EnterpriseDB (EDB) has released benchmark results for its EDB Postgres AI (EDB PG AI), showing superior performance compared to various AI data platforms, including vector databases and managed Postgres platforms. The independent testing by McKnight Consulting Group indicated that EDB PG AI outperformed competitors in all evaluated metrics, including query speed, accuracy, and cost efficiency.
Key findings include:
- EDB PG AI achieved a median query latency of 50 milliseconds at 50 million vectors, making it the fastest platform tested, 80 times faster than Databricks and 21 times faster than MongoDB Atlas.
- It demonstrated the highest accuracy for core vector search with a recall rate of 0.911, outperforming MongoDB by 26% and Databricks by 17%.
- In a concurrent agent loop test, EDB PG AI completed tasks in 27 milliseconds, significantly faster than MongoDB Atlas (699 milliseconds) and Databricks (5.1 seconds).
- EDB PG AI provided 76 times better price performance than Databricks and 34 times better than MongoDB.
- The platform integrates vectors, structured data, and analytics within a single operational database, enhancing efficiency and reducing the risk of data inconsistency.
The benchmark study assessed the performance of EDB PG AI against Amazon Aurora PostgreSQL, Databricks, Crunchy Bridge, MongoDB Atlas, and open source PostgreSQL, all standardized on equivalent enterprise hardware.
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EDB Postgres AI is a pioneering solution that integrates intelligence and data on a unified sovereign foundation, eliminating the need for ETL processes, data duplication, and separate vector stores. Independent benchmarks show that EDB Postgres AI outperforms competing platforms in speed, accuracy, and cost-effectiveness. A study by McKnight Consulting Group indicates that EDB Postgres AI excels in key performance metrics, including query latency, accuracy, cost, and data freshness, outperforming specialized vector databases and other managed Postgres platforms.
EDB Postgres AI achieves median query latencies of 50 milliseconds at a scale of 50 million vectors, making it 80x faster than Databricks, 21x faster than MongoDB Atlas, and up to 2x faster than alternatives like Aurora and Crunchy Bridge. It also delivers the highest recall rates for core vector searches, surpassing competitors like MongoDB and Databricks. In tests involving concurrent retrievals, EDB Postgres AI completed a three-arm agent loop in 27 milliseconds, significantly faster than other platforms.
In terms of cost efficiency, EDB Postgres AI offers 76x better price performance than Databricks, 34x better than MongoDB, and 23%–28% better than the nearest managed Postgres competitors when considering query speed. The architecture of EDB Postgres AI allows for the integration of vector, filtered, and full-text retrieval in a single query path, ensuring real-time data access and eliminating the need for separate systems.
