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The two ways quota exhaustion appears
BigQuery handles an exhausted AI token quota differently depending on when it discovers the problem. The distinction explains why one query can fail immediately while another job finishes with errors embedded in its output.
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Before execution: the entire query is rejected
BigQuery performs a token-availability check before executing a query that contains a generative-AI function. If the applicable project or per-user quota is already exhausted, the job is rejected with QuotaExceeded and no query work begins. You do not receive successfully processed rows because the model calls were never started.
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The error reference classifies quotaExceeded as an HTTP 403 error. Its message identifies the quota dimension that was exceeded, so read the complete error object rather than relying only on the short status name. The message may distinguish input tokens from output or thought tokens, and project scope from per-user scope.
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During execution: later model calls fail
If the query passes the pre-execution check but consumes the remaining allowance while running, BigQuery can complete some model calls and reject later ones. Rows that depend on those later calls encounter quota-exhaustion errors. The result can therefore contain a mixture of successful AI output and failed rows.
For functions that support max_error_ratio, that setting controls whether the job may return partial results. If the proportion of failed rows stays within the configured allowance, the query can return the successful rows and the associated errors. If failures exceed the allowance, the whole query fails instead. The setting does not restore quota or make rejected model calls succeed; it only determines the job’s tolerance for row-level errors.
When the quota becomes available again
After a daily generative-AI token budget is exhausted, subsequent queries that use the affected functions continue to fail with QuotaExceeded until the daily quota resets. The reset occurs at midnight Pacific time, not at midnight in the time zone of your BigQuery reservation, user, or dataset.
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The reset is a daily boundary, not a rolling 24-hour window. If a workload exhausts its budget shortly before midnight Pacific, waiting for the reset may be faster than redesigning the query; if it exhausts the budget just after midnight, the next reset may be almost a full day away.
Which token limit was exceeded?
Google Cloud lists these default daily limits for the relevant BigQuery generative-AI SQL functions with Gemini models (Google Cloud, 2026):
| Quota dimension | Default daily limit | Scope |
|---|---|---|
| Input tokens | 200,000,000,000 | Project |
| Input tokens | 150,000,000,000 | Per user |
| Output and thought tokens | 20,000,000,000 | Project |
| Output and thought tokens | 15,000,000,000 | Per user |
These are default daily values, and the year qualification matters: the figures are listed by Google Cloud for 2026. Your organization can have a different approved limit or a quota override. The error message is authoritative for the dimension that blocked a particular job.
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Project and per-user limits can be reached independently. A project may still have input-token capacity while one user has exhausted the per-user input allowance, or a user may have capacity while the project-wide output budget is depleted by everyone sharing the project. Because accounting is global across regions, aggregate all locations when investigating usage.
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This policy covers BigQuery SQL functions designed for generative-AI inference with Gemini models. Embedding-generation and semantic-search functions use different billing and quota structures and are not governed by this token-quota policy.
Why a successful job can still contain AI errors
A completed BigQuery job is not proof that every model request succeeded. In particular, AI.GENERATE_TEXT can return retryable RESOURCE EXHAUSTED errors in individual rows when the remote endpoint is under quota or rate pressure. The query job itself may finish successfully because the function represented those failures as row values rather than aborting the entire job.
This situation is different from a daily BigQuery token-quota rejection:
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| Symptom | Likely mechanism | What it means |
|---|---|---|
Job rejected before any execution with QuotaExceeded (HTTP 403) |
BigQuery project or per-user daily token quota was already exhausted | No model-dependent rows were processed; wait for reset or change the workload/quota policy |
| Some rows succeed and later rows contain quota errors | Daily token allowance was consumed during execution | Earlier calls completed; dependent later rows could not call the model |
Job succeeds but rows contain retryable RESOURCE EXHAUSTED |
Remote endpoint quota or rate pressure | Retry failed rows separately; this is not necessarily the daily BigQuery token budget |
Google documents iterative SQL scripts and a Dataform package for retrying rows that contain retryable RESOURCE EXHAUSTED failures. Retrying every row blindly can repeat successful work and consume more tokens, so isolate failed rows and preserve successful output before rerunning.
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How AI.GENERATE_TEXT chooses a quota path
The request_type option changes which serving capacity is used for AI.GENERATE_TEXT. It is a routing decision, not a way to create additional daily token capacity.
request_type |
Quota path | Operational implication |
|---|---|---|
DEDICATED |
Provisioned Throughput only | Requests require the configured dedicated capacity; shared quota is not used as a fallback |
SHARED |
Dynamic shared quota only | Requests depend on shared serving capacity and its current pressure |
UNSPECIFIED |
Provisioned Throughput first, with shared-quota overflow when applicable | Requests may use dedicated capacity and then overflow to shared quota |
Check this option when the error is RESOURCE EXHAUSTED or when your organization expects dedicated capacity but requests are reaching the shared path. Changing the request type does not erase a project or per-user daily token count; it addresses serving-path behavior.
What to do immediately after an error
- Read the complete error object. Confirm whether the status is
QuotaExceededorRESOURCE EXHAUSTED, and record the quota name or dimension named in the message. - Determine when the failure occurred. A job rejected before execution points to an already exhausted daily allowance. A completed job with failed rows points to calls rejected during execution or by a remote endpoint.
- Check both scopes. Review project-wide and per-user input, output, and thought-token consumption. Include every region because the accounting is global.
- Estimate the next reset. If the intended daily budget is gone, schedule the next attempt after midnight Pacific time. Do not expect changing dataset location or reservation location to accelerate the reset.
- Preserve successful rows. For a job that completed with row-level errors, materialize or otherwise retain successful output and create a retry set containing only failed rows.
- Reduce the next request. Process fewer source rows, shorten prompts, lower output-token limits, or split the workload into controlled batches.
- Validate routing. For
AI.GENERATE_TEXT, verify thatDEDICATED,SHARED, orUNSPECIFIEDmatches the intended Provisioned Throughput and shared-quota policy. - Request a policy change when necessary. If normal demand repeatedly exceeds the default, request a quota adjustment or configure an approved quota override that fits your organization’s cost-control rules.
Reducing the chance of another exhaustion event
Control input volume
Token usage grows with the number of rows and the amount of text sent in each prompt. Filter rows before invoking the AI function, remove fields the model does not need, and avoid sending the same long context repeatedly when a shorter representation is sufficient. A smaller input can prevent both project and per-user input limits from being reached.
Bound generated output
Output and thought tokens have separate project and per-user ceilings. Set an output limit appropriate to the task instead of allowing unnecessarily long completions. A summarization job that needs two sentences should not reserve a large completion budget designed for open-ended generation.
Batch and checkpoint work
Divide large tables into batches with a durable checkpoint for completed rows. Batching cannot exceed the daily allowance, but it limits the amount lost when a query encounters mid-execution exhaustion and makes retrying failed rows practical. Keep the batch size small enough that a single run’s failure does not require repeating an entire day’s work.
Use an explicit error policy
For functions supporting max_error_ratio, choose a value that reflects the business result you can accept. A low tolerance protects data completeness by failing the job when too many rows are missing; a higher tolerance allows useful partial output when occasional row failures are acceptable. Monitor the failed-row count separately so a technically successful job does not hide a material data-quality problem.
Separate daily-budget alerts from endpoint alerts
Alert on QuotaExceeded as a daily-budget condition and on row-level RESOURCE EXHAUSTED as a serving-pressure or retry condition. They require different responses: waiting for the midnight-Pacific reset or changing quota policy for the first, and targeted retries, batching, or request-type review for the second.
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QuotaExceededbefore execution: identify the named dimension, check project and user totals, then wait for the reset, reduce future demand, or pursue an approved quota adjustment.- Quota errors on later rows: keep completed rows, record failed rows, and rerun only the dependencies after capacity is available.
- Successful job with retryable
RESOURCE EXHAUSTEDrows: treat it as row-level remote-endpoint pressure; use an iterative retry process rather than rerunning the full table. - Unexpected shared-capacity behavior: inspect
request_typeand confirm whether dedicated Provisioned Throughput, dynamic shared quota, or overflow behavior is intended. - Repeated exhaustion despite batching: compare actual project and per-user consumption with the listed defaults and request a quota change or organizational override if sustained demand justifies it.
What quota exhaustion does not mean
It does not mean BigQuery has permanently disabled the AI function, that moving the query to another region will provide a fresh budget, or that every completed job produced complete AI coverage. It is a limit on the applicable token or serving capacity at a particular point in the request path. Correct diagnosis depends on the status, timing, error message, scope, and request routing.
Frequently Asked Questions
How long does a BigQuery generative-AI token quota failure last?
A daily token quota remains unavailable until it resets at midnight Pacific time. Regional changes do not bypass the limit because usage is aggregated globally.
Will BigQuery return partial results after quota exhaustion?
It can. If exhaustion occurs during execution, earlier rows may succeed and later rows may contain errors. For functions supporting max_error_ratio, the query returns partial results only when failures remain within the configured ratio; otherwise the job fails.
Is RESOURCE EXHAUSTED the same as QuotaExceeded?
No. QuotaExceeded generally indicates an exhausted BigQuery daily token quota and is reported as HTTP 403. A completed job with retryable RESOURCE EXHAUSTED row errors can indicate remote-endpoint quota or rate pressure and is handled with targeted retries.
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