Snowflake was twice upgraded to Buy by a leading Wall Street analyst, who highlighted that the company’s valuation has come down from peak levels even as its core business momentum remains intact. The analyst noted that Snowflake’s forward revenue multiple is now closer to the historical peer average, creating an attractive entry point for investors who believe in the long-term shift to cloud data platforms. The upgrade also reflects increasing confidence in Snowflake’s differentiated position within the hybrid and multi-cloud ecosystem, as enterprises seek a unified data layer for analytics, data science and AI workloads.

In its most recent quarterly disclosure, Snowflake reported remaining performance obligations (RPO) of $7.88 billion, a 37% year-over-year increase. This metric—an industry-standard proxy for contracted future revenue—underscores the depth and duration of customer commitments, with multiyear enterprise agreements representing a growing share of the RPO backlog. The acceleration in RPO growth was driven by both net new logo additions, particularly among Fortune 500 customers deploying data mesh architectures, and expansion within the installed base as organizations adopt Snowflake’s advanced services such as Snowpark and Unistore.

Snowflake continues to demonstrate strong revenue resilience through upsells and cross-sell within its existing client base. Net revenue retention stood at 125% in the quarter, indicating that the average dollar of annual recurring revenue from the prior year generated 1.25 dollars in the current year. High retention was fueled by take-up of performance-tiered compute options, data marketplace transactions and early demand for Snowflake’s generative AI accelerator. The company added over 200 customers within its top 1,000 cohort, reflecting deepening relationships with large enterprises across finance, healthcare, retail and technology verticals.

Management reiterated that AI-driven use cases are increasingly central to Snowflake’s product roadmap, pointing to early traction for Cortex AI and embedded model inference capabilities. The platform’s native integration with leading open-source frameworks and its growing catalog of pre-trained models have already led to multiple pilot deployments in recommendation engines, anomaly detection and natural-language generation. Looking ahead, the company plans to invest further in automated model management, real-time feature stores and seamless MLOps workflows, which it expects will unlock new consumption tiers and extend average deal sizes over the next two years.