A scaled consumption platform with credible upside from paid trust layers and AI workloads running inside enterprise governance. The bar is defending differentiation as cloud vendors bundle analytics and AI.

Snowflake can compound as the governed, multi-cloud AI data plane: keep core consumption growing, add Postgres + AI workloads close to sensitive data, and monetize trust (provenance/attestation) so enterprise AI runs “inside the guardrails,” not in scattered point tools.

AI makes cognition cheap, but trusted data, governance, and distribution become scarce; Snowflake is well-placed as the execution layer where regulated enterprises run AI on their data.

Snowflake is already scaling core platform consumption at a strong clip and is expanding its monetization surface into AI features that must run where governed enterprise data lives. The non-linear upside is turning governance into a paid “trust layer” (provenance, audit, policy) and widening workloads with Postgres plus adjacent developer/ops use-cases, while remaining multi-cloud neutral. Even assuming valuation compresses as it matures, the business can still earn a durable premium if AI usage becomes repeatable, metered production spend rather than pilots.

The dominant risk is commoditization-by-bundling: hyperscalers (and Databricks) can reduce the effective price of data + AI workflows. The second risk is AI workload economics (GPU cost, utilization, and metering) undermining margins or forcing discounting. Finally, Snowflake’s expanding surface area (Postgres, observability, agent experiences) raises execution and focus risk.

Strengthened: Crunchy Data is now reflected as a completed business combination in filings, making the Postgres expansion more concrete vs prior optionality.

$276.16