pgvector vs. Pinecone
Compare pgvector vs. Pinecone for vector search workloads. We want you to choose the most suitable vector database for your use case, even if it’s not us.
As AI technologies evolve, vector similarity search has become essential for powering modern AI applications like retrieval-augmented generation (RAG), semantic search, and recommendation engines. There are various vector search solutions available, including purpose-built vector databases, vector search libraries, and traditional databases with vector search as an add-on. Selecting the right solution is crucial for the success of your AI applications.
pgvector and Pinecone both bring unique strengths to vector search workloads, each with its own capabilities and limitations. The best choice depends on your specific use case and requirements. In the following sections, we’ll compare both databases regarding functionality, scalability, and availability, helping you determine the most suitable option for your needs—even if it’s not us.
pgvector vs. Pinecone at a Glance
No. pgvector is just a vector search add-on to Postgres
Yes. Purpose-built vector database
PostgreSQL License (similar to MIT)
Proprietary License
13,445
N/A
On-prem
Cloud
pgvector overview
pgvector is an extension for PostgreSQL that adds support for vector similarity search directly within the database. It allows developers to store, index, and query vector embeddings alongside relational data. pgvector is ideal for hybrid applications that combine traditional relational queries with vector-based retrieval, leveraging PostgreSQL’s mature ecosystem.
Pinecone overview
Pinecone is a fully managed vector database service designed for high-performance vector search and retrieval. It specializes in scalability, enabling real-time, low-latency similarity searches across massive amounts of vectors. Pinecone’s integration with machine learning workflows and automatic indexing optimization makes it ideal for applications like recommendation systems and semantic search.
Benchmarking pgvector and Pinecone on your own
VectorDBBench is an open-source benchmarking tool designed for users who require high-performance data storage and retrieval systems, particularly vector databases. This tool allows users to test and compare the performance of different vector database systems using their own datasets and determine the most suitable one for their use cases. Using VectorDBBench, users can make informed decisions based on the actual vector database performance rather than relying on marketing claims or anecdotal evidence.
VectorDBBench is written in Python and licensed under the MIT open-source license, meaning anyone can freely use, modify, and distribute it. The tool is actively maintained by a community of developers committed to improving its features and performance.
Check out the VectorDBBench Leaderboard for a quick look at the performance of mainstream vector databases.
Compare pgvector with others
The Definitive Guide to Choosing a Vector Database
Overwhelmed by all the options? Learn key features to look for & how to evaluate with your own data. Choose with confidence.