Cost considerations for Cortex AI Functions

Snowflake Cortex AI functions incur compute cost based on the number of tokens processed. Refer to the Snowflake Service Consumption Table for each function’s cost in credits per million tokens.

A token is the smallest unit of text processed by Snowflake Cortex AI functions. An industry convention for text is that a token is approximately equal to four characters, although this can vary by model, as can token equivalence for media files.

  • For functions that generate new text using provided text (AI_COMPLETE, AI_CLASSIFY, AI_FILTER, AI_AGG, SUMMARIZE (SNOWFLAKE.CORTEX), and AI_TRANSLATE, and their previous versions in the SNOWFLAKE.CORTEX schema), both input and output tokens are billable.
  • For Cortex Guard, only input tokens are counted. The number of input tokens is based on the number of tokens output from AI_COMPLETE (or COMPLETE). Cortex Guard usage is billed in addition to the cost of the AI_COMPLETE (or COMPLETE) function.
  • For AI_SIMILARITY, AI_EMBED, and the SNOWFLAKE.CORTEX.EMBED_* functions, only input tokens are counted.
  • For EXTRACT_ANSWER, the number of billable tokens is the sum of the number of tokens in the from_text and question fields.
  • AI_CLASSIFY, AI_FILTER, AI_AGG, AI_SENTIMENT, AI_SUMMARIZE_AGG, SUMMARIZE, TRANSLATE, AI_TRANSLATE, EXTRACT_ANSWER, ENTITY_SENTIMENT, and SENTIMENT add a prompt to the input text in order to generate the response. As a result, the billed token count is higher than the number of tokens in the text you provide.
  • AI_CLASSIFY labels, descriptions, and examples are counted as input tokens for each record processed, not just once for each AI_CLASSIFY call.
  • For AI_PARSE_DOCUMENT (or SNOWFLAKE.CORTEX.PARSE_DOCUMENT), billing is based on the number of document pages processed.
  • For AI_EXTRACT, both input and output tokens are counted. The responseFormat argument is counted as input tokens. For document formats consisting of pages, the number of pages processed is counted as input tokens. Each page in a document is counted as 970 tokens.
  • For AI_REDACT, both input and output tokens are counted.
  • AI_COUNT_TOKENS incurs only compute cost to run the function. No additional token-based costs are incurred.

For models that support media files such as images or audio:

  • Audio files are billed at 50 tokens per second of audio.
  • The token equivalence of images is determined by the model used.

The cost associated with keeping a warehouse active continues to apply when executing a query that calls a Snowflake Cortex LLM Function. For general information on compute costs, see Understanding compute cost.

Warehouse sizing

Snowflake recommends using a warehouse size no larger than MEDIUM when calling Snowflake Cortex AI Functions. Using a larger warehouse than necessary does not increase performance, but can result in unnecessary costs. This recommendation may change in the future as we continue to evolve Cortex AI Functions.

Track costs for AI services

To track credits used for AI Services including LLM Functions in your account, use the METERING_HISTORY view:

SELECT *
  FROM SNOWFLAKE.ACCOUNT_USAGE.METERING_DAILY_HISTORY
  WHERE SERVICE_TYPE='AI_SERVICES';

Track credit consumption for Cortex AI Functions

To view the credit and token consumption for each AI Function call, use the CORTEX_FUNCTIONS_USAGE_HISTORY view:

SELECT *
  FROM SNOWFLAKE.ACCOUNT_USAGE.CORTEX_FUNCTIONS_USAGE_HISTORY;

You can also view the credit and token consumption for each query within your Snowflake account. Viewing the credit and token consumption for each query helps you identify queries that are consuming the most credits and tokens.

The following example query uses the CORTEX_FUNCTIONS_QUERY_USAGE_HISTORY view to show the credit and token consumption for all of your queries within your account.

SELECT * FROM SNOWFLAKE.ACCOUNT_USAGE.CORTEX_FUNCTIONS_QUERY_USAGE_HISTORY;

You can also use the same view to see the credit and token consumption for a specific query.

SELECT * FROM SNOWFLAKE.ACCOUNT_USAGE.CORTEX_FUNCTIONS_QUERY_USAGE_HISTORY
WHERE query_id='<query-id>';

Note

You can’t get granular usage information for requests made with the REST API.

The query usage history is grouped by the models used in the query. For example, if you ran:

SELECT AI_COMPLETE('mistral-7b', 'Is a hot dog a sandwich'), AI_COMPLETE('mistral-large', 'Is a hot dog a sandwich');

The query usage history would show two rows, one for mistral-7b and one for mistral-large.

See also

For day-to-day cost governance (usage views, account-level alerts, per-user spending limits, runaway query detection), see Managing Cortex AI Function costs with Account Usage.