Snowflake today announced dynamic model routing within Cortex AI Gateway, designed to help enterprises reduce unnecessary AI spend and improve what Snowflake calls intelligence efficiency, measuring how effectively companies turn compute, models, data, and context into business impact.
The new capabilities build on Cortex AI Gateway, which Snowflake announced in July 2026 as a unified foundation for governing agent connections, intelligently routing requests, and optimizing AI consumption. As companies deploy more AI apps and agents into production, using the same model for every task can drive up costs, while evaluating and managing a growing mix of models creates more work for development teams. Snowflake is addressing both challenges by making model selection more automatic and giving customers access to a broader range of open source and proprietary models.
Cortex AI Gateway with dynamic routing can automatically select the specific model with the optimal balance of quality and cost for the task at hand. Dynamic model routing is also integrated across Snowflake’s flagship AI products, including Snowflake CoCo and Snowflake CoWork, and is available to third-party AI agents using Cortex AI Gateway. This new capability directs lower-complexity or repetitive tasks to more efficient models, while work that requires deeper reasoning is routed to frontier models. This helps customers reduce unnecessary inference spend, without having to manage model selection for each request themselves. Snowflake will also be expanding customers’ access to leading open models, including DeepSeek-V4-Flash 0731 and GLM-5.3, through Snowflake Cortex AI. Together with models from providers including Anthropic, OpenAI, Google, SpaceXAI, Meta, and Mistral, this portfolio gives customers more freedom to choose the right combination of performance and cost for each workload.
“Enterprises are becoming much more rigorous about the economics of AI. The question is no longer how much AI they are using, but whether that AI is translating into meaningful business value,” said Sridhar Ramaswamy, CEO, Snowflake. “Achieving intelligence efficiency requires the flexibility to use the best model for each task as the landscape evolves. Snowflake’s role is to absorb that complexity so customers can focus on outcomes while we optimize model choice underneath.”
Snowflake’s internal testing indicates that using a mix of open and proprietary models for various tasks can deliver comparable quality, while materially improving token efficiency. In one evaluation, agents using dynamic model routing with Cortex AI Gateway built a dbt pipeline with up to 3x greater token efficiency than a frontier-model-only path, while maintaining the same quality. In a separate test, engineering teams completed the same number of pull-requests with 25 percent greater token efficiency.
