Snowflake sharpens AI and migration playbook while a major outage raises resilience questions

Over the past 18 months Snowflake has shifted from a focus on data warehousing toward a fuller AI Data Cloud that bundles developer tooling, migration automation, metadata discovery and observability alongside storage and query. Product launches in 2025–2026 added generative SQL functions, multi‑modal ingestion, Spark execution on Snowflake infrastructure, and inference-oriented capabilities intended to let teams prepare, query and operationalize models where data already lives. The company has also been acquisitive, bringing in providers focused on metadata, migration automation, Postgres compatibility and observability to shorten migration paths and reduce developer friction. Complementing those buys, Snowflake struck a multi‑year commercial agreement — reported at roughly $200 million — to make OpenAI models available to customers across the three major public clouds and to provide Snowflake staff with ChatGPT Enterprise; executives frame this and similar pacts as part of a model‑agnostic strategy that packages multiple external model providers so customers can choose models without moving governed data. Snowflake also introduced Cortex Code, a coding assistant that injects awareness of sensitivity labels, transformation cost and production links into developer IDEs and managed notebooks; the feature is being shipped first as a CLI for local editors with web console integration planned. Those model and tooling moves are designed to accelerate pilots and shorten time‑to‑production by keeping data and compute inside the platform, but they also layer contract complexity, token governance and billing considerations onto procurement and security workflows. The company’s December 2025 outage — a prolonged disruption in multiple regions — highlighted the operational risk of expanding the platform’s surface area with inference services and orchestration layers. Competitors have rapidly introduced similar SQL‑centric AI capabilities, turning a feature race into a platform contest where integration quality, runbook maturity and SLA clarity matter as much as raw model performance. Financial terms for most acquisitions remain undisclosed, leaving near‑term earnings effects unclear even as product breadth grows. For enterprise buyers, the promise is simpler stacks and faster AI projects; the trade‑off is greater reliance on a single vendor’s availability and a more complex contract ecosystem when multiple model providers are bundled. The coming quarters will test whether Snowflake can knit together acquired codebases, commercial partnerships and new services into a coherent, reliable offering that measurably reduces total cost of ownership and increases customer velocity.