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Google Cloud has made token-based cost management generally available for BigQuery generative-AI functions. Administrators can now set daily project and per-user limits for input tokens and for output/thinking tokens, giving teams a way to cap large-scale AI usage before it produces an unexpected bill.
BigQuery can run Gemini-powered inference across tables, so a query that touches millions of rows may generate a very large number of billable language-model tokens. The new controls, announced October 14, 2026, add daily guardrails that are administered through Google Cloud Identity and Access Management (IAM). They are designed for cost governance, not for changing a model’s per-request context or throughput limits.
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What the four BigQuery token quotas control
Google exposes four independent daily dimensions. The limits are aggregated globally for the project rather than tracked separately by region.
| Quota | Default daily value | What it measures |
|---|---|---|
| Project input tokens | 200,000,000,000 | AI input tokens consumed by the project across users |
| Per-user input tokens | 150,000,000,000 | AI input tokens attributed to one user in the project |
| Project output and thinking tokens | 20,000,000,000 | Generated output and model reasoning (“thinking”) tokens for the project |
| Per-user output and thinking tokens | 15,000,000,000 | Generated output and thinking tokens attributed to one user |
Those figures are the defaults in Google Cloud’s 2026 cost-control documentation. Quotas reset daily, and administrators can request higher limits for approved workloads or apply stricter overrides to keep a budget ceiling.
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How to configure a stricter or higher limit
- Open the Google Cloud console and go to IAM & Admin.
- Select Quotas & System Limits.
- Filter the quota list for the BigQuery API.
- Find the input-token or output/thinking-token quota, choosing the project-wide or per-user dimension.
- Edit the quota value to create an override, or submit a request for an increase when the workload legitimately needs more capacity.
Use project limits for a hard ceiling on a shared budget and per-user limits to prevent one analyst, service account, or automated job from consuming the entire allowance. Setting both levels creates a two-layer guardrail: a user can hit their own limit even when project capacity remains, while the project limit can stop aggregate usage across many users.
What happens when a query reaches a quota
Before execution
BigQuery checks the relevant token availability before starting a query that contains a generative-AI function. If a quota is already exhausted, the query can be rejected with a QuotaExceeded error instead of beginning work.
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During execution
BigQuery also monitors consumption while a query runs. If execution exhausts a quota, subsequent calls to the large language model are rejected. Rows that depend on those calls can therefore fail even though earlier rows completed successfully. For functions that support max_error_ratio, that setting determines whether BigQuery returns partial results within the permitted error ratio or fails the entire query.
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Which BigQuery AI functions count
The quotas apply to BigQuery SQL functions that perform generative-AI inference with Gemini. Google’s announcement names AI.GENERATE, AI.IF, AI.CLASSIFY, and AI.SCORE; AI.GENERATE_TEXT is another documented generative function covered by the same cost-management approach. Exact model limits and behavior can vary by function and model, so the quota setting does not replace the applicable function documentation.
Functions outside these quotas
Embedding-generation and semantic-search functions, including AI.EMBED, are excluded. Google says they use different billing structures, so their usage must be governed with the controls and cost estimates applicable to those services rather than these four token quotas.
Important operating details
- Usage is global: regional execution does not provide a way around a project-wide quota. Tokens from different regions count toward the same aggregate view.
- Cached tokens are excluded: cached token use does not count toward these quota totals.
- Provisioned throughput is different: customers using Gemini Enterprise Agent Platform models with provisioned throughput should set BigQuery token quotas high. That billing mode is not token based, so a low token quota could become an unintended execution constraint even when the provisioned capacity is paid for.
- Quotas are not a forecast: they cap consumption; they do not calculate the final invoice. Model choice, input size, output length, retries, and other BigQuery charges still affect total cost.
A practical rollout plan
1. Establish a baseline
Inventory scheduled queries, notebooks, and service accounts that call generative functions. Estimate their daily input and output volume, including the largest table scans and any retry behavior.
2. Set per-user protection first
Apply a lower per-user limit to experimental identities and automation accounts. This isolates mistakes such as an unbounded query without immediately stopping every team using the project.
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3. Add the project ceiling
Choose a project-wide limit that fits the approved daily budget and leaves headroom for normal variation. Keep the four dimensions separate: a workload can be input-heavy or output/thinking-heavy, and one quota may be reached long before another.
4. Test failure handling
Run a controlled query near the limit and verify that monitoring catches both a pre-execution QuotaExceeded rejection and a mid-query failure. For supported functions, decide whether max_error_ratio should permit partial output or require an all-or-nothing result.
5. Review globally
Because usage is aggregated across regions, review all locations and identities together. Raise a quota only after confirming that the additional consumption is intentional, and tighten an override when a recurring job no longer needs its previous allowance.
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Generative functions make it easy to apply classification, scoring, or text generation directly to a warehouse-scale dataset. That convenience also makes accidental fan-out expensive: a single query can invoke an LLM once per row. Daily token quotas turn that risk into an explicit administrative policy, with separate controls for shared projects, individual users, prompt-side input, and model-side output or reasoning.
The limits should be treated as one layer of FinOps and reliability control. Teams still need query reviews, bounded scans, job monitoring, and validation of partial results. The new IAM-managed quotas provide the hard stop that those process controls cannot guarantee on their own.
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