EDB Postgres AI stands out as a pioneering solution, maintaining intelligence alongside data on a unified sovereign foundation. This innovative approach eliminates the need for ETL processes, data duplication, and separate vector stores. Independent benchmarks reveal that EDB Postgres AI consistently outperforms competing platforms in terms of speed, accuracy, and cost-effectiveness.
Performance Benchmarks
According to a recent study conducted by McKnight Consulting Group, commissioned by EDB, EDB Postgres AI excels in key performance metrics that determine the success of enterprise AI in production environments. These metrics include query latency, accuracy, cost, and data freshness. The findings indicate that EDB Postgres AI outperformed specialized vector databases, lakehouses, document stores, and other managed Postgres platforms across all tested scenarios.
William McKnight, president of McKnight Consulting Group, noted, “Across every workload we tested—raw vector search, hybrid queries combining vectors with structured filters, and full end-to-end agent retrieval—EDB Postgres AI led the field, and did so while spending fewer tokens and dollars to get there.” He emphasized the platform’s consistent performance, particularly in comparison to those that separate storage from search, which experienced significant degradation at scale.
Speed and Accuracy
EDB Postgres AI demonstrated remarkable speed, achieving median query latencies of just 50 milliseconds at a scale of 50 million vectors. This performance is notably faster than competing platforms, including:
- 80x faster than Databricks
- 21x faster than MongoDB Atlas
- Up to 2x faster than the nearest managed Postgres alternatives, such as Aurora and Crunchy Bridge
In terms of accuracy, EDB Postgres AI delivered the highest recall rates for core vector searches, outperforming competitors like MongoDB and Databricks by significant margins. This level of accuracy is critical for autonomous agents, as incorrect data retrieval can lead to erroneous actions without human oversight.
End-to-End Agentic Retrieval
In a complex test involving concurrent retrievals, EDB Postgres AI completed a three-arm agent loop in an impressive 27 milliseconds. This efficiency starkly contrasts with the performance of other platforms, which took hundreds of milliseconds or longer. The architecture of EDB Postgres AI allows for seamless integration of vector, filtered, and full-text retrieval in a single query path, eliminating the need for separate vector stores and ensuring real-time data access.
Cost Efficiency
When evaluating price performance, EDB Postgres AI emerged as the most economical option among the tested platforms. It achieved:
- 76x better price performance than Databricks
- 34x better than MongoDB
- 23%–28% better price performance than the nearest managed Postgres competitors when factoring in query speed
A Unified Architecture
Max Romanenko, chief engineering officer at EDB, highlighted the significance of EDB Postgres AI’s architectural design: “These results come from a single architectural choice: We run vectors, structured data, and analytics together, in the operational database, on the live data.” This integrated approach eliminates the complexities and inefficiencies associated with maintaining separate systems, ensuring that intelligence and data coexist harmoniously.
EDB Postgres AI represents a transformative step for enterprises seeking to operationalize their data and AI capabilities, providing a robust foundation for transactions, analytics, and AI workloads. With its proven performance and cost advantages, it positions itself as a leader in the evolving landscape of enterprise AI solutions.
For further insights and to access the complete benchmark report, visit EDB’s benchmark report.
