Build AI Applications on PostgreSQL, faster and cheaper than specialized vector databases
Cost Benchmarks
Building AI applications starts with choosing the right database—one that balances performance, scalability, and cost without adding unnecessary complexity. Self-hosting PostgreSQL with pgvector and pgvectorscale is 75-79% cheaperthan Pinecone, costing $835/month on AWS EC2 versus Pinecone’s $3,241-$3,889/month.
With PostgreSQL’s advancements, you can build scalable, high-performance AI applications without the complexity and cost of specialized vector databases.
Pgvector powers RAG, search, and agents apps at companies big and small the world over:
Performance Benchmarks
In a recent benchmark comparing PostgreSQL with pgvector and pgvectorscale against Pinecone on a dataset of 50 million Cohere embeddings. PostgreSQL with pgvector and pgvectorscale achieved a 28x lower p95 latency and 16x higher query throughput for approximate nearest neighbor queries at 99% recall, all at 25% of the monthly cost.
Building AI Applications in 2025
Pinecone and PostgreSQL with the pgvector extension are two of the most popular vector databases to use when developing AI applications. Pinecone is a proprietary managed vector database, specifically designed for vector workloads. Then there’s PostgreSQL, the popular and robust general-purpose relational database with the pgvector extension, which adds support for vector storage and search.
Meet pgvectorscale
pgvectorscale is a ground-breaking open-source extension that takes PostgreSQL to new heights, enhancing performance and scalability for large-scale AI use cases. Pgvectorscale enhances the search performance of approximate nearest neighbor (ANN) queries, essential for leveraging vector embedding methods in semantic search applications like RAG, summarization, clustering, and search.
Most developers want to use a mature database with advanced production-necessary features for high availability, streaming replication, point-in-time recovery, and observability. By combining the power of pgvector with pgvectorscale, engineers building AI applications can use PostgreSQL, a database they trust and is already in their stack. And when put to the test against specialized vector databases like Pinecone, PostgreSQL with pgvector and pgvectorscale exceeds performance standards at a fraction of the cost.
