Snowflake puts Decision model into Cortex AI Functions private preview

Snowflake has put Snowflake Decision into private preview through its AI_COMPLETE SQL function. Available to selected customers, the model handles classification, scoring, filtering, routing and triage tasks that return structured answers. It is not supported for production use, and access requires a request through a Snowflake account team rather than a self-service switch.

Decision follows the restricted-access pattern used for Snowflake’s Kimi K3 private preview through Cortex AI Functions, which was also limited to explicitly enabled accounts. A caller sends AI_COMPLETE a JSON string containing a “state” — text or structured data to evaluate — and one or more typed questions. The model returns the answers in one object, allowing the same input to be evaluated against several bounded criteria in a single call.

For a choice question, the caller supplies a fixed set of options. The response includes the selected option’s key, a probability for every option and a confidence value; Snowflake’s documentation says the option probabilities sum to 1.

For a score question, the caller defines an ordered rubric. The response provides a probability-weighted position from 0 to the number of levels minus one, probabilities for each level, a legend and confidence. Snowflake cautions that the score is not a precise measurement.

A true-or-false assessment returns the probability that a statement is true, not a Boolean value. The application sets the threshold that triggers a downstream action. Snowflake documents using the returned probabilities and confidence values to route results to automated actions, human review or a fallback.

One request can combine choice, score and true-or-false assessments of the same state. A SQL statement can also apply the request across rows in a table or query result. Preview limits include a 256 KiB request, no more than 32 questions, two to 32 options for each choice question, two to 10 levels for each score question and 256 options across all questions.

Snowflake describes Decision as optimized for throughput rather than interactive latency. Its documentation recommends processing rows in parallel and says a single call can take seconds, making the model unsuitable for latency-sensitive interactions. Users also need an appropriate Snowflake Cortex database role and the preview-specific account configuration.

The company says the purpose-built model can reduce the cost and latency associated with using general-purpose language models for simple, high-volume decisions. The opened evidence includes no independent measurements of Snowflake Decision’s cost, latency, throughput or production efficiency.

Snowflake also reports a score of 57.63 and first place among 72 models on a 29-benchmark subset of the Jev Decision Index 0.2.1, covering 88,837 requests. The company says interface limits prevented it from running the full benchmark suite. The subset score and ranking are company-reported and have not been independently established.

Snowflake says running the model inside Cortex AI Functions allows existing governance, access-control and audit policies to apply, but the opened materials do not include an independent assessment of that claim. Snowflake has not disclosed preview customer numbers, pricing, regional availability for selected accounts or a general-availability date.