Pgvector vs. Pinecone: Vector Database Performance and Cost Comparison

Pgvector and Pinecone are two popular vector databases used for AI applications. While Pinecone is a proprietary managed vector database designed specifically for vector workloads, PostgreSQL with the pgvector extension is an open-source general-purpose relational database that supports vector storage and search. Pgvectorscale is a new open-source extension for PostgreSQL that enhances its performance and scalability for large-scale vector workloads. It introduces specialized data structures and algorithms, including StreamingDiskANN, a purpose-built index for high-performance and cost-efficient scalability, and Statistical Binary Quantization, which improves upon standard binary quantization techniques to reduce the space needed for vector storage without sacrificing accuracy. In benchmark tests comparing Pinecone with PostgreSQL using pgvector and pgvectorscale on a dataset of 50 million Cohere embeddings, PostgreSQL achieved significantly lower latency and higher query throughput at a lower cost compared to Pinecone's storage-optimized index (s1) and performance-optimized index (p2). Additionally, PostgreSQL offers several operational advantages over Pinecone, such as rich support for backups, point-in-time recovery, high availability, flexibility and control, and better observability and debugging tools. In conclusion, developers can use the open-source general-purpose PostgreSQL database with extensions like pgvector and pgvectorscale to achieve comparable or superior performance to specialized vector databases like Pinecone for large-scale vector workloads common in production AI applications.