pgvector resource
Comparison

pgvector vs FAISS, Pinecone, Qdrant, Milvus, and Chroma

pgvector is a PostgreSQL extension, so vectors live in the same database as the rest of your data and are covered by the same transactions, backups, and JOINs. FAISS is an in-process library with no database of its own. Pinecone is a fully managed vector database service. Qdrant and Milvus are dedicated, self-hostable vector databases, and Chroma is an open-source, developer-friendly embedding database. Each is a good fit for different situations.

Short answer: if you already run PostgreSQL and want vector search next to your relational data with normal SQL — no separate service to operate — use pgvector. Choose a dedicated vector database when you do not use Postgres, want a fully managed serverless service, or need engine-specific features at very large scale.

This comparison is structural and factual. It intentionally avoids benchmark numbers and does not declare an overall winner — the right choice depends on your use case. Every claim links to the project's own source.

At a glance

Capability matrix

Structural comparison of vector search options
Dimension pgvector FAISS Pinecone Qdrant Milvus Chroma
Kind PostgreSQL extension In-process library Managed vector DB service Vector database Distributed vector database Embedded vector store
Where vectors live In your Postgres tables In application memory/files you manage In Pinecone's service In a Qdrant deployment In a Milvus cluster In a Chroma instance (in-process or server)
Interface SQL C++ / Python API REST / SDKs REST / gRPC / clients SDKs / REST Python / JS SDK
SQL, JOINs & transactions ✓ full Postgres ✕ ✕ ✕ ✕ ✕
Metadata filtering Any WHERE clause Via ID lists / pre-filtering Metadata filters Rich payload filters Scalar filtering Metadata filters
Index types HNSW, IVFFlat IVF, HNSW, PQ and more Managed (internal) HNSW with quantization HNSW, IVF, FLAT, DiskANN, more Managed (internal)
Self-host ✓ (your Postgres) ✓ ✕ (managed only) ✓ ✓ ✓
Managed option Via hosted Postgres providers Build it yourself ✓ serverless Qdrant Cloud Zilliz Cloud Chroma Cloud
License PostgreSQL License MIT Proprietary Apache-2.0 Apache-2.0 Apache-2.0
Best for Vectors alongside relational data in Postgres Embedding search inside an app; research Managed vector search with no ops Dedicated vector workloads; filtering Very large, distributed vector workloads Python-first embedding storage and prototyping

Sources: pgvector, FAISS, Pinecone, Qdrant, Milvus, Chroma.

In detail

What each option is

FAISS

FAISS is an open-source library for efficient similarity search and clustering of dense vectors, developed by Facebook Research. It gives you index structures (such as IVF, HNSW, and product quantization) that run inside your own process. It is not a database: it does not provide persistence, transactions, or a SQL layer — you bring your own storage and serving.

Choose FAISS when you need a fast in-process index for offline experiments, or you are building vector search directly into an application and are prepared to manage storage and concurrency yourself.

FAISS on GitHub →

Pinecone

Pinecone is a fully managed vector database service. You do not run or tune any infrastructure; you call an API and pay for managed storage and queries. It offers metadata filtering and serverless scaling, but it is proprietary and lives outside your relational database.

Choose Pinecone when you want vector search with no database operations at all, and you are comfortable keeping vectors in a separate managed service rather than alongside your SQL data.

pinecone.io →

Qdrant

Qdrant is a vector similarity search engine and vector database written in Rust and licensed under Apache-2.0. It provides client-server deployment (or an in-process "Edge" mode), a rich payload-filtering system, hybrid search, quantization, and sharding and replication. It is available self-hosted or as the managed Qdrant Cloud.

Choose Qdrant when you want a dedicated vector database with strong filtering and hybrid search, and are happy to operate (or pay for) a service separate from Postgres.

qdrant.tech →

Milvus

Milvus is a high-performance, distributed vector database built for scale, written in Go and C++, and licensed under Apache-2.0 under the LF AI & Data Foundation. It offers standalone and lightweight (Milvus Lite) modes, many index types, GPU acceleration, and multi-tenancy, and is available as a managed service through Zilliz Cloud.

Choose Milvus when you need a highly scalable, distributed vector database and are prepared to run a cluster or use the managed offering.

milvus.io →

Chroma (ChromaDB)

Chroma — widely known as ChromaDB — is an open-source (Apache-2.0) embedding database and vector store for AI applications. It is Python-first (pip install chromadb) with a JavaScript client, runs embedded in your process or as a client–server service, and handles embedding and indexing automatically. A hosted Chroma Cloud is also available.

pgvector vs ChromaDB comes down to "vectors inside your database" versus a Python-first embedding store. Choose Chroma when you are building a Python or JavaScript AI app that needs a simple embedding store and you do not need SQL, JOINs, or your vectors to live in Postgres.

trychroma.com →

Stay in Postgres

Other PostgreSQL vector extensions

If you want to stay in Postgres but need different indexing or compression, these open-source extensions build on or sit alongside pgvector. They share the Postgres data type and SQL interface, so the trade-offs are about index design and licensing rather than moving to a new database.

pgvectorscale

An extension from Timescale that builds on pgvector and adds a diskann index (StreamingDiskANN, inspired by Microsoft's DiskANN research), statistical binary quantization, and label-based filtered search. It is written in Rust with the PGRX framework and requires pgvector.

pgvectorscale on GitHub →

VectorChord

A PostgreSQL extension from TensorChord that depends on pgvector and adds the vchordrq index with RaBitQ compression and automatic re-ranking, plus low-bit vector types. It is dual-licensed under AGPL-3.0 and the Elastic License v2.

VectorChord on GitHub →

Lantern

An open-source PostgreSQL extension providing a lantern_hnsw index built on the usearch HNSW implementation. It is interoperable with pgvector's data type and adds embedding generation helpers and parallel index creation.

Lantern on GitHub →

pgvecto.rs

An earlier Rust-based vector search extension for PostgreSQL from the same team behind VectorChord. VectorChord is its successor; consider pgvecto.rs only for existing deployments.

pgvecto.rs on GitHub →

Honest limits

When pgvector is the wrong choice

pgvector is not always the answer. Consider something else when:

  • You do not use PostgreSQL. pgvector is an extension, not a standalone product. If your stack is not Postgres-based, a library or dedicated database makes more sense.
  • You want zero infrastructure. If you cannot or do not want to operate a database, a fully managed service such as Pinecone removes that burden entirely.
  • You need a distributed vector cluster. While Postgres can be scaled and sharded (for example with Citus or PgDog), dedicated systems like Milvus are designed for horizontal scale out of the box.
  • You need a feature only a dedicated engine has. Evaluate specific engines if you depend on capabilities that pgvector does not provide.
Decision guide

Which should you pick?

Scenario to recommendation
If this describes you…Start with
You already run PostgreSQL and want vectors with SQL, JOINs, and transactionspgvector
You want Postgres but need disk-based indexes or stronger compressionpgvectorscale, VectorChord, or Lantern
You want an in-process index inside your application, with no databaseFAISS
You want fully managed vector search with no operationsPinecone (or Qdrant Cloud / Zilliz Cloud)
You want a dedicated, self-hostable vector database with rich filteringQdrant
You need distributed vector search at very large scaleMilvus
You want a simple, Python-first embedding store without SQLChroma

Ready to try pgvector? Read the documentation and install guide, then generate your index with the index planner. To see language bindings and tooling, visit the ecosystem page.