyers express concern about the ever-rising costs. Questions to Gartner about optimizing spend in this area are increasing, as is a willingness to compromise on capabilities or adopt open-source solutions to mitigate budgetary concerns.
Complexity: The applications and workloads that organizations are creating and operating today have risen substantially in complexity. Use of Kubernetes, multiple cloud providers and services, API-enabled third-party service dependencies — all add up quickly. Complex requirements can translate into complex products. AI, as well, represents a brave new world for most heads of I&O. Although widespread deployment of client-operated AI workloads is not yet upon us, it will be soon. The AI-enabled capabilities of observability platforms are now commonplace, and many vendors are well on their way to agentic AI. However, the rationale that “you need AI-based tools to operate AI workloads” is not yet a selling point.
Ambiguity: We have seen a bevy of new roles and teams in the product development and operations cadre: SRE, platform engineering, AI engineers, data scientists, cloud centers of excellence, central observability teams and so on. Factoring these into shops that are traditionally organized by technology silo is confusing. The expectations of I&O are also ambiguous: What does great availability and performance look like? How is more telemetry going to help when I’m already drowning in data and alerts?
Indeed, heads of I&O and their teams are seemingly not immune from VUCA; the operations management community may, in fact, have its own VUCA universe (VUCAverse?) that is distinct from that of the broader world.
However, after evaluating the vendors and products included in this Magic Quadrant research, readers should be encouraged. Some truly amazing observability platform products are available today, and many are included here. VUCA aside, it is an excellent time to be involved in digital operations.
If an antidote to VUCA is mastering the ability to turn telemetry data into the insights and actions that allow I&O teams to prime the flywheels of business, we’re willing to acknowledge the oasis — for now.
Market Overview
The observability platforms market of mid-2025 is continuing the lively evolution that began during the worldwide pandemic. This year, complying with the Magic Quadrant ceiling of 20 vendors required difficult inclusion decisions, as there was no choice but to leave viable participants out. Some of these are included in the Honorable Mention section, others may appear in subsequent research. We expect to see this level of energy continue, at least in the short term. To date, this has resulted in an ever-improving depth of capabilities and increased options for buyers. There may be a point at which the struggle for differentiation moves beyond more advanced use cases into a fashion show.
Some of the factors affecting ongoing growth and adoption include:
- Reaction to VUCA conditions — Organizations are required to make difficult choices regarding technology platforms, workload placement, geographic location and cost of ownership. Rarely do challenging conditions result in the loosening of availability and performance requirements, so organizations must frequently do more with less, or do more with something different. For example, the future of self-managed, private cloud infrastructure in light of VMware products becoming unattainable to many organizations is unclear and has huge implications for how the health and performance of these workloads are managed.
- AI exuberance — Every time we believe we’ve seen peak hype regarding AI, something happens that amplifies it again. Recently, this “something” has been agentic AI, which clearly holds tremendous promise for IT operations use cases in particular, and the hype may not be entirely misplaced. However, numerous supposedly agentic capabilities are being advertised today that are not, and the realizable, consistent economic return of these investments have similarly yet to achieve a level of sustainable credibility. The need to operate AI workloads on behalf of the business is increasing. Observability platform vendors as well as special-purpose vendors offer products to assist, as these workloads have characteristics that are orthogonal enough to traditional workloads to require additional knowledge and tooling.
- Business value of telemetry — Many observability platform products today claim to have capabilities intended to facilitate monitoring of business activities rather than simply technology. This functionality goes by names like “business observability,” and it can be extremely valuable but is rarely something that can be provided by a tool without the workloads undergoing some adaptation. Even beyond business observability, many organizations are recognizing the business insight that is hidden in the streams of telemetry being collected, analyzed and stored by I&O, and are looking for ways to democratize it. For those exploring this opportunity, the technical and nontechnical ongoing effort and ownership of creating and delivering insights in the most effective ways to additional audiences should be given equal weight, yet this rarely happens.
The consolidation of monitoring domains and practices in multiple patterns continues. This is a natural response to the ongoing shift of operational responsibility toward focusing on an application’s ability to deliver its intended outcomes and away from optimizing specific supporting technology. This shift is further reflected in the continued concentration of monitoring tooling spend growth in those market segments most aligned with this shift, and with growing market demand for observability products and adjacent segments, namely DEM and infrastructure monitoring. Given these trends, Gartner expects the market for observability products to reach an estimated $14.2 billion by 2028, with an 11.1% compound annual growth rate (CAGR) between 2021 and 2028 in constant currency (see Forecast: IT Operations Management Software, Worldwide, 2022-2028, 2Q24 Update).
The observability platforms market will continue to evolve during the next several years, driven by the following key trends:
- Cost-fatigue and the widespread demand for an understandable cost/value equation for observability platform products is reaching a fever pitch. While it has not been uncommon for inquiries about observability platforms to include discussion of cost and value, recently, it has been more unusual for them not to.
- The increase in the amount and types of data continues to drive health and performance monitoring tools to make them more closely resemble general-purpose analytics tools. Advances in AI are unlocking tremendous potential to develop more autonomous or “self-driving” features, including those that are focused on optimization rather than just problem resolution.
- AI observability is being actively sought by organizations that deploy their own AI- and LLM-based workloads. Collecting and analyzing the telemetry is an important first step, but a basic, iterative one that enables solving for the next hurdle (e.g.,determining who the responders are when AI workloads misbehave).
- Demand for greater support of use cases beyond the typical IT operations context, inclusive of external (market-facing) and internal application product owners, product teams, platform engineering/SRE/cloud operations teams, and developers is increasing. One example is the fresh take on products for developers that enable a debugger-like experience for production workloads. Another example is increased interest in business observability.
- Progressive delivery (feature flags), release management and observability are becoming more interdependent. Most recently, Datadog’s acquisition of Eppo and LaunchDarkly’s acquisition of Highlight suggest vendors recognize this as well.
- Adoption of central observability teams, an organizational device that centralizes SLO creation and reporting; telemetry life cycle management; tool selection and incident response management, is also increasing.
- OpenTelemetry adoption, eBPF instrumentation and the growing availability of telemetry pipeline solutions offer the opportunity to establish some industry blueprints and best practices for managing the flow of telemetry across the enterprise.
Acronym Key and Glossary Terms
| APAC | Asia/Pacific |
| APM | Application performance monitoring |
| CI/CD | Continuous integration/continuous delivery |
| CMDB | Configuration management database |
| DEM | Digital experience monitoring |
| eBPF | Extended Berkeley packet filter |
| EMEA | Europe, Middle East and Africa |
| ITSM | IT Service Management |
| LATAM | Latin America |
| MFA | Multifactor authentication |
| MSP | Managed service provider |
| OpAMP | Open agent management protocol |
| RBAC | Role-based access control |
| SI | System integrator |
| SLI | Service level indicator |
| SLO | Service level objective |
| SRE | Site reliability engineer |
| VAR | Value-added reseller |
This research is based on more than 1,000 customer interactions over the past 12 months. In addition, as part of our analysis, we have collected information from Gartner Peer Insights, client inquiries and publicly available sources to supplement the information provided by participating vendors.
Ability to Execute
Product/Service: Core goods and services offered by the vendor for the defined market. This includes current product/service capabilities, quality, feature sets, skills and so on, whether offered natively or through OEM agreements/partnerships as defined in the market definition and detailed in the subcriteria.
Overall Viability: Viability includes an assessment of the overall organization's financial health, the financial and practical success of the business unit, and the likelihood that the individual business unit will continue investing in the product, will continue offering the product and will advance the state of the art within the organization's portfolio of products.
Sales Execution/Pricing: The vendor's capabilities in all presales activities and the structure that supports them. This includes deal management, pricing and negotiation, presales support, and the overall effectiveness of the sales channel.
Market Responsiveness/Record: Ability to respond, change direction, be flexible and achieve competitive success as opportunities develop, competitors act, customer needs evolve and market dynamics change. This criterion also considers the vendor's history of responsiveness.
Marketing Execution: The clarity, quality, creativity and efficacy of programs designed to deliver the organization's message to influence the market, promote the brand and business, increase awareness of the products, and establish a positive identification with the product/brand and organization in the minds of buyers. This "mind share" can be driven by a combination of publicity, promotional initiatives, thought leadership, word of mouth and sales activities.
Customer Experience: Relationships, products and services/programs that enable clients to be successful with the products evaluated. Specifically, this includes the ways customers receive technical support or account support. This can also include ancillary tools, customer support programs (and the quality thereof), availability of user groups, service-level agreements and so on.
Operations: The ability of the organization to meet its goals and commitments. Factors include the quality of the organizational structure, including skills, experiences, programs, systems and other vehicles that enable the organization to operate effectively and efficiently on an ongoing basis.
Completeness of Vision
Market Understanding: Ability of the vendor to understand buyers' wants and needs and to translate those into products and services. Vendors that show the highest degree of vision listen to and understand buyers' wants and needs, and can shape or enhance those with their added vision.
Marketing Strategy: A clear, differentiated set of messages consistently communicated throughout the organization and externalized through the website, advertising, customer programs and positioning statements.
Sales Strategy: The strategy for selling products that uses the appropriate network of direct and indirect sales, marketing, service, and communication affiliates that extend the scope and depth of market reach, skills, expertise, technologies, services and the customer base.
Offering (Product) Strategy: The vendor's approach to product development and delivery that emphasizes differentiation, functionality, methodology and feature sets as they map to current and future requirements.
Business Model: The soundness and logic of the vendor's underlying business proposition.
Vertical/Industry Strategy: The vendor's strategy to direct resources, skills and offerings to meet the specific needs of individual market segments, including vertical markets.
Innovation: Direct, related, complementary and synergistic layouts of resources, expertise or capital for investment, consolidation, defensive or pre-emptive purposes.
Geographic Strategy: The vendor's strategy to direct resources, skills and offerings to meet the specific needs of geographies outside the "home" or native geography, either directly or through partners, channels and subsidiaries as appropriate for that geography and market.