Vector Databases: Directory & Guide

The best vector databases support embedding storage, similarity search, and retrieval-augmented generation (RAG), but the right choice depends on the surrounding data architecture. This category spans two product types: vector databases designed around vector retrieval, and search engines that combine keyword search with vector capabilities. We recommend evaluating them by the retrieval patterns, operational model, latency needs, and filtering requirements your application actually has.

How to Choose

Start with product type. A vector database is the better fit when embeddings and similarity retrieval are central to the application: pgvector brings vector search into PostgreSQL, Qdrant provides dense and sparse hybrid search, Milvus is designed for distributed similarity search, and Pinecone provides a managed API for large-scale production retrieval. Choose this type for RAG, recommendations, semantic search, and applications where vector indexes are a primary system concern.

A search engine is better when the user-facing experience needs conventional search features alongside embeddings. Typesense combines full-text search and vector search with typo tolerance, faceting, geo-search, and tunable ranking. It is a sensible choice for search-as-you-type interfaces where keyword matching, merchandising, or grouping matter as much as semantic retrieval.

Then assess these requirements:

• Existing data platform. pgvector uses familiar SQL syntax inside PostgreSQL and supports IVFFlat and HNSW indexes. It is a practical option when the embeddings belong beside relational application data and a separate vector service would add operational overhead. MongoDB Atlas Vector Search similarly stores operational data and embeddings together, while supporting $vectorSearch aggregation pipelines.

• Filtering and hybrid retrieval. Qdrant combines dense and sparse search with one-stage filtering and multivector support. MongoDB Atlas Vector Search supports metadata filters, lexical search, graph lookups, geospatial search, and aggregation pipelines. Select based on the filtering model your application needs rather than treating vector similarity as an isolated query.

• Scale and deployment model. Milvus has a distributed architecture, metadata filtering, hybrid search, and support for tens of billions of vectors. Pinecone offers real-time indexing, tiered storage, access controls, and a usage-based managed service. The former fits teams that need self-hosted, production-scale infrastructure; the latter fits teams prioritizing managed operation.

• Latency and real-time data. Redis Vector Search uses an in-memory architecture for low-latency queries and supports HNSW and FLAT indexing. It is appropriate where vector retrieval must work alongside Redis data structures and hybrid exact-match queries.

• Embedding generation and experience design. Marqo generates vectors on demand with built-in ML models, supports text, images, and multimodal search, and uses click-stream, purchase, and event data for personalized search experiences. That is a different requirement from simply storing pre-computed embeddings.

Top Tools

Marqo

Marqo is a vector database and open-source tensor search engine that combines vector generation and search through one API. It generates vectors on the fly with built-in ML models, supports text, image, and multimodal search, and includes automatic model management. Its focus is personalized search driven by click-stream, purchase, and event data.

Best suited for: Enterprise retail and e-commerce teams using first-party behavioural data to personalize search and improve conversion.
Pricing: Enterprise
The main trade-off is that it is a weaker fit for teams seeking a simple, general-purpose vector database. Compared with Milvus, Marqo distinguishes itself through built-in vector generation and personalized search journeys.

Typesense

Typesense is a search engine that combines traditional full-text search with vector search. It provides typo tolerance, faceting, geo-search, tunable ranking, dynamic sorting, grouping, distinct results, and multi-tenant API keys. Its emphasis is fast search-as-you-type experiences with semantic retrieval available in the same engine.

Best suited for: Small teams building typo-tolerant instant search or lightweight vector search where ease of use matters.
Pricing: Freemium from $21.60/mo
The main trade-off is that it is a weaker choice for organizations needing proven enterprise-scale vector-database capabilities. Compared with Qdrant, Typesense is differentiated by its search-as-you-type and full-text-search orientation.

pgvector

pgvector is an open-source vector database capability delivered as a PostgreSQL extension for embedding storage and similarity search. It supports exact and approximate nearest-neighbor search, cosine similarity, Euclidean distance, inner product, fixed-dimension vectors, and IVFFlat or HNSW indexing. Its defining advantage is native PostgreSQL integration with SQL syntax.

Best suited for: Teams already running PostgreSQL that want open-source vector search without operating a separate vector database.
Pricing: Open Source
The main trade-off is that it is best for small teams or cost-sensitive workloads that value SQL and operational simplicity. Compared with Qdrant, pgvector keeps vector data in PostgreSQL instead of introducing a dedicated vector search service.

Qdrant

Qdrant is an open-source vector database written in Rust for scalable similarity search. It provides native hybrid search across dense and sparse vectors, built-in multivector support, efficient one-stage filtering, full-spectrum reranking, real-time indexing, and memory-efficient storage. This makes it particularly relevant when vector results must be constrained by structured attributes.

Best suited for: Teams building semantic search, RAG, recommendation systems, or advanced search with rich filtering requirements.
Pricing: Freemium
The main trade-off is that its dedicated vector-service architecture adds a distinct system to operate or procure compared with pgvector. Compared with MongoDB Atlas Vector Search, Qdrant is separated by its purpose-built dense, sparse, and multivector retrieval features.

Mongo

DB Atlas Vector Search

MongoDB Atlas Vector Search is a vector database capability for storing vector embeddings alongside operational MongoDB data. It supports Flat Indexes for multitenant vector search, automated embedding during indexing, metadata filters, lexical search, graph lookups, aggregation pipelines, and geospatial search. Vector embeddings from any provider are supported under 4096 dimensions.

Best suited for: Teams already using MongoDB that need vector retrieval integrated with operational documents and aggregation workflows.
Pricing: Enterprise
The trade-off is that its value is most direct when unified operational and vector storage is an architectural requirement. Compared with Redis Vector Search, MongoDB Atlas Vector Search is differentiated by aggregation pipelines, graph lookups, and geospatial search.

Milvus

Milvus is an open-source vector database built for GenAI applications and production-scale embedding similarity search. It uses a distributed architecture for scale and high availability, supports metadata filtering and hybrid search, and is designed to handle tens of billions of vectors with minimal performance loss. Its architecture separates storage and computation.

Best suited for: Teams requiring self-hosted, distributed similarity search at very large vector counts.
Pricing: Open Source
The main trade-off is the operational responsibility of self-hosting distributed infrastructure. Compared with Pinecone, Milvus offers an open-source, self-hosted path rather than a usage-based managed API.

Redis Vector Search

Redis Vector Search adds vector similarity retrieval to Redis. It offers HNSW and FLAT indexing, RedisVL for vector storage and querying, hybrid vector and exact-match queries through Redis Query Engine, and support for text, image, and video embeddings from any provider. Its in-memory architecture targets low-latency, real-time processing.

Best suited for: Teams needing low-latency vector queries alongside Redis data structures and exact-match retrieval.
Pricing: Open Source
The trade-off is that its strongest fit is workloads already aligned with an in-memory Redis architecture. Compared with Aerospike, Redis Vector Search explicitly combines vector search with Redis Query Engine and RedisVL.

Pinecone

Pinecone is a managed vector database for semantic search, RAG, recommendations, and AI applications that search billions of items for similar matches. It provides real-time indexing, tiered storage, access controls, organization management, data security, and bring-your-own-cloud support. Its API-oriented model is intended for production retrieval without self-hosting the core vector service.

Best suited for: Teams needing managed, millisecond similarity search across billions of vectors with demand that can vary over time.
Pricing: Usage-Based
The main trade-off is that it may be a weaker fit for cost-sensitive teams that prefer self-hosting. Compared with FAISS, Pinecone adds managed production infrastructure, access controls, and real-time indexing.

Comparison Table

The table compares the primary architectural fit of each featured product rather than declaring a universal winner. Pricing reflects the supplied pricing category and published starting figure where available.

For a relational-first architecture, pgvector is the direct starting point. For application search that requires typo tolerance and traditional search controls, Typesense answers a different need from a dedicated vector database.

Our Methodology

We evaluate vector database tools using vendor documentation, published pricing information, documented product capabilities, editorial review, and public adoption signals. We focus on evidence that can be tied to a buyer requirement: supported index types, deployment model, filtering capabilities, storage architecture, published limits, and pricing structure.

Public signals can help establish that a product has visible usage, community activity, or documented customer-facing capabilities, but they do not prove enterprise adoption, production reliability for every workload, or suitability for a particular data architecture. We therefore do not treat public visibility as a ranking signal.

Editorial recommendations reflect documented trade-offs. For example, pgvector is recommended where PostgreSQL and SQL simplicity matter, Milvus where distributed self-hosted scale is required, and Pinecone where managed vector infrastructure is the priority. Pricing evidence is limited to the supplied pricing categories and figures. Where a vendor provides enterprise contact pricing or usage-based billing without a complete published estimate, we state that limitation rather than infer a deployment cost.

Frequently Asked Questions

Should I choose a vector database or a search engine?

Choose a vector database when embedding retrieval is central to the system and you need dedicated vector capabilities such as Qdrant’s dense and sparse hybrid search or Milvus’s distributed architecture. Choose a search engine when conventional user-facing search is equally important. Typesense combines vector search with typo tolerance, faceting, geo-search, grouping, and tunable ranking. The deciding factor is whether your application is primarily a retrieval system for embeddings or a search experience combining keyword and semantic behavior.

When is pgvector the right choice?

pgvector is a strong fit when your team already operates PostgreSQL and wants embeddings stored with relational data. It supports exact and approximate nearest-neighbor search, cosine similarity, Euclidean distance, inner product, IVFFlat indexing, and HNSW indexing. Its open-source model avoids adding a separate paid vector database tier. For a dedicated vector service with native hybrid search and one-stage filtering, Qdrant addresses a different architecture.

Which tool is best for filtered vector search?

Qdrant is a direct fit for dense and sparse hybrid retrieval with efficient one-stage filtering and multivector support. MongoDB Atlas Vector Search is appropriate when filters must work with MongoDB aggregation pipelines, lexical search, graph lookups, or geospatial search. Redis Vector Search supports hybrid vector and exact-match queries through Redis Query Engine. The best choice depends on whether the filtering logic belongs in a dedicated vector system, document database workflows, or Redis.

What are the managed and open-source options?

Pinecone uses a usage-based model, with a free tier and paid plans starting at $0.15 per hour for 4 cores. Typesense is freemium from $21.60/mo, while Qdrant is freemium. pgvector, Milvus, and Redis Vector Search are open source. Marqo and MongoDB Atlas Vector Search are enterprise offerings; their supplied pricing information does not include a published dollar figure.