Forrester is warning enterprise technology buyers that AI usage charges and software price increases will push budgets higher next year.

The Register cited a Forrester survey of more than 2,600 business and technology decision-makers, with the research firm saying vendors are passing AI costs to customers through higher prices and usage-based billing.

Forrester Cites AI Costs In Usage-Based Software Pricing

The Register cited Forrester's list of Anthropic, OpenAI and GitHub services that have moved away from flat-rate subscriptions in the last six months, creating cost concerns among users.

The research firm also listed Microsoft after a recent premium licence launch.

Bain & Company estimated last year that AI data-centre build costs would reach $2 trillion by 2030.

In Forrester's survey, 80 percent of decision-makers expected data and software budgets to increase as AI costs moved through vendor pricing.

Staffing Budgets Have Not Fallen With AI Rollouts

Forrester also found that personnel costs have not yet fallen despite layoffs in the technology sector.

The report said IT staffing spend has not declined in recent years, even as Oracle, Microsoft and Meta announced significant layoffs.

The Forrester figures cited by The Register put staffing at 35 percent of IT budgets in 2025., according to AI vendors have.

The research firm also found that 68 percent of data technology decision-makers expected data and analytics staffing budgets to rise.

FinOps Teams Face Token-Based AI Spending

FinOps practices should be adapted for unpredictable AI costs, according to Forrester.

Its report described traditional FinOps as unbuilt for token-based and usage-driven AI costs, but argued that those teams were best placed to build new controls.

The research firm named model routing, semantic caching and usage guardrails as runtime cost controls that could limit runaway spending.

KPMG research found in July that nearly a third of corporate leaders had difficulty understanding and controlling operating costs when implementing business AI at scale.

Named customer case studies, vendor-by-vendor price changes, audited AI savings, contract-level usage data and quantified budget impact by industry remain outside the public record .