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Reduce BigQuery AI spending by attacking two separate meters: scan fewer bytes in BigQuery, and send fewer prompt tokens and rows to the remote model endpoint. Filter and deduplicate data in ordinary SQL, project only the fields the model needs, materialize that small result, and then process homogeneous rows in deliberate batches. Set daily input and output token quotas, preserve completed results, and measure BigQuery bytes processed separately from inference charges.
Understand the two bills before optimizing
A query that invokes a BigQuery AI function can incur costs in two layers. BigQuery charges for the bytes processed by the SQL plan. The remote model endpoint adds inference charges for the data sent to the model and the output it generates. Reducing prompt length can lower endpoint usage while leaving a large table scan untouched; conversely, a highly selective SQL filter can reduce bytes scanned without changing the token cost of each remaining row.
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Google Cloud documentation says calls using Gemini 2.0 or later in the compared functions use batch API pricing. Pricing, model availability, quotas, regional support and function limits can change, so verify the current model and region configuration before committing a production design.
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|---|---|---|
| Reduce source rows | BigQuery bytes and model calls | Use WHERE predicates, remove duplicates and materialize only eligible rows. |
| Reduce projected columns | BigQuery bytes and prompt tokens | Select only fields needed to construct the instruction and expected answer. |
| Trim prompt text | Model input tokens | Remove repeated instructions, unused context and oversized examples. |
| Limit generated output | Model output tokens | Set a daily output-token quota and require the shortest useful response format. |
| Batch homogeneous work | Operational efficiency and retries | Send a materialized prompt table or a set-based query when every row uses the same task. |
| Inspect both invoices | Total cost visibility | Review BigQuery bytes processed and remote endpoint inference charges as separate numbers. |
Trim every part of the model input
Prompt text is billable model input. Token quotas count the instruction and other data supplied to the model, not just the business text you care about. A row can therefore become expensive through boilerplate that is repeated thousands of times.
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Keep only the required instruction
State the task once, specify the output shape, and remove prose that does not alter the answer. If the consumer needs one label, ask for one label rather than an explanation, confidence narrative and restatement of the source record. Keep a deterministic delimiter between the instruction and row data, but do not repeat a long policy block when a short rule has the same effect.
Project only fields that affect the result
Do not pass a complete customer, event or document record when two columns answer the question. Exclude internal IDs, ingestion metadata, unused dimensions and duplicate representations of the same text. The excluded columns still cost bytes if they are scanned and can add input tokens if they are concatenated into the prompt.
Remove duplicated context and examples
Few-shot examples are useful only when they change model behavior enough to justify their token cost. Use the smallest representative examples, avoid repeating the same example in every row, and remove context already present in the row. If examples are required, keep their format consistent so a batch does not contain several competing instruction styles.
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Watch visual-content prompts
Google Cloud documentation identifies fewer than 16,000 tokens for a visual-content prompt. Treat that figure as a documented limit for that prompt type, not as a general budget for every model or modality. Count text and other supplied content together, and leave headroom for the model and the current regional limit.
Filter and materialize rows before inference
The safest cost boundary is a normal SQL query that produces the exact rows requiring AI work. Apply eligibility predicates, remove duplicates, and select the prompt fields before invoking an AI function. Write that result to a temporary or permanent table, inspect its row count, and use the table as the function input.
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Why a direct source query is risky
When the AI function reads a large source relation directly, the query can scan and construct prompts for far more rows than the final result suggests. A later projection or result filter does not necessarily prevent those rows from entering the work graph. You also lose a stable checkpoint for retries and auditing.
Do not treat LIMIT or OFFSET as an early cost filter
In the documented prompt-query pattern, BigQuery can process all input before applying LIMIT or OFFSET. Those clauses are therefore unsuitable as the primary spending control. Use a selective WHERE predicate, deduplicate first, project only needed columns, and materialize the reduced set. If you need a sample for development, filter on a deterministic partition, date, key range or eligibility flag so the engine can eliminate data earlier.
Inspect the materialized batch
- Count rows and verify that each row has a non-null prompt payload.
- Check average and maximum prompt length; a few outliers can dominate token usage and runtime.
- Confirm that the table contains one logical task per row and no accidental duplicates.
- Record the batch identifier, source snapshot and model configuration so retries are reproducible.
Batch rows that share the same task
Batching is most effective when rows use the same instruction, output contract and model. A table of homogeneous prompts lets the service process work set-wise instead of submitting many separately managed calls. It also gives you a clear unit for quotas, retries and partial-result recovery.
Use the documented scale as a planning range
The function comparison describes processing between 1 million and 10 million rows per six-hour query job. This is a documented scale range, not a throughput promise. Actual throughput varies with average input-token length, output size, model, region, quota and concurrent workload. A batch of short classifications can behave very differently from a batch containing long documents.
Choose a batch boundary deliberately
Size batches around three limits: the time you can wait for a query job, the daily token quota you are willing to consume, and the amount of work you can safely retry. Smaller batches reduce replay cost and make quota exhaustion easier to recover from, but they add job overhead. Larger batches improve operational efficiency when prompts are uniform, but a failure can leave more work to replay.
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Use a stable key or batch ID rather than an arbitrary offset. Persist the eligible rows and mark each completed result. A subsequent run can select only rows without a successful result, avoiding duplicate inference charges.
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Do not blindly rerun a failed query over the entire source table. First identify whether the failure was a quota exhaustion, a transient job error, an invalid prompt or a model-region mismatch. Retry only unfinished rows after correcting the cause. If a row repeatedly fails, isolate it for inspection instead of allowing one malformed payload to force a full-batch replay.
Set quotas as a spending boundary
Daily input-token and output-token quotas limit exposure to runaway inference. Configure both rather than relying on a single ceiling: a short prompt can still generate expensive output, and a verbose prompt can exhaust input allowance before output becomes significant.
When a quota is exhausted, remaining rows that still require an LLM call can fail with quota exhaustion. Completed rows are not automatically a reason to repeat the entire job. Persist successful outputs, record the last completed batch, wait for the quota window or request an approved quota change, and then run only the unfinished set.
Estimate a safe allowance
- Measure representative prompt lengths after SQL concatenation, not just the source-column length.
- Estimate output tokens from the required format and set an explicit daily ceiling with retry headroom.
- Reserve capacity for scheduled jobs so an ad hoc backfill cannot consume the entire allowance.
- Alert before the quota is exhausted and include the batch ID in the alert.
Quotas are a guardrail, not a substitute for filtering. If the source query still scans millions of irrelevant rows, a quota may stop model calls while BigQuery continues to incur scan cost.
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Choose AI.GENERATE or AI.GENERATE_TEXT deliberately
The two functions support different pipeline shapes. Compare the input form, output contract and retry design before selecting one.
| Decision point | AI.GENERATE | AI.GENERATE_TEXT |
|---|---|---|
| Input shape | Can use a string or STRUCT prompt in a set-based query. | Table-valued function that can consume a prompt table or a prompt-producing query. |
| Output shape | Supports schema-constrained output, including a STRUCT. | Returns generated text suitable for text-oriented workflows. |
| Best fit | Tasks where downstream SQL needs typed fields and a defined schema. | Tasks where each row supplies a prompt and the result is principally text. |
| Batch design | Use a set-based query with the same instruction across eligible rows. | Materialize the prompt table first, then pass that table to the function. |
| Review before production | Confirm supported model, region, schema behavior and current pricing. | Confirm supported model, region, table-input limits, quotas and current pricing. |
Function choice does not remove either billing layer. A schema-constrained response can reduce downstream parsing work, but it does not make the input tokens or scanned bytes free. Likewise, table input makes row selection explicit; it does not eliminate the need to project only required columns.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Implement a materialized prompt table
The following pattern creates a small, retryable table and passes it to AI.GENERATE_TEXT. Adapt the model name, project, dataset, region and output handling to the current BigQuery documentation.
CREATE OR REPLACE TABLE `project.dataset.ai_batch` AS
SELECT
id,
CONCAT(
'Classify this record in one label. Return JSON. Text: ',
text_column
) AS prompt
FROM `project.dataset.source`
WHERE needs_ai = TRUE
AND text_column IS NOT NULL;
SELECT *
FROM AI.GENERATE_TEXT(
MODEL `project.dataset.gemini_model`,
TABLE `project.dataset.ai_batch`
);Make the SQL production-safe
- Replace the eligibility expression with the narrowest business predicate, such as an unprocessed status or a bounded date range.
- Deduplicate on the key that represents one logical task before constructing the prompt.
- Select only the identifier and source fields required by the instruction.
- Write the table and inspect its row count and prompt-length distribution.
- Invoke the AI function using the materialized table, not the original wide source.
- Store the returned text with the batch ID and model configuration, then mark successful keys complete.
If the same source row can change between retries, retain the source snapshot or a content hash. Otherwise a retry may produce a different prompt and make it difficult to distinguish a legitimate update from duplicate work.
Measure the result without confusing the meters
Check BigQuery bytes processed
Review the query plan and bytes processed for the materialization query and the AI invocation. A prompt optimization that still scans the same unpartitioned table may lower endpoint charges while leaving the BigQuery portion unchanged. Partition and cluster ordinary SQL tables where appropriate, and ensure predicates can eliminate data rather than merely filtering after a broad scan.
Check remote inference charges
Compare input and output token usage for the same number of completed rows. Look for repeated instructions, unexpectedly large columns, long generated explanations and retries. A reduction in row count should normally reduce calls; a reduction in prompt length should reduce input-token consumption even when the row count is constant.
Use a before-and-after run
- Record eligible row count, average prompt tokens and maximum prompt tokens.
- Record output-token usage, completed rows, failed rows and retry count.
- Record BigQuery bytes processed for the source and materialized-table steps.
- Record endpoint inference charges for the same time window.
- Compare quality metrics so a shorter prompt has not removed information the task genuinely needs.
Keep the comparison on the same model, region, date range and output contract. Otherwise a price or availability change can look like an optimization effect.
Troubleshoot common cost surprises
“The prompt is shorter, but the bill barely changed.”
Check whether the dominant amount is BigQuery scan cost rather than endpoint inference. A wide table, missing partition filter or unnecessary join can continue processing the same bytes. Inspect the SQL plan, materialize a narrow table and compare bytes processed before changing the prompt again.
“The query processed more rows than the LIMIT suggests.”
Remove reliance on LIMIT or OFFSET as a cost control. Apply a selective WHERE condition, deduplicate and materialize the eligible rows first. Verify the materialized row count before invoking the model.
“A quota failure left the job half complete.”
Use the persisted successful results as the checkpoint. Determine whether input or output tokens exhausted the daily allowance, wait for reset or obtain an approved increase, and rerun only keys without a completed result. Do not replay the full source table.
“Throughput is far below the six-hour range.”
Compare average input-token length with the documented 1-million-to-10-million-row range. Long prompts, large outputs, regional capacity, model limits and retries can all reduce throughput. Split oversized or heterogeneous work, shorten prompts and isolate rows that repeatedly fail.
“The model output is difficult to use downstream.”
When the task requires typed fields, evaluate AI.GENERATE and its schema-constrained output instead of forcing text parsing. Keep the same SQL filtering and batching discipline; changing the function does not replace those controls.
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Production checklist
- Define the minimum instruction and output format.
- Remove repeated instructions, unused columns, duplicated context and oversized examples.
- Use
WHEREpredicates and deterministic boundaries to select eligible rows. - Deduplicate before prompt construction.
- Materialize a narrow prompt table and inspect row count and token-length distribution.
- Batch homogeneous work and choose a retry boundary that fits the quota and job-time budget.
- Configure daily input and output token quotas with alerting.
- Persist successful results, batch IDs and source snapshots.
- Retry only unfinished rows after diagnosing the failure.
- Review BigQuery bytes processed and endpoint inference charges separately.
- Recheck current model, region, pricing and function limits before production rollout.
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