How it was ranked:10 SaaS products compared. Each verdict is below.
Pinecone is the best vector database for most engineering teams because it is fully managed and serverless, holds dense vectors and BM25 full-text search in one index, and has a free tier with paid plans from $20 a month. Qdrant is the pick for fast filtered search you can self-host, Milvus for billion-vector scale through Zilliz Cloud, and Weaviate for hybrid search with embeddings created inside the database.
Top 10 Vector Databases, compared
RANK
ENTRY
BEST FOR
SCORE
FROM
1
PineconePinecone made full-text search generally available on 9 September 2026, adding BM25 keyword ranking and text-match filters to the same index that holds dense vectors.
Teams building RAG applications and semantic search features quickly
96.8
$0 (free tier), Starter $25/month
2
QdrantQdrant says it applies payload filters during HNSW graph traversal rather than before or after the search, and it lists HubSpot, TripAdvisor and Deutsche Telekom as production users.
WeaviateWeaviate says its open-source database has passed 20 million downloads, and it lists Booking, Cisco and Intuit among its users.
Enterprises wanting open-source control with professional support options
92.9
Free (open-source), Weaviate Cloud $99/month
5
ElasticsearchElastic says Better Binary Quantization cuts the memory used by vector indexes by up to 95%.
Self-hosted and custom analytics setups
91.6
95.00 USD per month
6
turbopufferturbopuffer says it holds more than 1 trillion documents in production and has seen more than 250 million namespaces.
Huge multi-tenant datasets on a budget
90.4
Usage-based, from $16/month minimum
7
MongoDB Atlas Vector SearchAtlas Vector Search indexes support embeddings of up to 8,192 dimensions, and free Atlas clusters allow up to three search or vector indexes.
Apps already built on MongoDB
89.2
Free tier; dedicated from $56.94/month
8
VespaVespa names Perplexity, Spotify and Yahoo as production users on its website.
Large-scale search applications requiring real-time ML ranking
87.9
Free (open-source), Vespa Cloud custom
9
ChromaChroma had more than 29,000 GitHub stars on 27 October 2026.
Teams rapidly prototyping RAG systems and LLM applications
86.7
Free (open-source), Chroma Cloud custom pricing
10
SupabaseSupabase Vector is built on pgvector, an open-source Postgres extension that had more than 23,000 GitHub stars on 27 October 2026.
Full stack developers building scalable applications
Managed vector database for semantic search at scale
Pinecone, founded by Edo Liberty, runs a fully managed, serverless vector database with integrated embedding, reranking and a Bring Your Own Cloud option. It raised a $100 million Series B led by Andreessen Horowitz at a $750 million valuation in April 2023, and named Ash Ashutosh chief executive in September 2025.
From $0 (free tier), Starter $25/month
PROS
Exceptional ease of use with minimal DevOps required
Built-in metadata filtering and hybrid search capabilities
CONS
Pricing increases significantly at production scale
Vendor lock-in as a proprietary cloud service
Best for
Teams building RAG applications and semantic search features quickly
Where it falls short
Costs rise significantly when scaling to production environments.
Vector search engine optimised for extended filtering and production workloads
Berlin-based Qdrant builds an open-source vector search engine in Rust that runs self-hosted or on Qdrant Cloud, with hybrid and private cloud options for regulated teams. It raised a $50 million Series B led by AVP in March 2026, taking total funding to $87.8 million, according to TechTarget.
Free (open-source), Cloud service $25/month
PROS
Advanced filtering and aggregation on payload data
Excellent support for edge deployment and distributed setups
CONS
Smaller community and ecosystem than Weaviate
Documentation gaps in advanced configuration scenarios
Open-source vector database engineered for scalable similarity search
Milvus is an open-source vector database created by Zilliz and a graduated LF AI & Data Foundation project, built to scale to billions of vectors in distributed mode. Zilliz sells it as a managed service, Zilliz Cloud, and raised a $60 million Series B extension in 2022 that took its total funding to $113 million.
Free (open-source), Managed service varies
PROS
Exceptional performance for extremely large-scale datasets
Rich index support including HNSW, IVF, and Annoy
CONS
Requires specialised knowledge for optimal tuning
Community is smaller compared to Weaviate or Elasticsearch
Open-source vector search platform with GraphQL API
Amsterdam-based Weaviate makes an open-source vector database with built-in hybrid search, multi-tenancy and modules that create embeddings inside the database, plus a Query Agent that turns plain-language questions into queries. It raised a $50 million Series B led by Index Ventures in April 2023.
Free (open-source), Weaviate Cloud $99/month
PROS
Complete transparency and customisability with open-source codebase
Native GraphQL API simplifies complex queries
CONS
Steeper learning curve than fully managed solutions
Self-hosting requires more operational expertise
Best for
Enterprises wanting open-source control with professional support options
Where it falls short
It requires more operational expertise for self-hosting.
Pick something else if
you lack the technical skills to manage infrastructure.
Elastic's Elasticsearch adds dense vector fields, HNSW indexing and Better Binary Quantization to a mature search engine, so teams can blend BM25 keyword and vector results with filters in one query. Elastic added the AGPL as an open-source licence option for Elasticsearch in August 2024.
From 95.00 USD per month
PROS
Highly customisable with powerful search features
Strong community support and extensive documentation
CONS
Requires significant operational overhead to maintain clusters
Best for
Self-hosted and custom analytics setups
Where it falls short
It demands significant operational overhead to maintain clusters.
Pick something else if
you want a low maintenance log management solution.
turbopuffer, launched in 2023 by former Shopify engineers Simon Eskildsen and Justine Li, keeps vector and full-text indexes on object storage to cut the cost of very large, many-namespace workloads. It lists Anthropic, Cursor, Notion and Atlassian among its customers and added Thrive Capital as an investor in December 2025.
MongoDB Atlas Vector Search stores embeddings next to operational documents in Atlas and supports pre-filtering and hybrid queries that combine vector and full-text results. MongoDB acquired Voyage AI, a maker of embedding and reranking models, in February 2025 to build those models into Atlas.
Big data search engine supporting vectors and real-time ML
Vespa, spun out of Yahoo in 2023, combines vector search, keyword search and machine-learned ranking with model inference in one open-source engine, offered self-managed or as Vespa Cloud. It raised a $31 million Series A from Blossom Capital in November 2023.
Free (open-source), Vespa Cloud custom
PROS
Sophisticated ranking models directly integrated into search pipeline
Excellent at very large-scale deployment with low latency
CONS
Steep learning curve with complex configuration
Smaller community relative to Elasticsearch or Milvus
Best for
Large-scale search applications requiring real-time ML ranking
Embedding database purpose-built for LLM applications
Chroma is an open-source, Apache 2.0 search database for AI apps that runs in-process for prototypes or as Chroma Cloud, a serverless service built on object storage. It supports vector, BM25 and SPLADE sparse, full-text, regex and metadata search, according to its site.
Free (open-source), Chroma Cloud custom pricing
PROS
Exceptionally simple API, extremely fast onboarding
In-memory or persistent storage options for flexibility
CONS
Limited filtering and advanced query capabilities
Production scaling requires migration to more robust solutions
Best for
Teams rapidly prototyping RAG systems and LLM applications
Where it falls short
Limited filtering and advanced query capabilities.
The open source Firebase alternative with powerful AI features
Supabase is a managed Postgres platform that includes the pgvector extension, so teams can store embeddings in ordinary tables and query them with SQL joins and filters next to app data. It supports HNSW and IVFFlat indexes and bundles authentication, file storage and edge functions in the same project.
Free
PROS
Instant database setup with auth and storage
Built-in vector embeddings for AI workflows
CONS
Complex pricing scaling for high volume projects
Best for
Full stack developers building scalable applications
Where it falls short
Complex pricing scaling for high volume projects may be difficult to manage.
Pick something else if
you need predictable costs for large scale usage.
85.4
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Ask us for a quote for one, two or three years. Nothing recurring.
Most vector databases have a free tier or an open-source edition, so small projects can start at no cost. Managed plans start at $20 a month for Pinecone, $45 a month for Weaviate and $99 a month for Elastic Cloud, while turbopuffer bills by usage with a $16 monthly minimum. Self-hosting Qdrant, Milvus or Chroma removes licence fees but adds infrastructure and operations cost.
How do I choose a vector database?
Start with your data size, your filtering needs and whether you want to run servers yourself. Pinecone suits teams that want no operations, Qdrant and Milvus suit teams that want open source with a managed option, and Vespa suits teams that need custom ranking models. If your data already lives in MongoDB or Postgres, MongoDB Atlas Vector Search or Supabase can save you running a second database.
What is the best vector database for a startup or small team?
Pinecone and Chroma are the quickest to start, with Pinecone's free Starter tier for managed use and Chroma's in-process library for local work. Supabase is a strong choice for a small team already on Postgres, because its free plan includes pgvector alongside authentication and storage.
Should I use a dedicated vector database or pgvector?
pgvector keeps embeddings in Postgres, so a team on Supabase can join vectors with relational data and avoid syncing a second system. Dedicated engines such as Pinecone, Qdrant and Milvus are built for larger vector counts, faster filtered search and features such as quantisation and hybrid ranking. A sensible path is to start on pgvector and move to a dedicated database when latency or scale becomes a problem.
Which vector databases support hybrid search?
Hybrid search combines keyword matching such as BM25 with vector similarity, which helps with product codes, names and other exact terms. Weaviate, Qdrant, Elasticsearch, Vespa, turbopuffer and Pinecone support it in a single query, and MongoDB Atlas Vector Search can merge vector and full-text results. Elasticsearch brings the most mature keyword engine, while Pinecone added full-text search to its vector indexes more recently.
