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6 tools scored · 6 dimensions · Last reviewed June 2026
A vector database stores embeddings — numerical representations of text, images, or other data — and finds the most semantically similar ones to a query, the core lookup behind every RAG and AI search application.
Pinecone became the default choice for production RAG applications largely on the strength of its simplicity and reliability at scale, but the open-source field has grown fast: Weaviate and Qdrant both offer self-hosting with strong hybrid search (combining vector similarity with keyword filtering), Chroma optimises for the fastest possible path from prototype to working demo, and Milvus targets billion-scale production deployments where operational complexity is an accepted tradeoff for raw capacity. For teams already on Postgres, pgvector avoids adding a separate database entirely by adding vector search as an extension.
What are you looking for?
I want the most reliable managed option for production RAG
Pinecone remains the most widely adopted managed vector database for production use, prioritising reliability and simplicity over raw self-hosting control.
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I am prototyping an AI app and want the fastest path to "it works"
Chroma is explicitly designed to be the simplest way to add vector search to a project — ideal for early prototyping before you've committed to production architecture.
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I already run Postgres and don't want a separate database
pgvector adds vector similarity search directly inside Postgres as an extension. If you're already on Supabase or Neon, this avoids introducing an entirely new database just for embeddings.
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| Weighted average | Overall | SDK quality, indexing API, and setup speed. | ANN search speed and recall accuracy at scale. | Index size limits and horizontal scaling for billions of vectors. | Cost per million vectors and free tier generosity. | Combining vector similarity with keyword/metadata filtering. | LangChain/LlamaIndex integrations and framework support. | Monthly |
| ★Pinecone The most widely adopted managed vector… | 89 | 94 | 92 | 95 | 65 | 75 | 95 | Free freemium |
| Qdrant Fast, open-source vector search writte… | 88 | 88 | 92 | 88 | 88 | 85 | 82 | Free freemium |
| Weaviate Open-source vector database with built… | 86 | 85 | 86 | 85 | 82 | 92 | 88 | Free freemium |
| Milvus Enterprise-grade open-source vector da… | 82 | 65 | 90 | 98 | 85 | 78 | 75 | Free free |
| Chroma The embedding database built for AI ap… | 79 | 92 | 75 | 60 | 98 | 65 | 85 | Free free |
| pgvector Vector search as a Postgres extension … | 77 | 80 | 72 | 65 | 96 | 88 | 80 | Free free |
Click tool names to see the full radar breakdown · Open screener for advanced filtering
Developer UX
SDK quality, indexing API, and setup speed.
Query Performance
ANN search speed and recall accuracy at scale.
Scalability
Index size limits and horizontal scaling for billions of vectors.
Price / Value
Cost per million vectors and free tier generosity.
Hybrid Search
Combining vector similarity with keyword/metadata filtering.
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If you're already running Postgres and your scale is moderate (up to a few million vectors for most workloads), pgvector is genuinely sufficient and avoids the operational cost of a second database. Dedicated vector databases (Pinecone, Weaviate, Qdrant, Milvus) earn their place at higher scale, when you need specialised indexing algorithms, or when query performance at scale becomes the bottleneck.
Hybrid search combines vector similarity (semantic meaning) with traditional keyword/metadata filtering (exact terms, dates, categories) in a single query. Pure vector search alone often misses exact-match cases — a product SKU or a specific name — that keyword search catches easily. Weaviate and Qdrant both built strong hybrid search specifically to address this gap.
Pinecone trades flexibility for operational simplicity — you don't manage infrastructure, and reliability at scale is proven. Weaviate, Qdrant, Milvus, and Chroma all let you self-host for free (or use their managed cloud tiers), giving more control and avoiding per-vector usage costs, at the expense of taking on more operational responsibility yourself.
Milvus is purpose-built for billion-scale vector search in production at large enterprises, with the operational complexity that implies. Qdrant, Weaviate, and Chroma are easier to run and tune for most teams' actual scale (millions, not billions, of vectors) — most RAG applications never need what Milvus is built for.
Yes — Pinecone, Weaviate, Qdrant, Chroma, and Milvus all have first-class integrations with both LangChain and LlamaIndex, the two most widely used RAG orchestration frameworks. pgvector also has community integrations, though slightly less polished given it's a Postgres extension rather than a purpose-built vector database.
How these scores are calculated
Vector database scores are based on published ANN search benchmarks (recall and queries-per-second), self-hosting versus managed pricing per million vectors, hybrid search capability, and LangChain/LlamaIndex integration depth as of 2026.
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