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Weaviate

Open-source vector database for AI-native applications and semantic search.

Weaviate is an open-source vector database purpose-built for AI applications. Supports vector, keyword, and hybrid search with built-in vectorization modules for OpenAI, Cohere, Hugging Face, and more. Used for RAG pipelines, semantic search, recommendation engines, and multimodal search. Written in Go for high performance.

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About Weaviate

Weaviate is a cloud-native vector database that stores both objects and vectors, enabling the combination of vector search with structured filtering. Unlike simpler vector stores, Weaviate includes built-in vectorization modules that can automatically generate embeddings from text, images, and other data types using models from OpenAI, Cohere, Hugging Face, and others.

The database supports multiple search types — pure vector (semantic), keyword (BM25), and hybrid search that combines both approaches. Its GraphQL-based API and REST endpoints make integration straightforward. Weaviate also supports generative search (RAG) natively, combining retrieval with LLM-based answer generation.

Weaviate is open source under the BSD 3-Clause license. Self-hosted deployment is free under BSD-3-Clause. Current Weaviate Cloud pricing includes an always-free Engram option plus Flex pay-as-you-go, Premium prepaid-contract, and Enterprise tiers, with AI-service usage billed separately where applicable.

Pricing & Platform Specs

Pricing Summary

Weaviate is free and open-source under BSD-3-Clause. Weaviate Cloud provides a persistent Free tier for prototyping (100k objects), a pay-as-you-go Flex plan starting at $45/month on shared infrastructure, Dedicated Cloud starting at $400/month (99.95% SLA), and custom BYOC Enterprise plans.

full pricing breakdown →

Supported Platforms

Self-hosted on Docker, Kubernetes. Weaviate Cloud fully managed. Go-based, REST + GraphQL APIs.

Explore categories, tags & use cases

Alternatives

All Weaviate alternatives →
USearch logo

USearch

Fast embeddable vector search engine

USearch is a high-performance vector search engine implementing HNSW algorithms for approximate nearest neighbor queries across C++, Python, JavaScript, Rust, Java, Go, and more. It supports user-defined distance metrics, memory-mapped persistence for datasets larger than RAM, and filtered search with predicates. Used by YugabyteDB and ScyllaDB as their production vector indexing backend.

Open Source
WeKnora logo

WeKnora

Enterprise RAG framework by Tencent

WeKnora is a Tencent-developed LLM-powered knowledge management and Q&A framework for enterprise document understanding and semantic retrieval. Supports 10+ document formats including PDF, Word, Excel, and images with seamless IM platform integration for WeCom, Feishu, Slack, and Telegram. Offers Quick Q&A mode using RAG pipelines and Intelligent Reasoning mode with ReACT agents for complex multi-step reasoning tasks across organizational knowledge bases.

freemiumOpen Source
Marqo logo

Marqo

Embedding-first search and discovery engine for AI-powered product experiences.

Marqo is an open-source tensor search engine that combines embedding generation and vector search in a single API, removing the need to manage separate embedding pipelines and vector databases. Built for product discovery and multi-modal search, it lets teams index text, images, and structured data together, returning ranked results based on semantic similarity rather than keyword overlap.

freemiumOpen Source
FAISS logo

FAISS

Library for efficient similarity search and clustering of dense vectors at billion-scale.

FAISS is Meta AI Research's open-source library for efficient similarity search and clustering of dense vectors. It implements approximate nearest-neighbor algorithms designed to scale to billions of vectors, with optimized indexes that fit in RAM and GPU acceleration for the largest workloads. Engineering teams use FAISS as the retrieval primitive underneath custom RAG pipelines, recommendation systems, and large-scale embedding search infrastructure.

Open Source
hnswlib logo

hnswlib

Header-only C++ implementation of HNSW for fast approximate nearest-neighbor search.

hnswlib is a header-only C++ library implementing the Hierarchical Navigable Small World (HNSW) graph algorithm for approximate nearest-neighbor search, with Python bindings and a tiny dependency footprint. Originally developed by the nmslib team, it has become the default HNSW implementation embedded inside many vector databases and search products. Engineers use it directly when they want HNSW retrieval without pulling in a heavyweight vector DB.

Open Source
Vald logo

Vald

Cloud-native distributed vector search engine built for Kubernetes with automatic indexing and horizontal scaling.

Vald is a highly scalable distributed approximate nearest neighbor (ANN) vector search engine designed for cloud-native, Kubernetes-based architectures. Maintained by LY Corporation and listed in the CNCF Landscape, it uses the NGT algorithm (developed at Yahoo Japan), supports automatic incremental index backup, and handles billion-scale datasets across loosely coupled microservice components that scale horizontally via Helm.

Open Source

Side-by-Side Comparisons

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Weaviate
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pgvector PostgreSQL parent mark
pgvector

Weaviate vs pgvector: AI-Native Hybrid Search or Postgres Simplicity?

Weaviate and pgvector can both support production RAG, yet their product boundaries are fundamentally different. Weaviate is an AI-native vector database with object and vector storage, BM25 and vector hybrid search, model-provider integrations, reranking, multi-tenancy, replication, and access-control features. pgvector is a PostgreSQL extension that adds vector similarity to the relational database many applications already use. For teams explicitly comparing the two to build a search or RAG platform, Weaviate stands out as the primary recommendation. Its integrated hybrid retrieval, tenant-aware data model, modular vectorization, and production search controls reduce the amount of application glue required for a sophisticated retrieval service. pgvector remains the better minimalist option when vectors should stay beside existing relational data, but Weaviate wins the dominant search-platform intent.

Weaviatepgvector
Weaviate logo
Weaviate
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Chroma logo
Chroma

Weaviate vs Chroma: Production AI Database or Fast Retrieval Stack?

Weaviate and Chroma both serve RAG and semantic search teams, but they sit at different stages of the AI database maturity curve. Weaviate is the stronger production platform when teams need object/vector modeling, integrated vectorizers, hybrid search, governance, multi-tenancy, replication, and RBAC. Chroma is the faster retrieval stack when AI teams want a simple collection API, local-to-cloud iteration, and focused vector, hybrid, and full-text search. This is a fit-based comparison, not a universal winner call.

WeaviateChroma
Weaviate logo
Weaviate
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Milvus logo
Milvus

Weaviate vs Milvus — AI-Native Vector Platform vs Billion-Scale Distributed Search

Weaviate and Milvus are both mature, permissively licensed open-source vector databases for RAG, semantic search, and recommendation workloads, but they optimize for different teams. Weaviate bundles built-in vectorization, hybrid BM25-plus-vector search, and generative retrieval into an AI-native database platform. Milvus is a dedicated distributed search engine with broad index selection, GPU-accelerated options, and an architecture designed for very large vector collections. This comparison frames the decision as integrated AI convenience versus dedicated distributed scale, not as a universal winner.

WeaviateMilvus
Qdrant logo
Qdrant
vs
Weaviate logo
Weaviate

Qdrant vs Weaviate — Vector Search Engines for Production AI in 2026

Qdrant and Weaviate are two of the most established open-source vector databases powering retrieval-augmented generation, semantic search, and AI agents in production. Both let you store embeddings, run approximate-nearest-neighbor queries, and filter on structured metadata — but their philosophies, query surfaces, and operational profiles diverge enough that the right pick usually comes down to your stack and team rather than benchmarks.

QdrantWeaviate
View 2 more comparisons

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FAQ

What is Weaviate?

Weaviate is an open-source vector database purpose-built for AI applications. Supports vector, keyword, and hybrid search with built-in vectorization modules for OpenAI, Cohere, Hugging Face, and more. Used for RAG pipelines, semantic search, recommendation engines, and multimodal search. Written in Go for high performance.

Is Weaviate free?

Weaviate offers a free tier alongside paid plans. Weaviate is free and open-source under BSD-3-Clause. Weaviate Cloud provides a persistent Free tier for prototyping (100k objects), a pay-as-you-go Flex plan starting at $45/month on shared infrastructure, Dedicated Cloud starting at $400/month (99.95% SLA), and custom BYOC Enterprise plans.

Is Weaviate open source?

Yes — Weaviate is open source.

Is Weaviate still maintained?

Yes — Weaviate is active. Its listing was last verified on August 26, 2026.

What are the best Weaviate alternatives?

The first editor-selected Weaviate alternatives are USearch, WeKnora, Marqo, and more.

How does Weaviate score in our review?

The published editorial review lists Weaviate at 85/100 overall across speed, privacy, and developer experience. Check the review's evidence status and test metadata for its verification level.