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When a BigQuery generative-AI token quota is exhausted, BigQuery either rejects the query before it starts or rejects individual model calls while the query is running. A pre-execution exhaustion produces a QuotaExceeded failure. If exhaustion happens during execution, later LLM calls fail and rows that depend on them receive quota errors. Functions that support max_error_ratio, such as AI.IF, can return partial results only when the error percentage stays within the configured allowance. Otherwise, the entire query fails.
The daily generative-AI budget remains unavailable until it resets at midnight Pacific time. Token consumption is aggregated globally across regions, so changing a job’s location does not create a fresh allowance.
What BigQuery does at each stage
Before execution: the job is rejected
BigQuery checks available token quota before executing a query that contains a generative-AI function. If the applicable quota is already exhausted, no model calls are made and the query job is rejected with QuotaExceeded. The error is an HTTP 403 response, and its message identifies the quota dimension that was exceeded.
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During execution: later calls and dependent rows fail
A query can begin while quota is available and consume the remaining budget as it processes rows. Once the budget is exhausted, subsequent LLM calls are rejected. Any rows that depend on those calls encounter a quota-exhaustion error. Earlier rows whose model calls completed can remain available, while later dependent rows fail.
Whether the job is reported as successful or failed depends on the function’s error-handling behavior and the resulting error ratio. A successful job does not necessarily mean every generated value succeeded.
Partial results depend on max_error_ratio
For supported functions, max_error_ratio sets how much row-level failure the query may tolerate. If the proportion of failed rows remains within that allowance, BigQuery can return partial results together with errors for affected rows. If failures exceed the configured ratio, the whole query fails. The setting does not restore quota or make rejected model calls succeed; it only controls whether an otherwise mixed result can be accepted.
For AI.IF, check the configured ratio when deciding whether a query that encountered quota errors can still produce a usable result set. Do not assume that every BigQuery AI function supports this behavior; apply the option only where the function documentation exposes it.
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How long the failure lasts
Daily reset at midnight Pacific time
After the daily generative-AI token budget is exhausted, subsequent queries that use the affected generative-AI functions continue to fail with QuotaExceeded until the daily quota resets at midnight Pacific time. The reset is based on Pacific time, not the time zone of the project, user, reservation, or job.
If a query fails before execution, retrying it immediately does not help: the same quota state is checked again. If a running query consumed the last tokens, new queries can fail as soon as the exhaustion is recorded. Schedule a retry after the reset or reduce the workload so it fits within the remaining allowance.
Quotas are global across regions
BigQuery aggregates token usage globally across regions for this policy. Running the same SQL in another BigQuery location does not provide an independent daily token pool. Account for usage from every region when investigating a project-wide exhaustion event.
Which quota can be exhausted?
The cost-control limits distinguish project and per-user usage, and they distinguish input tokens from output and thought tokens. Google Cloud lists these default daily limits for 2026:
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| Quota dimension | Default daily limit | Scope and qualification |
|---|---|---|
| Project input tokens | 200,000,000,000 | Google Cloud default for 2026; aggregated at project scope |
| Per-user input tokens | 150,000,000,000 | Google Cloud default for 2026; aggregated for an individual user |
| Project output and thought tokens | 20,000,000,000 | Google Cloud default for 2026; aggregated at project scope |
| Per-user output and thought tokens | 15,000,000,000 | Google Cloud default for 2026; aggregated for an individual user |
These are daily defaults, not a promise that every organization has the same effective allowance. A query can fit under a project total yet be blocked by a per-user limit, or the reverse. The exact error message identifies which dimension was exceeded, so use that message rather than guessing from the query’s token count.
How to diagnose QuotaExceeded or RESOURCE EXHAUSTED
1. Read the complete error object
Start with the error object’s HTTP status, reason, and message. A quotaExceeded error is HTTP 403, and the message names the quota that was exceeded. Record whether it refers to project input, user input, project output/thought, or user output/thought tokens.
2. Check both project and user consumption
Because the limits have project and per-user dimensions, inspect usage for the project and the identity running the query. Include jobs from all regions in the analysis. A regional dashboard or a single user’s activity can understate the total that is consuming the global allowance.
3. Determine when the failure occurred
- Rejected immediately: the pre-execution check found no available quota. The query did not begin model inference.
- Errors in later rows: the query started, quota ran out while processing, and dependent LLM calls were rejected.
- HTTP 403 with
quotaExceeded: a BigQuery quota dimension is exhausted. RESOURCE EXHAUSTEDin individualAI.GENERATE_TEXTrows: investigate remote-endpoint quota pressure as well as the daily token budget.
Why a job can succeed while rows contain RESOURCE EXHAUSTED
AI.GENERATE_TEXT can return a completed query job even when some rows contain retryable RESOURCE EXHAUSTED errors caused by remote-endpoint quota pressure. This is a row-level outcome, not proof that every model call completed. Treat the generated column and its error information separately: keep successful rows, identify retryable failures, and retry only the failed work.
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Google documents iterative SQL scripts and a Dataform package for retrying those rows. Iterative processing is preferable to rerunning the entire original dataset, because a full rerun repeats successful calls and can consume additional tokens before reaching the same endpoint pressure.
AI.GENERATE_TEXT request types and quota routing
The request_type value determines which serving path is used for AI.GENERATE_TEXT. Verify this setting when the observed error does not match the quota you expected:
request_type |
Quota path | Operational implication |
|---|---|---|
DEDICATED |
Provisioned Throughput only | Requests do not use the dynamic shared-quota path. |
SHARED |
Dynamic shared quota only | Requests depend on shared capacity and its current availability. |
UNSPECIFIED |
Provisioned Throughput first, with shared-quota overflow when applicable | Overflow can move requests onto shared capacity, so observed failures may differ from a dedicated-only expectation. |
Changing the request type is not a way to bypass a project or per-user daily token limit. It selects the serving and quota route for the request; the daily generative-AI policy still applies.
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Wait when the budget was intentionally consumed
If the error identifies an exhausted daily token budget and the workload is complete for the day, wait for the midnight-Pacific reset. Avoid repeated retries before that time; they will continue to fail while the quota is unavailable.
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Reduce the amount of work per run
- Process fewer rows in each query.
- Shorten prompts and remove input that the model does not need.
- Lower output limits where the application can accept shorter responses.
- Run batches or iterative chunks instead of one large statement.
- Persist successful results and submit only failed or unprocessed rows on the next batch.
These changes reduce token consumption and also limit the number of rows exposed to a mid-query exhaustion event.
Retry row-level failures selectively
For a completed job with retryable RESOURCE EXHAUSTED values, filter to the failed rows, wait for endpoint capacity when necessary, and use an iterative SQL script or the documented Dataform package. Preserve the successful rows so that retries do not duplicate model calls.
Request more capacity when the limit is a policy constraint
If the default limit is too restrictive for a recurring workload, request a quota adjustment or configure a quota override that complies with your organization’s cost-control policy. An override is an administrative change, not an automatic recovery for a query that has already failed; continue to design batches and retries for ordinary endpoint pressure.
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Confirm the serving route
For AI.GENERATE_TEXT, verify that request_type matches the intended Provisioned Throughput or shared-quota path. A setting of DEDICATED, SHARED, or UNSPECIFIED can change where capacity is sought and how overflow is handled.
Daily token quotas versus endpoint exhaustion
| Characteristic | Daily token quota exhaustion | Remote-endpoint quota pressure |
|---|---|---|
| Typical signal | HTTP 403 quotaExceeded; message identifies the exhausted token dimension |
Retryable RESOURCE EXHAUSTED values in some AI.GENERATE_TEXT rows |
| Where it appears | Before execution or when new calls are attempted after the daily budget is consumed | During execution at the remote model endpoint |
| Query outcome | Immediate job rejection, or later dependent rows fail; supported functions may allow partial results | The overall job can finish successfully while individual rows carry errors |
| Best first response | Identify project/user dimension, reduce or batch work, or wait for the midnight-Pacific reset | Retry failed rows iteratively and investigate serving capacity and request_type |
Scope of this token policy
This policy covers BigQuery SQL functions designed for generative-AI inference with Gemini models. Embedding-generation and semantic-search functions use different billing structures and are outside this token-quota policy. Do not interpret an error from those functions using the generative-AI token limits described here.
Quick Recap
A practical decision checklist
- Capture the full error object and confirm whether the reason is
quotaExceededorRESOURCE EXHAUSTED. - For
quotaExceeded, note the exact project or per-user input/output/thought-token dimension named in the message. - Check usage across every region and across all users contributing to the project total.
- Decide whether the query was rejected before execution or produced row-level errors after starting.
- If the daily budget is exhausted, schedule work after the midnight-Pacific reset.
- If only some rows failed, retain successful rows and retry the failed set iteratively.
- Reduce rows, prompt size, or output limits before the next run.
- For recurring demand, request an approved quota adjustment or configure a policy-compliant override.
- For
AI.GENERATE_TEXT, verify thatrequest_typeselects the intended dedicated, shared, or overflow behavior.
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