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These quotas are guardrails, not a complete price quote. Gemini model usage can create Vertex AI charges in addition to BigQuery query-processing charges, so both must be considered when you estimate cost.
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The four BigQuery generative-AI token quotas
Google Cloud’s current quota table lists four daily defaults for BigQuery generative-AI functions that use Gemini models. Each limit has both a token type and a scope; confusing those dimensions is a common cause of unexpected QuotaExceeded errors.
| Quota | Daily documented default | Scope |
|---|---|---|
| Input tokens | 200,000,000,000 | Per project |
| Input tokens | 150,000,000,000 | Per project, per user |
| Output and thought tokens | 20,000,000,000 | Per project |
| Output and thought tokens | 15,000,000,000 | Per project, per user |
These are current documented defaults, not guaranteed permanent values. Google Cloud can change quota and pricing documentation, so verify the quota table before setting production limits or publishing a budget.
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Project limits and user limits work together
A project-wide allowance is shared by all users and jobs in the project. The per-user allowance applies to each individual user within that project. A request can therefore be blocked even when the project still has capacity: exhausting the requesting user’s allowance is sufficient to reject it. Conversely, one user can remain below the personal limit while the project-wide pool has been consumed by activity from many users.
Input versus output and thought tokens
Input quotas cover data sent to the model, including the prompt and other values passed by the SQL function. Output quotas cover generated candidate tokens and the model’s internal reasoning, called thought tokens in the quota documentation. A workload with short prompts but unusually long generated responses can therefore hit the output-and-thought limit first.
Which BigQuery AI functions use these quotas?
The four limits are for generative inference. Examples include AI.CLASSIFY and AI.GENERATE_TEXT, along with other BigQuery functions that send a generative request to a Gemini model.
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Google explicitly excludes embedding-generation and semantic-search functions from these generative token quotas. In particular, AI.EMBED and AI.GENERATE_EMBEDDING are not measured against the four daily input and output/thought pools described above. They use different billing structures and should be planned separately.
Do not use the 200-billion or 20-billion figures as a capacity estimate for an embedding pipeline. Check the current documentation for the embedding function’s own limits and charges, and keep its jobs separate when you analyze usage.
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Why the distinction matters operationally
A project can run out of generative input tokens while embedding jobs continue, or incur embedding charges without affecting the generative pools. Monitoring and budget controls should therefore identify the function family rather than treating every function whose name begins with AI. as one quota category.
What counts toward a token quota?
Prompt and row data are input
Every value sent to the model contributes to input usage. For a query that invokes a function once per row, the prompt template, column values, and any other request data are counted for each model call. A query can consume a large allowance even when its SQL text is short if it processes many rows or sends large text fields.
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Generated candidates and reasoning are output
The output side includes the candidate tokens returned by the model and its internal thought tokens. Thought tokens are not necessarily visible in the query result, but they still count against the output-and-thought quota. This is why a result set containing concise labels can consume more output quota than the visible text suggests.
Cached tokens are excluded
Google states that cached tokens do not count toward these quotas. Caching can therefore change quota consumption for repeated model context, but it does not turn a query into a free operation: model pricing and BigQuery processing charges still need separate evaluation.
Usage is tracked in million-token increments
Quota accounting is tracked in million-token increments. Very small configured limits may not appear perfectly precise because the reporting and enforcement granularity is larger than a single token. Treat a displayed value near a threshold as an approximation rather than proof that exactly one more token remains.
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When do the quotas reset?
Google’s documented boundary is midnight Pacific time. The reset is a daily boundary in Pacific Time, not midnight in the region where your dataset or query job runs. Teams operating outside the Pacific time zone should convert that boundary when scheduling overnight workloads.
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Quota usage is tracked globally across all regions. A project does not receive an independent daily token pool for each BigQuery location. Running jobs in multiple regions can draw down the same project-level allowance, so moving a query to another region will not bypass an exhausted quota.
Do not generalize this rule to every BigQuery quota
The midnight-Pacific rule applies to the documented daily token quotas for these generative-AI functions. BigQuery has other quota categories with different behavior; the general quotas guidance notes that some daily quotas can be replenished intermittently. Check the specific quota entry instead of assuming every BigQuery limit resets at the same instant.
What happens when BigQuery returns QuotaExceeded?
Before execution: the query is rejected
BigQuery performs a pre-execution quota check. If the relevant project or per-user allowance is already exhausted, the query is rejected with a QuotaExceeded error before new model calls are started. This can happen even when the SQL is valid and the referenced tables are available.
During execution: later model calls fail
A running query can consume its remaining allowance and exhaust a quota partway through processing. New LLM calls made after exhaustion are rejected, and rows that depend on those calls can receive quota errors. Earlier rows may have completed successfully while later rows fail, so inspect the job’s error details and result behavior rather than assuming an all-or-nothing model response.
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The next generative query continues to fail until reset
Once a daily project or user quota is exhausted, subsequent generative-AI queries that require that allowance fail until the daily reset. Waiting for midnight Pacific time is the normal recovery when no quota increase or override changes the limit.
Partial results and max_error_ratio
Functions that support max_error_ratio, such as AI.IF, can use that setting to decide whether errors remain within an accepted fraction of rows. Depending on the configured ratio and observed failures, BigQuery can return partial results or fail the entire query. This setting does not replenish tokens; it controls how the query responds to row-level function errors.
How administrators view or change a quota
Quota changes require the appropriate Google Cloud permissions. An administrator can inspect the current setting, request more capacity, or deliberately impose a lower cap.
- Open Google Cloud Console.
- Go to IAM & Admin → Quotas & System Limits.
- Set the service filter to BigQuery API.
- Find the relevant generative-AI entry, such as
GenAiInputTokensPerDay. - Open the quota row and choose the option to edit or request a change.
- Enter the desired limit, explain the workload and capacity need when prompted, and submit the request.
Requesting an increase
An increase is appropriate when legitimate workloads repeatedly reach the documented default and the project needs more daily capacity. Approval is subject to Google Cloud’s capacity and policy review; submitting a request does not guarantee a particular value or timing.
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Creating a stricter quota override
An override can impose a lower ceiling than the service default. This is useful when you want a hard guardrail against runaway jobs or budget overruns. A lower override can cause expected production queries to fail sooner, so document the owner, limit, and reset boundary before applying it.
How quotas relate to billing
Generative model charges and BigQuery charges are separate
Google says token usage for BigQuery generative-AI functions correlates directly with Vertex AI billing when Gemini models are used. Separately, BigQuery can charge for query processing. A quota dashboard tells you how much token capacity was consumed; it does not, by itself, calculate the complete invoice.
For a cost estimate, account for both sides:
- Model usage: input tokens and output/thought tokens used by the Gemini-backed function, subject to the applicable model pricing.
- BigQuery processing: the data scanned and other query-processing charges associated with running the SQL job.
Cached tokens are excluded from the daily token quotas, but that exclusion should not be interpreted as a blanket waiver of all model or query charges. Pricing depends on the service and model terms in effect for your project.
Provisioned-throughput models are a special case
Google notes that Gemini Enterprise Agent Platform models using provisioned throughput are not billed by token usage. In that arrangement, setting a low token quota can block queries even though token consumption is not the billing meter. Google recommends setting the token quotas high enough for the provisioned capacity so the quota guardrail does not unnecessarily interrupt workloads.
Planning workloads around the limits
Estimate both dimensions before a large run
Estimate input volume from the number of model calls multiplied by the prompt and row data sent per call. Estimate output separately, including the possibility of thought tokens. Compare each estimate with both the project-wide and per-user limits; the smaller remaining allowance is the effective constraint for a request.
Separate generative and embedding pipelines
Track generative functions and embedding or semantic-search functions as different workload classes. Their quotas and billing structures differ, so combining them in one daily budget can hide the reason a job failed or a bill changed.
Schedule with the Pacific reset in mind
If a batch must run after a fresh allowance becomes available, schedule it after midnight Pacific time and account for the project’s global usage from every region. Do not assume that midnight in the dataset’s local time starts a new pool.
Use overrides as governance, not as a cost calculation
A quota override can cap exposure, but it cannot predict the invoice. Pair the cap with query-cost controls, model pricing review, and job-level monitoring so an apparently safe token limit does not conceal high BigQuery processing charges.
Quick Recap
Troubleshooting checklist
- Immediate
QuotaExceededbefore any rows return: check whether the project-wide or requesting user’s daily allowance is already exhausted. - Only later rows fail: the running query may have consumed the remaining quota mid-execution; inspect row-level errors and job details.
- Moving the job to another region changes nothing: generative token usage is global across regions.
- Embedding work is unexpectedly unaffected: that is consistent with
AI.EMBEDand related embedding functions using separate quota and billing rules. - A query still fails after a local midnight: the documented reset is midnight Pacific time, not local midnight.
- Usage appears slightly above or below a tiny configured threshold: quota accounting uses million-token increments, so small values are not perfectly granular.
- The token allowance looks adequate but costs are high: review BigQuery query-processing charges separately from model-token billing.
Key takeaways for production owners
- The documented defaults are 200 billion and 150 billion input tokens per day at project and per-user scopes, plus 20 billion and 15 billion output-and-thought tokens at those same scopes.
- These limits cover Gemini-backed generative functions, not
AI.EMBED,AI.GENERATE_EMBEDDING, or other embedding and semantic-search functions. - Daily generative token quotas reset at midnight Pacific time and are shared globally across regions.
- Input includes prompt and request data; output includes generated and internal thought tokens; cached tokens do not count.
- BigQuery can reject a query before execution or reject later model calls after a running query exhausts its allowance.
- Quota settings are capacity guardrails. Complete cost planning requires separate consideration of Vertex AI model charges and BigQuery query-processing charges.
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