Postgres vs. Qdrant: Why Postgres Wins for AI and Vector Workloads

1. pgvector

   1 117 17,259 9.0 C

   Open-source vector similarity search for Postgres

   

   We’re releasing a new benchmark that challenges the assumption that you can only scale with a specialized vector database. We compared Postgres (with pgvector and pgvectorscale) to Qdrant on a massive dataset of 50 million embeddings. The results show that Postgres not only holds its own but also delivers standout throughput and latency, even at production scale.

2. InfluxDB

   www.influxdata.com featured

   InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.

   

3. pgvectorscale

   2 14 2,171 8.2 Rust

   A complement to pgvector for high performance, cost efficient vector search on large workloads.

   

   We’re releasing a new benchmark that challenges the assumption that you can only scale with a specialized vector database. We compared Postgres (with pgvector and pgvectorscale) to Qdrant on a massive dataset of 50 million embeddings. The results show that Postgres not only holds its own but also delivers standout throughput and latency, even at production scale.

4. pgai

   3 19 5,198 9.8 PLpgSQL

   A suite of tools to develop RAG, semantic search, and other AI applications more easily with PostgreSQL

   

   The answer lies inpgvectorscale (part of thepgai family), which implements the StreamingDiskANN index (a disk-based ANN algorithm built for scale) for pgvector. Combined with Statistical Binary Quantization (SBQ), it balances memory usage and performance better than traditional in-memory HNSW (hierarchical navigable small world) implementations.

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