Gartner Forecasts Worldwide End-User Spending on GenAI Models to Total $14.2 Billion in 2025

STAMFORD, Conn., July 10, 2025

Specialized GenAI Models End-User Spending to Reach $1.1 Billion in 2025

Worldwide end-user spending on generative AI (GenAI) models is projected to total $14.2 billion in 2025, according to Gartner, Inc. End-user spending on specialized GenAI models, which include domain-specific language models (DSLMs), is estimated to total $1.1 billion in 2025 (see Table 1).

Specialized GenAI models are trained or fine-tuned on industry or business process-specific data. The way DSLMs are developed and deployed results in an impact that exceeds the direct spending they generate. Gartner predicts that by 2027, more than half of the GenAI models used by enterprises will be domain-specific (that is, specific to an industry or business function), up from 1% in 2024.

Expert Take:

“Foundation GenAI models (including LLMs) are trained on vast amounts of data and used for many different tasks. They are the first models supporting GenAI and will continue to represent the largest area of spending by organizations in the coming years,” said Arunasree Cheparthi, Senior Principal Research Analyst at Gartner. “However, organizations are also turning to more domain-specific or vertical GenAI models because they offer improved performance, cost, reliability and relevance in targeted enterprise use cases over foundation models.”

Table 1: Gartner Data Snapshot: GenAI Models End-User Spending by Subsegment, 2024-2025, Worldwide (Millions of U.S. Dollars)

Market

2024

Spending

2024

Growth (%)

2025

Spending

2025

Growth (%)

Foundation GenAI Models

5,416

306.3

13,053

141.0

Specialized GenAI Models

302

1010.2

1,146

279.2

Total

5,719

320.4

14,200

148.3

Source: Gartner (July 2025)

Gartner clients can read more in Forecast: Generative AI Models, Worldwide, 2023-2029, 2Q25 Update. Learn how to prioritize GenAI capabilities in the complimentary Gartner ebook Top GenAI Opportunities for Tech Products.

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