The generative AI functions in BigQuery (AI.GENERATE, AI.IF, AI.CLASSIFY, AI.SCORE, and more) bring large language models (LLMs) directly into your SQL, so a single query can classify millions of support tickets or score each product review for sentiment. However, because these functions are billed by the token, scaling them across massive datasets highlights the importance of managing your budget proactively.
We recently announced the general availability (GA) of token-based cost management for BigQuery generative AI functions: daily quotas that let administrators set self-serve guardrails on AI token consumption, so teams can innovate while staying within your budget. In this blog, we provide an overview of cost management for BigQuery generative AI functions, walk through a few practical examples, and share resources to help you get started. Let’s jump in.
Quotas for managing costs
Four daily quotas, managed through the Google Cloud Identity and Access Management (IAM) quota system, provide controls at the project level and the per-user level. Per-user limits apply to each user within a project, not across your organization.
|
Quota name |
Metric |
Scope |
Default value |
|---|---|---|---|
|
AI input tokens per day |
Input tokens used by the LLM |
Per day, per project |
200 billion |
|
AI input tokens per day per user |
Input tokens used by the LLM |
Per day, per project, per user |
150 billion |
|
AI output tokens per day |
Output and thinking tokens used by the LLM |
Per day, per project, per project |
20 billion |
|
AI output tokens per day per user |
Output and thinking tokens used by the LLM |
Per day, per project, per user |
15 billion |
Key details:
-
The quotas are global. Usage is summed across all regions, so costs can’t pile up unnoticed in a region you weren’t watching.
-
Output quotas include “thinking” tokens. Reasoning tokens count toward your output quota, just as they count toward your Gemini platform bill.
Which functions are covered?
The quotas apply to these BigQuery functions that perform LLM-backed generation:
What’s not covered: Embedding functions such as AI.GENERATE_EMBEDDING (also available as ML.GENERATE_EMBEDDING) and AI.EMBED are not subject to these quotas, because they run on different, substantially cheaper models.
Managing your quotas
Administrators can view and adjust the quotas directly in the Google Cloud console as workloads change, with no support case required.
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Navigate to IAM & Admin → Quotas & System Limits.
-
Filter by service and select BigQuery API.
-
Sort by the Name column to bring the four AI quotas to the top.
