Snowflake is adding native PostgreSQL support to its AI Data Cloud, targeting enterprises that want transactional workloads and analytics under one governance perimeter.
The service, launching after Snowflake's acquisition of Crunchy Data last year, offers full open-source Postgres compatibility. Organizations can migrate existing apps without code changes, according to the vendor.
The integration uses pg_lake extensions to read and write directly to Apache Iceberg tables from PostgreSQL. This eliminates extract-and-load pipelines between transactional and analytical systems.
"With the PostgreSQL service, our goal is to provide this secure boundary where, if customers build apps or build agents within that boundary, their data has not left the compliance and regulatory perimeter," said Christian Kleinerman, Snowflake EVP of product.
The timing matters: Snowflake announced this capability one day after revealing a $200M OpenAI partnership. The company is positioning for AI agents that need continuous access to both live transactional data and analytical insights.
Snowflake already offered Unistore for transactional workloads, announced in 2022 but only reaching general availability in late 2024. Industry observers noted limited adoption. The Postgres approach gives enterprises what they're already familiar with.
"It reflects a broader trend of vendors pairing operational databases with analytics to support real-time and agentic AI workflows," said IDC research director Devin Pratt. Databricks and others are pursuing similar strategies.
Early users include BlueCloud in financial services and Sigma Computing for low-latency workloads.
The real test: Can Snowflake convince enterprises to consolidate database infrastructure when legacy vendors already own those relationships? The platform play makes sense for AI use cases. Whether it triggers actual migrations remains to be seen.
General availability is expected soon. Pricing details weren't disclosed, though organizations should evaluate total cost against maintaining separate OLTP and analytics environments plus the pipelines between them.