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Worldwide end-user spending on AI models and platforms is projected to reach $64.3 billion in 2026, up 63.4% from $39.3 billion this year, as enterprises accelerate AI adoption while demanding greater cost control and measurable business outcomes, according to Gartner.
The analyst firm said spending on generative AI models will more than double in 2026, rising 117%, while investment in AI platforms is expected to grow 36.9% as organizations expand AI deployments beyond pilot projects.
"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.
"Spending is shifting toward providers who can demonstrate clear value across cost, latency, performance and reliability," she said.
According to Gartner, enterprises are increasingly favoring AI vendors that provide built-in capabilities for evaluating model performance, tracking usage and improving cost transparency, enabling organizations to optimize AI spending as deployments scale.
The report also forecasts rapid growth in domain-specific language models (DSLMs) and specialized AI models, with spending expected to surge 210% in 2026, making them the fastest-growing segment of the AI software market.
Among individual segments, spending on foundation generative AI models is projected to rise from $11.4 billion in 2025 to $23.4 billion in 2026, while AI application development platforms are expected to grow from $6.9 billion to $9.5 billion. Spending on AI platforms for data science and machine learning is forecast to increase from $19.4 billion to $26.4 billion during the same period.
Gartner said the shift toward usage-based pricing is also reshaping enterprise buying decisions, placing greater pressure on AI providers to demonstrate sustained adoption and long-term value rather than simply offering access to large language models.
"Over the long term, the biggest winners will be vendors that help enterprises manage where and how AI is used across the business," Cheparthi said.
"As more models enter the market and usage-based pricing becomes harder to predict, buyers will turn to platforms that help them choose the right tools, monitor performance, enforce policy and keep costs under control."
The findings suggest that enterprise AI investments are entering a new phase, where organizations are focusing less on experimentation and more on governance, optimization and return on investment as AI becomes embedded across business operations.
The analyst firm said spending on generative AI models will more than double in 2026, rising 117%, while investment in AI platforms is expected to grow 36.9% as organizations expand AI deployments beyond pilot projects.
"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.
"Spending is shifting toward providers who can demonstrate clear value across cost, latency, performance and reliability," she said.
According to Gartner, enterprises are increasingly favoring AI vendors that provide built-in capabilities for evaluating model performance, tracking usage and improving cost transparency, enabling organizations to optimize AI spending as deployments scale.
The report also forecasts rapid growth in domain-specific language models (DSLMs) and specialized AI models, with spending expected to surge 210% in 2026, making them the fastest-growing segment of the AI software market.
Among individual segments, spending on foundation generative AI models is projected to rise from $11.4 billion in 2025 to $23.4 billion in 2026, while AI application development platforms are expected to grow from $6.9 billion to $9.5 billion. Spending on AI platforms for data science and machine learning is forecast to increase from $19.4 billion to $26.4 billion during the same period.
Gartner said the shift toward usage-based pricing is also reshaping enterprise buying decisions, placing greater pressure on AI providers to demonstrate sustained adoption and long-term value rather than simply offering access to large language models.
"Over the long term, the biggest winners will be vendors that help enterprises manage where and how AI is used across the business," Cheparthi said.
"As more models enter the market and usage-based pricing becomes harder to predict, buyers will turn to platforms that help them choose the right tools, monitor performance, enforce policy and keep costs under control."
The findings suggest that enterprise AI investments are entering a new phase, where organizations are focusing less on experimentation and more on governance, optimization and return on investment as AI becomes embedded across business operations.
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