Vector databases compared: Pinecone vs FAISS vs pgvector

Every RAG system needs the same thing: store a pile of embeddings, and given a query vector, find the nearest ones fast. The moment you go looking for how to do that, you hit a wall of options and a lot of marketing. The three I keep coming back to are Pinecone, FAISS, and pgvector — and they're not really competitors so much as three different answers to "how much infrastructure do you want to own?"

None is best. They occupy different points on a tradeoff curve, and the right pick depends far more on your team and stage than on any benchmark. Here's how I think about each, and a framework for choosing.

Pinecone — the managed service

Pinecone is a hosted vector database. You send it vectors and queries over an API; it handles indexing, sharding, replication, scaling, and uptime. There are no servers for you to run, no index to rebuild by hand, and metadata filtering plus namespaces come built in. When you have hundreds of millions of vectors and don't want a team babysitting infrastructure, that operational relief is the whole value proposition.

The costs are the usual ones for managed services. You pay per month, and the bill scales with your data and query volume — at large scale it can become a real line item. Your vectors live in someone else's cloud, which may matter for compliance. And you're coupled to a vendor's API and pricing. For a lot of teams that's a fair trade: money and lock-in in exchange for never being paged about the vector store.

FAISS — the library

FAISS (from Meta) isn't a database at all — it's a library for similarity search that runs inside your process. It is extremely fast, supports a rich menu of index types (flat, IVF, HNSW, product quantization), and runs on GPU. If raw search performance and control over the index are what you need, nothing here beats it.

But "library, not database" is the whole story. Out of the box there is no persistence— you build the index in memory and it's your job to serialize it and reload it. There is no metadata filtering: FAISS returns nearest vectors by ID, and if you want "nearest documents where tenant = X and published" you build and maintain that layer yourself. No replication, no concurrent writes story, no server. FAISS gives you a fast engine and hands you every other database responsibility. It shines as a component inside a system you're already building, less so as the system.

pgvector — vectors inside Postgres

pgvector is an extension that adds a vector column type and nearest-neighbor search (IVFFlat and HNSW indexes) to PostgreSQL. If your application already runs on Postgres, this is quietly the most pragmatic option on the list. Your embeddings live in the same database as everything else, which means real transactions, real joins, and one system to back up, secure, and operate.

The killer feature is hybrid queries. Because vectors are just another column, you filter and join with plain SQL in the same statement as the similarity search — tenant scoping, date ranges, status flags, metadata, all with the query planner and indexes you already trust. That's the thing FAISS makes you build and Pinecone bolts on; in Postgres it's native.

-- Nearest chunks to the query embedding, but only this tenant's
-- published docs from the last year. One planner, one index strategy.
SELECT id, content, embedding <=> $1 AS distance
FROM chunks
WHERE tenant_id = $2
  AND status = 'published'
  AND created_at > now() - interval '1 year'
ORDER BY embedding <=> $1       -- cosine distance operator
LIMIT 10;

The honest limit is scale. pgvector is excellent into the millions and tens of millions of vectors on a well-tuned instance. Push into the high hundreds of millions with heavy query concurrency and you'll start fighting index build times and memory, and a purpose-built system starts to earn its keep. For the vast majority of RAG projects, you never reach that point.

How to choose

Skip the benchmarks first and answer five questions about your situation. They'll point you at the answer faster than any latency chart.

• Scale. Thousands to tens of millions of vectors? Almost anything works, and pgvector is likely enough. Hundreds of millions with high query volume? A dedicated system like Pinecone starts to pull ahead.
• Ops appetite. Do you have people who can run infrastructure? If not, managed (Pinecone) buys you out of that problem. FAISS demands the most operational work of the three.
• Filtering and hybrid search. Need to combine vector similarity with metadata, tenant scoping, or SQL joins? pgvector does it natively, Pinecone supports it, FAISS makes you build it.
• Cost model. A predictable monthly SaaS bill, or the cost of running your own boxes plus the engineering time to operate them? At small scale self-hosting is cheap; the crossover comes later than people expect.
• Existing stack. Already on Postgres? Adding pgvector is one extension and zero new systems to operate — a huge, easily overlooked advantage.

Where I usually land

My default is pgvector, and it stays my default longer than most people assume. The operational simplicity of one database, plus transactional consistency and SQL-native hybrid search, wins the majority of real projects — because the majority of real projects never approach the scale where it breaks down. I reach for Pinecone when scale or ops constraints make managed genuinely worth the cost, and for FAISS when it's a fast in-process component inside a larger system I'm already building, not the database itself.

The mistake I see most often is picking the tool built for a scale you don't have yet, and paying for that complexity from day one. Start with the simplest thing that fits your actual numbers. Migrating a few million embeddings later is a manageable afternoon; running infrastructure you never needed is a tax you pay every single day.