Global AI Platforms and Models Spending to Surge 63.4% to $64 Billion in 2026, Gartner Reports

Brickinfo News Agency – Worldwide end-user spending on artificial intelligence (AI) platforms and models is projected to reach $64 billion in 2026, representing a 63.4% increase from $39 billion in 2025, according to research published by Gartner, Inc. Spending on generative AI (GenAI) models is expected to expand by 117%, while investment in AI platforms will rise by 36.9% during the same year.

Enterprise AI budgets are undergoing increased scrutiny as organizations shift their focus toward usage efficiency, cost control, and measurable business outcomes. Spending is increasingly directed toward AI solution providers that can demonstrate explicit value across critical operational metrics, including cost, latency, performance, and reliability.

“Enterprise AI budgets are coming under greater scrutiny, with increased focus on usage efficiency, cost control and measurable outcomes,” said Arunasree Cheparthi, Senior Principal Research Analyst at Gartner. She highlighted that vendors embedding evaluation, cost transparency, and usage tracking directly into enterprise workflows hold a distinct advantage. However, as the market transitions toward usage-driven pricing, technology providers face growing pressure to demonstrate sustained adoption and durable profit margins.

This market shift is rapidly accelerating demand for domain-specific language models (DSLMs) and specialized AI models, which are forecast to achieve the fastest growth segment at 210% in 2026. Meanwhile, spending on foundation generative AI models is projected to increase by 104.2%, rising from $11.4 billion in 2025 to $23.4 billion in 2026.

In the platform sector, spending on AI platforms for data science and machine learning is forecast to reach $26.4 billion, up from $19.4 billion in 2025, while AI application development platforms will grow from $6.9 billion to $9.5 billion. Gartner projects that long-term market success will belong to vendors that enable enterprises to regulate, monitor, and optimize how AI technologies are deployed across their organizations.