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This guide explains the token types, documented defaults, exact console workflow, enforcement behavior, monitoring tools, and an operating plan for keeping BigQuery functions such as AI.GENERATE, AI.IF, and related Gemini-backed functions within a predictable usage envelope.
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What BigQuery AI token quotas control
BigQuery generative-AI functions send prompts and other request data to Gemini models through Google Cloud services. Usage is measured in tokens rather than rows or queries alone. A single query can therefore consume very different amounts depending on prompt length, source-column content, model behavior, and generated response size.
Input, output, and thought tokens
- Input tokens represent the prompt and other content supplied to the model, including text assembled from table columns.
- Output tokens represent the generated response returned by the model.
- Thought tokens, where applicable, represent model reasoning tokens counted by the service.
Daily quota controls are published for input and for the combined output-and-thought category. Cached tokens do not count toward these quotas. Google Cloud reports quota values in million-token increments, so very small limits may not appear perfectly in usage reporting because of aggregation and rounding.
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Project and per-user scopes are separate
Project quotas cap aggregate usage for the Google Cloud project running the BigQuery jobs. Per-user quotas constrain usage attributable to an individual user. A request must remain within the applicable project and user limits; raising one scope does not remove the other scope. Quota accounting is global across regions, so sending jobs in several locations does not create an independent daily allowance in each region.
Daily reset timing
BigQuery AI token quotas reset every day at midnight Pacific time. A team operating in another time zone should convert that reset to its local schedule before planning batch jobs or incident procedures. A quota exhausted near the end of one Pacific day can become available again after the reset, while a new day can begin with the full configured allowance.
Documented defaults and an important per-user discrepancy
Google Cloud documentation published in 2026 gives the following project-level defaults. These are service defaults, not a recommendation for your workload, and an administrator can request stricter overrides.
| Scope | Input tokens per day | Output and thought tokens per day | Qualification |
|---|---|---|---|
| Project | 200,000,000,000 | 20,000,000,000 | Documented by Google Cloud in 2026; aggregate across the project and all regions. |
| Per user | 150,000,000,000 | 15,000,000,000 | Values listed in Google Cloud’s 2026 control-costs guide. |
| Per user | 40,000,000,000 | 4,000,000,000 | Different values listed in Google Cloud’s 2026 quotas reference. |
The two official pages disagree about the per-user defaults. Do not assume either pair applies universally. Open the quota panel for the project that runs your jobs and use the values displayed there as the operational baseline. Record the observed values, date, project, and region policy in your runbook so that a later administrator can distinguish a documented default from an approved override.
How to set BigQuery AI token quotas in the console
You need permission to view and edit quotas for the project. Perform the change in the project that owns the BigQuery jobs, not merely in a project from which someone happens to open the console.
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- Open the Google Cloud console and select the project that executes the BigQuery queries.
- Go to IAM & Admin > Quotas & System Limits.
- In the service filter, choose BigQuery API. Quota entries for other services will not control BigQuery AI function calls.
- Search for
GenAiInputTokensPerDay. Review the project-level and per-user entries shown for input, output, and thought-token usage. - Select the relevant quota row, choose Edit, enter the daily ceiling you want, and submit the quota change or override request.
- Repeat the edit for each scope you intend to control. Project input and output limits are distinct from per-user input and output limits; changing one row does not change the others.
- Record the approved values, effective date, project, owners, and reset time in the team’s runbook.
The console may display a request or override workflow rather than applying a value immediately. Follow the status shown there and verify the resulting number in the quota list before relying on it for a production cutover.
Choose a ceiling from workload evidence
Start with observed token use, not the service default. Measure representative prompts and rows, include both successful and unusually verbose responses, and then set a daily ceiling that covers planned work without leaving an unreviewed path to the maximum default. Revisit the value when any of these change:
- the Gemini model or model configuration;
- prompt templates, system instructions, or the number of source columns concatenated into a prompt;
- daily row counts, backfills, or retry policies;
- the project or regional deployment used by scheduled jobs;
- the number of analysts or services sharing the project.
A low ceiling is safer for an experimental project. A production project may need a higher limit for a scheduled batch, but that limit should be paired with row-count controls, query review, and an on-call response for quota errors.
What happens when a quota is reached
Rejection before execution
BigQuery performs a pre-execution check. If the relevant daily quota has already been exhausted, the query is rejected with a QuotaExceeded error before additional model calls are made. This protects a project whose allowance was consumed by earlier jobs, even if the new query itself is small.
Failure during a running query
A query can start successfully and then reach a configured input or output quota while it is processing rows. Once the allowance is exhausted, new LLM calls in that query are rejected. Rows that depend on those calls can therefore return quota errors or fail to produce their generated value. Subsequent generative-AI queries continue to fail until the daily reset or until an administrator changes the effective quota and the service accepts the new limit.
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The special case of AI.IF
For functions such as AI.IF, the final result also depends on the function’s max_error_ratio argument. If the proportion of row-level errors remains within the permitted ratio, BigQuery may return partial results. If errors exceed that allowance, the query can fail. Set this argument deliberately: allowing partial output may be appropriate for a triage report, while a financial or compliance dataset may require a zero-error policy and a retry after the quota reset.
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Estimate usage before running a query
Use AI.COUNT_TOKENS
AI.COUNT_TOKENS estimates input-token consumption for the content you plan to send. Use it against the same prompt construction, representative columns, and row filters as the production query. The estimate is useful for deciding whether a batch fits inside the remaining daily allowance and for identifying a prompt that has grown unexpectedly large.
For a large table, sample enough rows to capture long and short values, then extrapolate cautiously. Keep a margin for retries and for output or thought tokens, which are separate from the input estimate. A count of input tokens alone cannot prove that the complete query will fit.
Control the amount of work sent to the model
- Filter rows before invoking the AI function instead of filtering after generation.
- Select only the columns needed to build the prompt.
- Trim or normalize unbounded text fields when the business rule permits it.
- Process a backfill in dated or key-range batches so one run cannot consume the entire daily allowance.
- Make retries explicit and bounded; an automatic retry can repeat input and output consumption.
Inspect token counts after execution
After a query finishes, open its Job information pane in the BigQuery interface. Review the reported input, output, thought, and cache token counts. Cache tokens are shown separately and do not count toward the documented quotas, but they are still useful for understanding how the service handled the request.
Compare the job’s counts with the estimate from AI.COUNT_TOKENS. A large difference usually points to a changed prompt, a different row population, model behavior that produced longer responses, or additional calls caused by query logic. Save the job ID and token details with the batch’s operational record so that a later charge investigation has a concrete starting point.
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Attribute Vertex AI charges to a BigQuery job
BigQuery AI functions can create Vertex AI charges. To attribute those charges, open Cloud Billing reports, filter the service to Vertex AI, and filter by the bigquery_job_id_prefix label. Google notes that some charges can take up to 24 hours to appear, so same-day billing data is not necessarily complete.
Use the label together with the BigQuery job’s token counts. The combination distinguishes a high-token job from a low-token job that happened to run many times and helps finance or platform teams map charges back to a scheduled query, user, or deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Quotas and Cloud Billing budgets solve different problems
| Safeguard | Primary purpose | Enforcement timing | Scope and visibility | Operational action |
|---|---|---|---|---|
| BigQuery AI token quota | Limit model-token consumption. | Before execution when the allowance is already exhausted; during execution when new calls exceed the remaining allowance. | Separate project and per-user controls; usage aggregates globally across regions; daily reset at midnight Pacific. | Quota errors stop or limit further calls; an administrator edits the quota or waits for reset. |
| Cloud Billing budget and alert | Monitor charges and notify owners when thresholds are reached. | Notification and reporting, subject to billing-data delay. | Billing-account or project reporting, with service and label filters such as Vertex AI and bigquery_job_id_prefix. |
Investigate, pause jobs, or change quotas manually; do not assume an alert instantly kills a query. |
Set both layers. Quotas provide the enforceable usage ceiling documented for BigQuery AI functions. Budgets provide financial visibility and escalation when charges approach a threshold. A budget alert is not a substitute for a token quota, and a token quota does not replace a budget because token consumption and billed amounts are not the same reporting system.
A practical operating policy for teams
Separate experimentation from production
Use a dedicated project or clearly bounded quota policy for experiments. Analysts can then test prompts without sharing the production project’s daily allowance. If projects cannot be separated, apply per-user limits and require a reviewed service account for scheduled production jobs.
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Schedule large backfills in batches and leave headroom for critical workloads. Because all regions draw from the same daily accounting, moving a job to another region is not a way to obtain a fresh quota.
Review changes as code and configuration
Store the quota values, prompt version, model choice, expected row count, and reset-time convention in the runbook or change record. When a prompt expands, a model changes, or a new team begins using the project, review both project and per-user ceilings rather than silently increasing only one.
Pair quotas with query controls
- Require a bounded
WHEREclause for ad-hoc production queries. - Review SQL that invokes an AI function more than once for the same row.
- Use scheduled batches for backfills instead of one unbounded query.
- Define whether partial results are acceptable before setting
max_error_ratioonAI.IF. - Alert the owner when a job’s measured token count materially exceeds its estimate.
Troubleshooting quota errors and unexpected charges
“QuotaExceeded” appears immediately
Check the project’s BigQuery API quota rows and the user’s applicable rows. Confirm whether another job consumed the allowance, whether the Pacific-time reset has occurred, and whether the displayed value differs from the number recorded in your runbook. Do not assume that an immediate failure means the SQL is invalid; it can be a normal pre-execution quota rejection.
Only some rows fail
If the query began successfully, inspect whether the daily input or output allowance was consumed during execution. For AI.IF, compare the observed error ratio with max_error_ratio. Preserve successful rows if the business process permits partial output, or rerun the failed batch after the reset with a smaller row range.
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Usage seems higher than expected
Open Job information and compare input, output, thought, and cache counts with the prompt estimate. Look for duplicated function calls, unexpectedly long source text, a wider row filter, retries, or a model change. Then check Cloud Billing’s Vertex AI report using bigquery_job_id_prefix; allow up to 24 hours for all charges to appear.
A budget alert arrived but queries are still running
Treat the alert as a signal to investigate, not as proof that execution was stopped. Pause or cancel jobs through your normal operational controls, then lower or override the BigQuery token quotas if additional model calls must be blocked. Keep the budget threshold and quota ceiling documented separately.
Pre-deployment checklist
- Project selected in the console is the project that runs the BigQuery jobs.
- BigQuery API is selected under Quotas & System Limits.
- Project input and output/thought quotas have explicit values.
- Per-user input and output/thought quotas have been reviewed, with the current console values recorded because official 2026 pages disagree.
- Daily reset at midnight Pacific time is reflected in schedules and on-call documentation.
AI.COUNT_TOKENShas been run on representative prompts and rows.- Job information token counts are captured after test runs.
- Cloud Billing reports can filter Vertex AI charges by
bigquery_job_id_prefix. - A Cloud Billing budget and alert are configured as a complementary financial monitor.
- Batch size, retry behavior, and
AI.IFerror policy are documented.
With these controls, a BigQuery AI workload has a measurable token allowance, a predictable response when that allowance is exhausted, and a billing trail that can be reconciled to individual jobs.
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