By Mukundan Sivaraj · AIM Media House
Snowflake has introduced dynamic model routing in Cortex AI Gateway, allowing enterprises to automatically select AI models for individual tasks based on factors including cost, quality and latency.
The company announced the capability on August 18 as part of a push to improve what CEO Sridhar Ramaswamy calls “intelligence efficiency.” Snowflake said its initial benchmarks showed the routing approach could deliver better economics at a given quality level than relying on a single model.
The announcement comes as enterprises face growing costs from AI inference. As AI systems move into production, the economics of running models at scale are becoming a larger consideration, particularly as companies expand the number of AI agents and workloads they operate.
AI infrastructure demand is already reshaping enterprise technology costs. Snowflake Lets Enterprises Set Model Policies Cortex AI Gateway allows customers to define which models they approve and the tradeoffs they want the system to consider.
The gateway then evaluates each task against those policies and available cost and performance data before selecting a model. Snowflake said the system is designed to adapt as models change.
After one model completes a task, another model evaluates the result, creating a feedback loop that can inform future routing decisions.
The company said its internal testing found that dynamic routing delivered up to three times greater token efficiency on some workloads compared with relying on a frontier model alone while maintaining comparable quality.
Snowflake also reported a 25% improvement in token efficiency at the same pull-request throughput in a separate coding evaluation.
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