AI Systems·9 min read

Vector Databases Compared: Pinecone, Weaviate, pgvector

The three vector database options most teams actually consider, compared on the dimensions that matter for production AI systems.

FA
Flowtix Team
July 15, 2026

The Three Real Options

There are 20+ vector databases. Three are what serious teams actually pick between in 2026: pgvector (a Postgres extension), Pinecone (managed, purpose-built), and Weaviate (open-source, self-hostable or managed).

pgvector

A Postgres extension that adds vector similarity to a regular relational database. The pitch: one database for everything.

Strengths: No new infrastructure. SQL queries can mix vector and metadata filters. Cheap. Transactional consistency.

Weaknesses: Performance ceilings at very large scale (tens of millions of vectors). Less specialized indexing than purpose-built tools.

Right when:You're already on Postgres and have under ~10M vectors. Most SMB and mid-market deployments.

Pinecone

Managed vector database, purpose-built. The pitch: don't think about infrastructure.

Strengths: Excellent performance at scale. Polished developer experience. Mature.

Weaknesses: Cost grows quickly with scale. Separate infrastructure to manage and authenticate. Vendor lock-in.

Right when:You're at very large scale or want to minimize ops burden, and the cost trade-off works.

Weaviate

Open-source vector database with managed and self-hosted options. The pitch: flexibility without lock-in.

Strengths: Powerful hybrid search built-in. Multi-modal support. Open source.

Weaknesses: More configuration than Pinecone. Self-hosting adds ops overhead.

Right when: You want managed simplicity with the option to self-host. Strong hybrid search needs.

Decision Heuristic
  • • Already on Postgres + under 10M vectors → pgvector.
  • • Want zero infrastructure thought → Pinecone.
  • • Want flexibility + strong hybrid search → Weaviate.

Decision Criteria

  • Scale — how many vectors, how many queries/sec?
  • Latency budget — p99 target?
  • Hybrid search — need keyword + semantic?
  • Existing stack — already on Postgres?
  • Cost sensitivity — what's the budget at year 2?
  • Ops capacity — can you run infrastructure?

Cost At Scale

At 1M vectors: pgvector ~$50/month on a small Postgres instance, Pinecone ~$70/month, Weaviate ~$50/month self-hosted or $200/month managed. At 50M vectors: pgvector $300–$500, Pinecone $1.5–$3k, Weaviate $300–$1.5k. Exact numbers vary; do your own math.

Migration Between Them

Wrap your vector layer behind a thin interface. Switching between any of the three is then a few days of work, not a rewrite. Avoid using provider-specific SQL extensions or query features.

For 80% of teams shipping RAG today, pgvector is the right answer. The other 20% know exactly why they need Pinecone or Weaviate — usually scale or a specific feature.

See RAG done right.

FAQ

What about Chroma, Milvus, Qdrant? All capable. We see less production adoption than the three above.

Cloud-vendor vector DBs? AWS, GCP, Azure all have offerings. Fine but more lock-in than self-hosted Weaviate.

Should we shard? Rarely below 100M vectors.

Tags:Vector DatabaseRAGInfrastructure
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Flowtix Team

Flowtix is a design-first studio building AI systems, automations, and digital products for businesses that refuse to look average.