Google adds pay-as-you-go Gemini pricing as enterprises seek control over AI spending

AI agents could make software development and other enterprise tasks more productive, but they are also making technology spending harder to predict. Unlike traditional software licenses, the cost of running an agent can vary depending on the models it uses, the number of tokens it consumes, and how long it runs.

Google on Wednesday added new pricing options, discounts and cost-management tools to Gemini Enterprise that it says are aimed at helping enterprises reduce the cost of certain AI workloads while giving enterprises better visibility into where their AI budgets are going.

As part of the new pricing options, the hyperscaler introduced a pay-as-you-go model and Flexible Savings Plans (FSPs).

While the pay-as-you-go model allows enterprises to pay for the compute and tokens they consume instead of committing to a base subscription, which in turn avoids paying for empty seats or unused capacity, the FSPs offer discounts of 10% for one-year commitments and 20% for three-year commitments on Gemini Enterprise spending.

Flexible pricing lowers barriers, but adds new trade-offs

For enterprise teams and their CIOs, the pay-as-you-go model lowers the barrier to adoption and is better suited to experimentation, temporary projects, and agent workload bursts, said Stephanie Walter, practice lead of AI stack at HyperFRAME Research.

“While Per-seat pricing forces you to buy capacity before you know if an idea is worth it, the pay-as-you-go lets you spin up an agent experiment on a Friday afternoon and only pay for what it actually burns,” echoed Manoj Chandra Jha, principal analyst at Nord-IQ Research.

That means the newer pricing model also removes procurement friction from the experimentation loop, Paul Chada, cofounder of agentic AI startup Doozer AI, pointed out.

“When a pilot requires a license commitment, every experiment needs a business case. When it’s metered, an engineer can run the pilot on Tuesday and show finance a real bill on Friday. That shortens the distance between idea and evidence, which is where most enterprise agent programs die,” Chada said.

However, these advantages come with their own set of trade-offs, especially predictability.

“One user request can trigger an opaque chain of model calls, reasoning steps, and tool invocations, so consumption can grow much faster than employee headcount with the possibility of surprise bills at the end of a billing period,” Walter said.

That unpredictability also means the new pricing model does not automatically translate into cost savings, echoed Jha.

“It’s mostly a shift, not a discount. But matched to the right workload, it can save real money: bursty, unpredictable agent usage no longer subsidizes idle seats, while steady, high-volume usage may still be better suited to a committed plan. The savings come from matching each workload to the right pricing model, not from pay-as-you-go being cheaper by default,” Jha added.

However, the FSPs have their own caveats, especially the three-year plan.

While the FSPs can offer meaningful savings for enterprises with steady or growing AI usage, the three-year commitment is harder to justify in the wake of models, prices, and application architectures changing so quickly, Walter said, adding that the commitment is not just financial but also about choosing a platform as well.

Further, the analyst cautioned that the FSPs are less suitable for enterprises that have yet to establish a reliable baseline for consumption, as committing too early could turn an unpredictable operating expense into a predictable overcommitment.

Currently, FSPs are available for self-serve customers and customers already on enterprise agreements.

The pay-as-you-go model, though, remains only available to select customers with the hyperscaler planning a broader rollout “soon”.

Deferred execution trades speed for lower inference costs.

In addition, the hyperscaler is introducing a third cost-cutting option that is based less on how much an enterprise consumes than on how quickly it needs the result.

The option, named deferred execution pricing, will allow enterprises to mark eligible agent workloads for execution during off-peak capacity windows, with Google offering discounts of up to 50% on inference costs in return, the hyperscaler said in a statement.

Deferred execution pricing, it added, is aimed at workloads that can tolerate delays, potentially giving enterprises a way to lower costs for background tasks and other agent workloads where an immediate response is not essential.

That up to 50% discount in inference costs, Walter pointed out, can be material for CIOs at scale.

However, they must decide which work can safely wait and whether delayed tasks still meet the business requirement, Walter cautioned, adding that Deferred execution fits tasks such as evaluations, document processing, indexing, batch summarization, code analysis, and other background tasks.

Further, the analyst warned that CIOs also need to consider a “development tax” when considering deferred execution: “If agents need to be redesigned to accommodate real-time vs. asynchronous execution, the engineering effort required to build and maintain those different workflows can offset some of the savings.”

But even for workloads that can tolerate those trade-offs, the option will not be immediately available, with Google initially limiting deferred execution pricing to select workloads only. Details of which workloads are eligible were not immediately available.

New FinOps tools target AI spending visibility

Separately, Google is also adding an AI spend anomaly detection capability with root-cause analysis and pairing centralized billing reports with a FinOps agent that can generate natural-language summaries of where AI budgets are being spent.

While the anomaly detection capability is designed to flag projects where AI spending is trending higher than normal and identify the top three SKUs driving the increase, the FinOps agent is intended to make it easier for CIOs to understand where their AI spending is going.

Anomaly detection as a feature, according to Walter, can be valuable for agentic workloads because their consumption can increase through loops, retries, or unexpectedly long execution chains that users never see.

“Identifying what is driving a spike shortens the investigation for CIOs and enterprise teams,” Walter added.

The FinOps agent, meanwhile, Jha said, could help CIOs and other business leaders ask questions about AI spending that traditional dashboards were not designed to answer. However, its usefulness will depend on the quality of the underlying cost attribution, as the agent can only explain spending it can accurately associate with particular teams, projects, or workloads, Jha added.