Snowflake Inc. (NYSE: SNOW) shares rose 5.08% to $331.39 on Thursday, lifting off Salesforce’s stronger-than-expected second-quarter results and raised full-year guidance.

Salesforce Results Drive Sector Sentiment

Salesforce Inc. (NYSE: CRM) reported Q2 revenue of $11.35 billion, beating the consensus estimate of $11.32 billion. This marks an 11% year-over-year increase. Adjusted earnings per share landed at $5.90, significantly exceeding the consensus call of $3.27.

Management also upgraded its full fiscal-year targets. The revenue range was raised to $46.1 billion to $46.4 billion, up from the prior window of $45.9 billion to $46.2 billion. Adjusted earnings guidance jumped to a new range of $16.67 to $16.71 per share, compared to the previous outlook of $14.06 to $14.12.

For the third quarter, Salesforce projected revenue between $11.42 billion and $11.50 billion, edging out the Street’s projection of $11.41 billion. Adjusted profit guidance for the quarter stands at $3.42 to $3.44 per share, ahead of the $3.37 consensus.

The strong performance from Salesforce is spilling over into Snowflake as both companies operate in the enterprise software sector. Investors view Salesforce’s beat and AI-driven growth as a signal of healthy enterprise technology spending, which benefits Snowflake’s data and AI-related business.

Technical Setup and Peer Performance

Snowflake shares are trading above their 20-day, 50-day, 100-day, and 200-day moving averages. A golden cross formed in July, reinforcing the longer-term uptrend. However, near-term momentum appears mixed, with the MACD line below its signal line and a negative histogram, suggesting some loss of steam in the recent upward push.

Traders are watching $342 as resistance, near the stock’s 52-week high of $341.95. Snowflake is outperforming its peers, gaining 5.12% versus a 2.58% gain for the Technology Select Sector SPDR Fund. The technology sector itself has climbed 9.62% over the past 30 days but remains down 1.82% over the past 90 days.

What the Numbers Show

The divergence between Salesforce’s reported adjusted EPS of $5.90 and the consensus estimate of $3.27 highlights a significant market underestimation of profitability in the enterprise software segment. This substantial beat, combined with raised full-year guidance, suggests that operational efficiencies or higher-margin revenue streams may be driving value beyond top-line growth alone.

Snowflake (NYSE: SNOW) introduced dynamic model routing within its Cortex AI Gateway, a capability designed to automatically select the most efficient AI model for specific enterprise tasks based on quality, speed, and cost parameters. The update aims to reduce unnecessary inference spend by routing lower-complexity workloads to more efficient models while reserving frontier models for tasks requiring deeper reasoning.

The new functionality is integrated across Snowflake’s flagship AI products, including Snowflake CoCo and Snowflake CoWork, and is available to third-party AI agents using the gateway. By automating model selection, Snowflake intends to remove the operational overhead associated with managing a growing mix of open-source and proprietary models at scale.

Efficiency Gains in Internal Testing

Snowflake’s internal testing suggests that mixing open and proprietary models can materially improve token efficiency without compromising output quality. In one evaluation involving the construction of a dbt pipeline, agents utilizing dynamic model routing achieved up to 3x greater token efficiency than a path relying solely on frontier models, while maintaining identical quality standards.

In a separate test focused on engineering workflows, teams completed the same volume of pull requests with 25 percent greater token efficiency when using the dynamic routing feature. These results highlight the potential for enterprises to optimize compute resources as they deploy more AI applications into production.

Expanded Model Portfolio

Alongside the routing update, Snowflake will add DeepSeek-V4-Flash 0731 and GLM-5.3 to its Snowflake Cortex AI library. This expansion adds to an existing portfolio that includes models from Anthropic, OpenAI, Google, SpaceXAI, Meta, and Mistral.

Internal evaluations by Snowflake’s AI Research Team indicated that DeepSeek-V4-Flash scored 74.4 percent on data engineering tasks, outperforming leading proprietary models in that specific benchmark. GLM-5.2 also demonstrated strong performance at 62.8 percent, while consuming fewer tokens than any other model tested in the evaluation.

What the Numbers Show

The divergence between the two internal testing scenarios reveals distinct efficiency opportunities across different workload types. While the dbt pipeline task saw a 3x improvement in token efficiency, the engineering pull-request scenario showed a 25 percent gain. This suggests that the economic benefit of dynamic model routing varies significantly by use case, with complex data engineering pipelines potentially offering higher leverage for cost optimization than standard software development tasks.

Governance and Control

Cortex AI Gateway provides administrators with visibility into token usage and costs, allowing organizations to set spending limits across AI apps and agents. Snowflake CoCo extends these controls through role-based access and tagging frameworks, enabling administrators to attribute usage to specific teams or cost centers and establish per-user quotas.

Sridhar Ramaswamy, CEO of Snowflake, stated that enterprises are becoming more rigorous about AI economics, focusing on whether AI translates into meaningful business value. The company positions these innovations as a way to absorb the complexity of model choice, allowing customers to focus on outcomes while Snowflake optimizes the underlying infrastructure.