EDB Postgres AI Outpaces Rivals in Agentic AI Benchmarks
Event summary
- EDB Postgres AI outperformed competitors in speed, accuracy, and cost for agentic AI workloads according to independent benchmarks by McKnight Consulting Group.
- The platform achieved 50ms median query latency at 50 million vectors, 80x faster than Databricks and 21x faster than MongoDB Atlas.
- EDB Postgres AI delivered the highest accuracy in core vector search with a Recall@10 of 0.911, 26% higher than MongoDB and 17% higher than Databricks.
- The platform completed end-to-end agentic retrieval in 27 milliseconds, significantly faster than competitors like MongoDB Atlas (699ms) and Databricks (5.1 seconds).
- EDB Postgres AI demonstrated 76x better price performance than Databricks and 34x better than MongoDB.
The big picture
As agentic AI becomes a cornerstone of enterprise IT spending, the need for unified data and AI platforms is growing. EDB's benchmark results highlight the strategic advantage of integrating intelligence directly within operational databases, eliminating the inefficiencies of separate vector stores or lakehouses. This shift towards sovereign data architectures aligns with broader industry trends towards data governance and operational efficiency.
What we're watching
- Market Adoption
- Whether enterprises will migrate from specialized vector databases and lakehouses to unified platforms like EDB Postgres AI.
- Competitive Response
- How competitors such as Databricks and MongoDB will react to EDB's benchmark results and performance claims.
- Technological Integration
- The pace at which EDB can integrate additional AI capabilities into its Postgres foundation to maintain its competitive edge.