Vector Database Directory
Compare 12+ vector databases for AI applications. Find the right vector store for RAG, semantic search, and recommendation systems based on performance, pricing, and deployment.
Pinecone
Free tier / Standard from $70/mo / Enterprise customFully managed, serverless vector database designed for simplicity and production AI workloads.
Weaviate
Free (self-hosted) / Weaviate Cloud from $25/moOpen-source vector database with built-in vectorisation modules and hybrid search capabilities.
Qdrant
Free (self-hosted) / Qdrant Cloud from $25/moHigh-performance open-source vector similarity search engine built in Rust for speed and reliability.
ChromaDB
Free (open source)Lightweight, developer-friendly open-source embedding database designed for prototyping and small-scale AI apps.
PgVector
Free (PostgreSQL extension)PostgreSQL extension adding vector similarity search to your existing Postgres database.
Milvus
Free (self-hosted) / Zilliz Cloud from $65/moScalable open-source vector database designed for billion-scale similarity search workloads.
Zilliz Cloud
Free tier / Standard from $65/mo / Enterprise customManaged cloud version of Milvus with enterprise features, auto-scaling, and global deployment.
Elasticsearch (Vector Search)
Free (self-hosted) / Elastic Cloud from $95/moVector search capabilities added to Elasticsearch for combined full-text and semantic search.
Redis Vector Search
Free (Redis Stack) / Redis Cloud from $7/moVector search module for Redis, adding similarity search to the popular in-memory data store.
Supabase Vector
Free / Pro $25/mo / Team $599/moVector search powered by PgVector within the Supabase platform for full-stack AI applications.
Marqo
Free (self-hosted) / Marqo Cloud availableEnd-to-end vector search engine with built-in ML models for automatic vectorisation of text and images.
LanceDB
Free (open source) / LanceDB Cloud availableServerless, embedded vector database built on Lance columnar format for efficient storage and retrieval.
Guide
How to choose
The right vector database depends on your scale, infrastructure preferences, and team expertise. For teams starting out or building prototypes, ChromaDB and LanceDB offer the simplest setup — they can run embedded within your application without separate infrastructure. PgVector is ideal if you already use PostgreSQL and want to add vector search without introducing a new database. For production workloads, the key decision is managed vs self-hosted. Managed options like Pinecone and Zilliz Cloud handle scaling, backups, and infrastructure, letting your team focus on application logic. Self-hosted options like Weaviate, Qdrant, and Milvus give you full control over data and infrastructure but require DevOps expertise. Consider your data sovereignty requirements — some industries and regions require self-hosted or specific cloud region deployments. Performance characteristics matter at scale. Qdrant (built in Rust) and Milvus (GPU-accelerated) excel at high-throughput workloads. Weaviate and Elasticsearch shine when you need hybrid search combining vector similarity with keyword/filter queries. For cost optimisation, evaluate whether you need real-time search or can use batch processing, as this significantly affects infrastructure costs.
FAQ
Frequently asked questions
A vector database stores and retrieves data as high-dimensional vectors (embeddings) rather than traditional rows and columns. This enables semantic similarity search — finding items by meaning rather than exact keywords. They are essential for RAG systems, recommendation engines, and semantic search.
Not necessarily. If you already use PostgreSQL, PgVector adds vector search without a new system. Elasticsearch and Redis also offer vector capabilities as extensions. A dedicated vector database (Pinecone, Qdrant, Milvus) makes sense when vector search is your primary workload or you need high-scale performance.
Performance depends on dataset size, query patterns, and hardware. At small scale (<1M vectors), most options are fast enough. At large scale, Milvus (with GPU acceleration), Qdrant (Rust performance), and Pinecone (optimised cloud) lead benchmarks. Always test with your specific workload.
Open-source options are free for self-hosting (plus infrastructure costs). Managed services start from free tiers for prototyping, with production pricing ranging from $25-$500/month depending on data volume and query throughput. Enterprise deployments can range into thousands per month.
Yes — vector databases are the standard storage layer for RAG (Retrieval-Augmented Generation) systems. They store document embeddings and enable fast semantic retrieval of relevant context to include in LLM prompts. Most vector databases integrate well with RAG frameworks like LangChain and LlamaIndex.
Need help picking the right tool?
Our team can help you evaluate options and implement the best solution. Book a free strategy call.