Heavy AI/ML training shops (Databricks better), single-cloud teams that could just use BigLake or Lake Formation, or buyers who reject credit-based pricing.
Cloud-neutral enterprises (500+ employees) wanting lakehouse semantics in Iceberg without operating a separate engine, with a strong preference for managed SaaS and SQL workloads.
Why we say this
Editorial pulled these weaknesses from Snowflake + Polaris Catalog’s product card in our Top 10 Data Lakehouse Software for 2026:
- ! Credit-based pricing easy to overspend without strict governance
- ! External Iceberg catalogs require careful planning; performance trade-offs vs internal tables
- ! May 2024 customer credential incident still discussed in deals
If Snowflake + Polaris Catalog is wrong for you, consider these instead
Same Data Lakehouse category, different best-fit buyer.
Best for
Mid-market and enterprise data teams (200-50,000 employees) running serious ML training plus analytics, where lakehouse governance and AI workflow integration matter more than pure SQL simplicity.
See full profile →Best for
Engineering-led organizations of any size committing to open-format lakehouse architecture, particularly multi-engine or multi-cloud teams who want to avoid table-format lock-in.
See full profile →Best for
Streaming-first data engineering teams (50-50,000 employees) with heavy CDC, frequent upserts, or real-time ingestion requirements where Hudi incremental processing is differentiating.
See full profile →Related editorial
Last updated 2026-05-27. Editorial verdict based on the published Top 10 Data Lakehouse Software for 2026 ranking. Disagree? Tell us.