A vector database stores embeddings and returns the items closest in meaning to a query, which makes it a core part of RAG, semantic search and agent memory. Buyers now choose among purpose-built engines, vector features inside databases they already run and managed cloud services.
Most published comparisons rely on vendor benchmarks that rarely match your data, filters or query mix. The right choice comes from your own corpus, your own filters and a target recall you set before you compare anything.
The real buying question is not which product, but where vector search should sit in your architecture.
Pinecone, Weaviate, Milvus and Zilliz, Qdrant and Chroma focus on vector workloads and expose the most index and tuning options. Pick one when scale, filtering or index choice demand it and a benchmark proves the need.
pgvector, MongoDB Atlas, Elasticsearch, OpenSearch and Redis keep vectors beside existing data. You get SQL joins or document context, one fewer system and mature keyword search, with dimension limits and tuning work at scale.
Azure AI Search, Vertex AI Vector Search, Amazon S3 Vectors, Databricks Vector Search and Snowflake Cortex Search trade tuning control for integration and governance. Start here if your governed data and access controls already live on that platform.
Learn how HNSW, IVF, quantization and DiskANN trade recall, latency and memory, and match the metric to your model.
Combine keyword and vector retrieval, test selective filters and add a reranker where relevance is actually won.
Build a golden query set, sweep index parameters, add filters and soak tests, then compare latency and cost at the same recall.
Price at 24-month scale, verify encryption, ACLs and deletion, and decide with a nine-criterion weighted scorecard.
A database that stores data as embeddings, fixed-length arrays of numbers produced by a machine learning model, and retrieves records by similarity instead of exact match. It pairs an approximate nearest neighbor index with metadata filtering, so applications find the most semantically relevant items in milliseconds.
If you already run PostgreSQL, have under tens of millions of vectors and value SQL joins, start with pgvector. Move to a purpose-built engine only when your own benchmark shows you need its scale, filtering or index options.
Pick a target recall first, often 0.95 at k=10. Build a golden query set on your own data, compute exact ground truth, sweep index parameters, add your real filters and run a soak test, then compare p95 latency, throughput and 24-month cost at that recall.
HNSW is a multi-layer graph with high recall and low latency but high memory use. IVF clusters vectors and searches only the nearest clusters, with faster builds and a smaller footprint. DiskANN keeps a graph index mostly on SSD, enabling billion-scale search on one node with modest RAM.
Hybrid search runs keyword and vector retrieval together and fuses the results. Vector search captures meaning but misses exact tokens such as part numbers and drug names, which keyword search catches. Hybrid plus a reranker usually beats vector-only retrieval.
Azure AI Search suits RAG and semantic search with hybrid retrieval and a semantic ranker. Azure Cosmos DB fits when vectors should live with operational application data, using DiskANN at scale. Azure Database for PostgreSQL with pgvector fits Postgres-centric teams.
Yes. Researchers reconstructed 92% of 32-token inputs exactly from their text embeddings. Vectors built from regulated data carry the same HIPAA, GLBA and privacy obligations as the source and need encryption, access controls and deletion processes.
CTOs, engineering leaders and platform teams choosing where vector search should live for RAG, semantic search and agent memory.
Drop your details and we'll send Vector Database Selection Guide for Semantic Search straight to your inbox - no spam, unsubscribe anytime.
Talk through a side-by-side vector database evaluation on your own corpus with our engineering leads. A working session, not a sales pitch. SECTION 7 - FAQ - 5 to 8 questions
Talk to our engineers