The market has settled — mostly
Two years ago, the vector database landscape was a confusing mess of new entrants, overlapping claims, and unclear differentiation. In 2026, the market has consolidated into a handful of credible options, each with distinct strengths.
This comparison is based on our experience deploying RAG systems across different scales and requirements. It's opinionated — because unhelpful comparisons are the ones that refuse to take a position.
The main contenders
Pinecone
Best for: Teams that want a fully managed service and are willing to pay for it.
Pinecone's managed offering is the most polished in the market. The developer experience is excellent, scaling is handled automatically, and the query performance is consistently fast. The trade-off is cost: at scale, Pinecone is one of the more expensive options, and you're fully dependent on their infrastructure.
Use it when operational simplicity is your top priority and you're comfortable with vendor lock-in.
Qdrant
Best for: Teams that want strong performance with self-hosting options.
Qdrant offers a good balance between performance and flexibility. It can be self-hosted or used as a managed service, supports filtering natively (which matters for real-world RAG where you need to combine vector similarity with metadata filters), and has an active open-source community.
Weaviate
Best for: Teams building complex search applications that combine vector and keyword search.
Weaviate's hybrid search capabilities — combining dense vector retrieval with BM25 keyword search — are its strongest differentiator. For applications where some queries are best served by semantic similarity and others by keyword matching, Weaviate handles both without requiring two separate systems.
Milvus
Best for: Teams operating at very large scale (100M+ vectors) who can invest in ops.
Milvus handles massive datasets well and offers fine-grained control over index types and search parameters. The trade-off is operational complexity: running Milvus at scale requires meaningful infrastructure expertise.
pgvector (PostgreSQL extension)
Best for: Teams already using PostgreSQL who want to avoid adding another database to their stack.
pgvector lets you add vector search to your existing PostgreSQL database. Performance has improved significantly with recent versions, and for datasets under ~5M vectors, it's often good enough. The major advantage is operational: one less database to manage.
What actually matters in production
Query latency at your scale
Benchmark numbers from vendors are measured under ideal conditions. What matters is latency at your data volume, with your filtering requirements, under your concurrent query load. Always run your own benchmarks.
Filtering performance
Real RAG queries almost always include metadata filters (date ranges, document types, user permissions). Some vector databases handle filtering before vector search (pre-filter), while others do it after (post-filter). Pre-filtering is more efficient but not all systems support it well.
Operational complexity
A faster database that requires a dedicated team to operate costs more than a slightly slower one that runs itself. Factor in the ops burden when comparing options.
Cost at scale
Vector databases cost money: compute for search, storage for vectors, memory for indexes. At 10M+ vectors, the cost differences between providers become significant. Model your expected growth before choosing.
The build-from-scratch option
For teams with specific requirements, building a vector search layer on top of existing tools (FAISS + Redis, for example) is viable. This gives maximum flexibility but requires more engineering effort. It's worth considering if your search requirements are unusual or if you need performance that dedicated databases don't provide.
Our recommendation
For most teams in 2026: start with pgvector if you already use PostgreSQL and your dataset is under 5M vectors. Graduate to Qdrant or Pinecone when you need better performance, more advanced features, or larger scale. Use Milvus if — and only if — you're operating at a scale that justifies the operational investment.
The choice matters less than you think. All the major options are good enough for most production use cases. What matters much more is your embedding quality, your chunking strategy, and your retrieval pipeline design — the vector database is just the storage layer underneath.