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Set enforceable daily limits in Google Cloud Console at IAM & Admin > Quotas & System Limits. Filter for Service: BigQuery API, find entries such as GenAiInputTokensPerDay and the related output/thought-token quotas, choose Edit, and submit conservative project and per-user values. These quotas reject or stop further BigQuery generative-AI calls when exhausted. Pair them with token monitoring and Cloud Billing budgets: quotas enforce usage, while budgets alert you to spending.
What BigQuery AI token quotas control
BigQuery’s generative-AI functions call Gemini models. Each call can consume input tokens, output tokens, and, where the model exposes them, thought tokens. Input tokens represent the prompt and data sent to the model; output tokens are generated text or structured results; thought tokens are internal reasoning tokens counted by the service. The quotas limit those token categories over a daily window.
A quota is an enforcement control, not a price estimate. The number of tokens a query consumes depends on the function, model, prompt, source rows, output length, and whether calls are repeated across rows. A low token price does not make an unrestricted workload safe: a large table or an accidental cross join can create millions of model calls. Conversely, a quota can stop a runaway workload even when a billing report has not yet updated.
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Project and per-user limits are separate. A project limit constrains aggregate usage for the project, while a per-user limit constrains usage attributed to an individual user. Quotas are tracked globally across regions, so running jobs in different locations does not create a separate daily allowance. They reset at midnight Pacific time. Cached tokens do not count toward these quotas.
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Google reports quota values in million-token increments. Very small limits may therefore not appear with perfect precision because of reporting and aggregation. Treat the value shown in the console as the authoritative effective setting for your project.
Published daily defaults—and an important per-user discrepancy
Google’s 2026 documentation publishes the following project defaults. Two official pages currently disagree about the per-user defaults, so do not assume either pair applies universally; verify the values displayed for your project before setting policy.
| Scope and token type | Published value | Qualification |
|---|---|---|
| Project input tokens | 200,000,000,000 per day | Google Cloud documentation, 2026 |
| Project output and thought tokens | 20,000,000,000 per day | Combined output/thought allowance in Google Cloud documentation, 2026 |
| Per-user input tokens | 150,000,000,000 per day | Value listed in Google’s control-costs guide, 2026 |
| Per-user output and thought tokens | 15,000,000,000 per day | Value listed in Google’s control-costs guide, 2026 |
| Per-user input tokens | 40,000,000,000 per day | Different value listed in Google’s quotas reference, 2026 |
| Per-user output and thought tokens | 4,000,000,000 per day | Different value listed in Google’s quotas reference, 2026 |
The conflicting per-user figures are a documentation inconsistency, not two allowances that are added together. Open the quota entry for the project and record the value the console exposes. If your organization has an existing override, that displayed value may differ from either published default.
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Set a project or per-user quota in the Cloud Console
You need permission to view and edit quotas for the project that runs the BigQuery jobs. Make the change in that project, not merely in a project where you happen to open the console.
- Open the correct project. In Google Cloud Console, select the project that owns the BigQuery jobs and billing relationship you intend to control.
- Open the quota page. Go to IAM & Admin > Quotas & System Limits.
- Filter the service. Set Service to BigQuery API. This removes unrelated API quotas from the result set.
- Find the token entries. Search for
GenAiInputTokensPerDay. Locate the project input quota and the corresponding per-user input and output/thought entries. Depending on the console view, output and thought tokens may appear together in one quota name or as closely related entries. - Select the quota row and choose Edit. Confirm the scope (project or user) before changing the value. Editing a project row does not change the per-user row.
- Enter a conservative daily ceiling. Base it on expected row count, prompt size, model behavior, and the maximum number of scheduled or ad hoc runs. Set input and output/thought ceilings independently; a workload can exhaust one category while the other remains available.
- Submit the quota change or override. Wait for the console to confirm the request. The effective value is the one shown after the change is applied, not the number you intended to enter.
- Document the decision. Record the project, scope, token category, value, owner, date, workload assumptions, and a rollback contact in your team runbook.
Start with a limit that protects the budget while leaving room for normal daily work. After observing several representative runs, adjust the ceiling rather than copying the very large published default into every project.
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Choose limits from workload reality
Estimate input demand
Before production, identify how many rows can invoke an AI function in one day and how much text each row contributes. Include fixed prompt text, column values, instructions, and any repeated context. A query that scans 10 million rows can approach a very different limit from a query that processes 10,000 rows, even when the SQL looks similar.
Use AI.COUNT_TOKENS to estimate input-token consumption before executing the production query. Run the estimator against representative prompts and data, then multiply by the largest plausible daily row count. Keep an allowance for retries, scheduled reruns, and analyst experimentation.
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Output length is workload-dependent. A function returning a short label usually consumes fewer output tokens than one producing a summary or JSON document. Models that use thought tokens can consume additional tokens that are not visible in the returned text. Set the output/thought quota using the longest acceptable result, not only the average result.
Use separate project and user guardrails
A project limit protects the shared budget. A per-user limit prevents one identity, service account, or analyst from consuming the whole project allowance. Set both when multiple teams or automated principals share the project. For a single controlled pipeline, the project limit may be the primary guardrail, but review whether service accounts are attributed as expected before relying on a per-user ceiling.
Revisit limits after material changes
- Changing the Gemini model or generation settings can change output and thought-token consumption.
- Adding columns or longer source text increases input tokens.
- Increasing row counts, backfill windows, or schedule frequency raises daily demand.
- Moving jobs between regions does not create a new quota pool because accounting is global.
What happens when a quota is reached
Before execution
BigQuery performs a pre-execution check. If the relevant daily quota is already exhausted, the query is rejected with a QuotaExceeded error before it can make additional generative-AI calls. This is the cleanest failure mode: no new model work is started by that query.
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During execution
If a configured input or output quota is exhausted while a query is running, subsequent LLM calls in that query are rejected. Rows that depend on those calls can therefore encounter quota errors even though earlier rows succeeded. The query may contain a mixture of successful results and row-level failures, depending on the function and error handling.
After exhaustion
Further generative-AI queries continue to fail until the daily reset at midnight Pacific time, unless an administrator changes the quota or the service’s accounting shows remaining capacity. The reset is global rather than region-specific.
The AI.IF exception
For functions such as AI.IF, the final result also depends on the max_error_ratio argument. If the proportion of failed rows remains within that allowed ratio, BigQuery can return partial results; if errors exceed the permitted ratio, the operation fails. Set this argument deliberately: a permissive ratio can preserve usable rows during a quota event, while a strict ratio surfaces the problem quickly for remediation.
Monitor token use before and after every important run
Preflight with AI.COUNT_TOKENS
Use AI.COUNT_TOKENS on representative input to estimate the tokens a planned call will send. Compare the estimate with the remaining daily project and per-user ceilings, accounting for all other workloads that share the same quota. This is an estimate of input consumption; it does not replace observation of generated or thought tokens.
Inspect the BigQuery job
After execution, open the query’s Job information pane. Review the input, output, thought, and cache token counts. Cache tokens are reported for visibility but do not count toward the daily quotas. Compare actual counts with the preflight estimate and retain the job identifier for incident analysis.
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Attribute Vertex AI charges in Cloud Billing
To connect model charges to a BigQuery job, 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. A same-day billing report can therefore be incomplete even when the BigQuery job details are already available.
Build a simple operating record
- Store the job ID, query owner, model/function, row count, and token counts.
- Record the quota values in force at the time of the run.
- Compare daily project and per-user consumption with scheduled workload forecasts.
- Investigate sudden changes in input, output, or thought tokens before raising limits.
Quotas and Cloud Billing budgets serve different purposes
| Safeguard | What it controls | When it acts | Scope and visibility |
|---|---|---|---|
| BigQuery token quota | Input, output, or thought-token usage | Rejects a query before execution when exhausted; rejects additional LLM calls during execution | Project or user; accounting is global across regions; daily reset at midnight Pacific time |
| Cloud Billing budget and alert | Monitors Google Cloud charges and sends notifications | After spend data is processed and alert thresholds are evaluated | Billing-account or configured budget scope; some Vertex AI charges may take up to 24 hours to appear |
A budget is not documented as an immediate query kill switch. It can warn an owner that spending is approaching a threshold, but it should not be treated as a hard spending cap that synchronously blocks BigQuery AI calls. Use quotas for the enforceable usage ceiling and budgets for financial visibility and escalation.
Configure the budget to cover the same billing account or projects that contain the BigQuery workloads, then route alerts to the people who can reduce schedules, pause jobs, or change quotas. Keep the quota and budget thresholds intentionally different: the quota should stop an unsafe workload before the budget’s delayed charge data catches up.
Design a two-layer cost-control policy
Layer 1: enforce token ceilings
- Set project input and output/thought quotas below the maximum level your budget could tolerate in one day.
- Set per-user limits for analysts, service accounts, or teams sharing the project.
- Use a lower quota in development and a separately reviewed quota in production.
- Require an owner to approve quota increases and record the reason and expiry date.
Layer 2: observe and respond
- Estimate input tokens with AI.COUNT_TOKENS before a new query pattern is released.
- Inspect Job information after representative runs.
- Review Vertex AI billing reports using the
bigquery_job_id_prefixlabel, allowing for the 24-hour reporting delay. - Use Cloud Billing budget alerts to trigger operational action, not as the sole enforcement mechanism.
Protect against query-shape mistakes
- Check row counts before adding an AI function to a query.
- Test on a bounded sample, then estimate the full run.
- Review joins for accidental row multiplication.
- Schedule large backfills in controlled batches so one mistake cannot consume the entire daily allowance.
- Keep prompts concise and avoid sending columns the model does not need.
Troubleshoot quota errors and unexpected charges
A query immediately returns QuotaExceeded
Open Quotas & System Limits and check the relevant project and per-user rows. Confirm whether another job already consumed the daily allowance, whether the quota is global across a different region, and when the next midnight-Pacific reset occurs. Do not repeatedly retry: retries cannot succeed while the same daily quota remains exhausted.
Only some rows fail
Inspect the job’s token counts and identify where the quota was crossed. A running query can complete earlier LLM calls and reject later ones. For AI.IF, check max_error_ratio and decide whether partial results are acceptable. If not, rerun after the reset with a smaller batch or a lower estimated token demand.
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Billing is higher than expected but quotas look normal
Compare the job’s input, output, and thought tokens with the Vertex AI billing report. Remember that billing data can lag by up to 24 hours. Check for multiple scheduled runs, retries, backfills, or row multiplication. A quota limits token volume; it does not guarantee a particular currency amount when model pricing or workload composition changes.
The console value does not match a published default
Use the console value as the effective setting. An administrator may have applied an override, and Google’s two 2026 pages list different per-user defaults. Record the displayed value and its scope in the runbook instead of assuming the larger or smaller published number.
A limit seems too coarse for a small workload
Quota values are tracked in million-token increments, so very small requested changes may not be reflected exactly. Choose a practical ceiling at the supported granularity and use query-level batching, sampling, or scheduling controls to handle finer operational limits.
Runbook checklist for administrators
- Identify the project, billing account, regions, users, and service accounts that run BigQuery AI functions.
- Record the current project and per-user input and output/thought quotas from the BigQuery API quota page.
- Estimate representative input demand with AI.COUNT_TOKENS.
- Set conservative daily quotas through IAM & Admin > Quotas & System Limits.
- Test a bounded query and inspect Job information token counts.
- Configure Cloud Billing budgets and alert recipients for the associated billing scope.
- Document the midnight-Pacific reset, quota owners, escalation path, and rollback procedure.
- Review limits whenever the model, prompt, row count, schedule, or workload region changes.
Key decisions to make before enabling a new AI function
First decide whether the workload needs row-by-row generation or can be reduced through filtering and batching. Next decide the maximum daily input and output/thought volume the project can tolerate, then set both project and per-user ceilings below that risk boundary. Finally, decide how the application should behave at the boundary: fail fast, accept partial results where AI.IF and max_error_ratio permit them, or defer work until the next reset.
This sequence matters because a billing alert cannot undo model calls already made. A quota creates the hard operational boundary; token counts explain what happened; billing reports attach a financial value after the provider processes the usage.
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