Has anyone explored private instance of LLMs for their own organisation and what are the pros and cons for it?
Sort by:
On the pros side, private deployments offer stronger data privacy and security, better regulatory compliance, full control over data residency, deeper customization for domain-specific use cases, and predictable behavior aligned to internal policies. On the cons side, they come with higher infrastructure and operational costs, significant complexity in model hosting and scaling, ongoing maintenance and fine-tuning effort, dependency on scarce Artificial Intelligence and Machine Learning talent, slower access to rapid model improvements, and challenges in achieving the same performance and cost efficiency as managed hyperscaler offerings.
Great question this is something a lot of teams struggle with. One approach I’ve seen work well is moving beyond static FAQs and creating a short, role-based “attendee playbook” that combines clear messaging pillars, common talking points, and real scenarios they’re likely to encounter at the event. Pairing that with a quick live or recorded briefing where attendees can ask questions, plus a simple feedback loop during the conference (Slack/WhatsApp check-ins or daily huddles), helps keep everyone aligned and confident while still sounding human and authentic on the floor.
We have one, however we still do not allow company proprietary information to be used for queries
We have one. Works well but costly.
A viable alternative to a costly private LLM is to employ a SLM (like personal.ai) that has been trained on your domain expertise because you own the memory stack.
We have one for our team and we've deployed for 3 customers our "Copilot-like" AI-on prem solution. Deploying one LLM on prem is straight forward, but in order to transform its power into productivity gain the organisation needs several new people/roles or a full-stack AI solution partner. You need to choose the GPU server configuration, AI inference software, AI engine(out of 1M+), AI load balancing, prompt manager, access control and deep integration into existing apps. Just chat is not enough, from our experience.
Pros: you control the AI engine version (same accuracy over time), predictible and guarantied capacity, predictible price (high in the begining, but no suprises after), faster deployments (shorter and easier discution with Legal and Privacy teams), you can save money by buying more efficient hw, like Nvidia GB10 or RTX Pro 6000.
Cons: the initial investment is bigger (but the TCO on 2 years, for 200 users is cheaper vs Copilot), you need to reskill some hw engineers and create new roles like AI engine engineer, Prompt engineer (or find a partner that offer the AI as a full stack).
For the companies in Europe I think on-prem will be the best choice since cloud providers are processing the AI inference all-over the globe. Also, since the AI will be part of many critical business procces, is it safe to rely on a best-effort cloud LLM?