Weaviate
FreemiumThe AI-native vector database for search, RAG, and agents.
Published 12 October 2026
Scores
Popularity4/5
One of the vector databases named in most RAG guides and framework integrations, and the bundled default in AI platforms such as Dify, with a GitHub following smaller than Qdrant's or Chroma's. Well known among developers who build retrieval systems, much less outside that work.
Learning Curve3/5
A single Docker container and the Python client get a first collection working quickly, but collection schemas, vectorizer and generative modules, multi-tenancy, and quantization settings are more concepts to learn than a minimal vector store asks for.
Flexibility5/5
Vector, keyword, and hybrid search, pluggable embedding and generative modules for many providers, multi-tenancy, three API styles, and deployment in Docker, Kubernetes, embedded mode, the managed cloud, or the customer's own cloud.
Performance4/5
A Go server with an HNSW index, quantization options that cut memory use, and horizontal scaling with replication. Memory use grows quickly with uncompressed vectors, so large indexes need tuning.
Portability4/5
The BSD-3-Clause core runs anywhere Docker or Kubernetes does, and data can be exported through the APIs. A few newer self-hosted features sit behind an enterprise license key, and the built-in vectorizer modules tie a collection's embeddings to the provider it was configured with.
About Weaviate
Weaviate is an open-source vector database written in Go. It stores data objects together with their vector embeddings and searches them by meaning, by keyword with BM25, or by both at once in hybrid search, with metadata filters applied during the search. Collections can be split into isolated tenants, replicated across nodes, and compressed with product, binary, or rotational quantization so large indexes fit in less memory.
What sets it apart is how much of the AI pipeline runs inside the database. Model integration modules can create embeddings on import and at query time by calling OpenAI, Cohere, Google, Hugging Face, Ollama, and other providers, and generative modules pass search results to a language model for retrieval-augmented generation in the same request. Applications talk to it over REST, gRPC, or GraphQL, with official clients for Python, TypeScript, Go, Java, and C#. Many AI platforms ship it as their default vector store, Dify's Docker Compose deployment among them.
Weaviate runs as a single Docker container for development, on Kubernetes through the official Helm chart for production, or embedded inside a Python or JavaScript process for tests. Weaviate Cloud is the managed service from Weaviate B.V., with a permanent free tier, a pay-as-you-go serverless plan, and contracted Premium plans on shared or dedicated clusters, plus a bring-your-own-cloud option. The database core is BSD-3-Clause licensed. A separate enterprise directory, unlocked by a license key, holds a small set of newer self-hosted features such as namespaces and backup deduplication.
Key Features
- Vector, BM25 keyword, and hybrid search with filters applied during the search
- Model integration modules that create embeddings on import and at query time
- Generative modules that run retrieval-augmented generation inside the query
- Multi-tenancy, replication, and product, binary, and rotational quantization
- REST, gRPC, and GraphQL APIs with clients for Python, TypeScript, Go, Java, and C#
- Runs in Docker, on Kubernetes with an official Helm chart, embedded, or on Weaviate Cloud
Pros
- Hybrid search and built-in vectorization reduce the glue code a RAG pipeline needs
- BSD-3-Clause core that can be self-hosted at no license cost
- Native multi-tenancy suits products that keep each customer's data separate
- Ships as the default vector store in AI platforms such as Dify, so it is well tested in that role
Cons
- Memory-hungry at scale, since the HNSW index lives in RAM unless quantization is tuned
- Collections, modules, and vectorizer settings take longer to learn than a minimal vector store
- A few newer self-hosted features now require an enterprise license key
- Version upgrades of the server and the Python client have needed migration work, as the v4 client change showed
Weaviate Pricing
Freemium- · BSD-3-Clause database core in Docker or on Kubernetes
- · Enterprise features such as namespaces need a separate license key
- · Weaviate Cloud with no time limit and no credit card
- · 100,000 objects, 1 GB of memory, 10 GB of disk, and 1 collection
- · Pay-as-you-go shared cloud cluster from $45 a month, no commitment
- · Billed per million vector dimensions stored and per GiB, with a 99.5% uptime SLA
- · Prepaid contract on a shared or dedicated cluster
- · Up to 99.95% uptime SLA; bring-your-own-cloud and HIPAA on AWS through sales
Tech Stacks with Weaviate
Dify Self-Hosted
InfrastructureRun Dify, the open-source platform for building AI workflows, RAG chatbots, and agents, from its official Docker Compose file on a server you control. The bundle brings Dify's API, workers, and web app with a database (PostgreSQL by default, or MySQL), Redis, a vector store for knowledge bases (Weaviate by default, or Qdrant or pgvector), and its own NGINX, so a team gets the whole platform without Dify Cloud's plan limits.
AnythingLLM Self-Hosted
InfrastructureRun AnythingLLM, the open-source private AI workspace for chatting with your documents and running agents, in Docker on a server you control. One container holds the app, its database, and a built-in vector store, and a model runtime such as Ollama, LM Studio, or llama.cpp answers the questions, usually on a machine with a graphics card, so documents and conversations stay on hardware you run.
Tools Related to Weaviate
Integrates with Weaviate(3)
Weaviate is the default vector store in Dify's Docker Compose deployment, started as its own container and selected with the VECTOR_STORE setting to hold every knowledge base's embeddings.
The langchain-weaviate partner package provides a Weaviate vector store for LangChain retrievers and RAG chains, including hybrid search and multi-tenant collections.
The llama-index-vector-stores-weaviate package lets LlamaIndex build and query indexes stored in Weaviate collections, including hybrid queries.
Alternatives to Weaviate(4)
Weaviate has a BSD-licensed core you can self-host as well as a managed cloud; Pinecone is closed-source and fully managed, with no server to run. Pick Weaviate to keep the option of running it yourself.
Weaviate is a server built for production scale, with replication, multi-tenancy, and hybrid search; Chroma has the smallest API and runs in-process or as one command, which suits prototypes and small RAG apps.
Weaviate builds embedding and generative modules into the database and has native multi-tenancy; Qdrant is a Rust engine with composable, low-level control over scoring and filtering. Both are open source and self-hostable.
Weaviate is a dedicated vector database with hybrid search, quantization, and multi-tenancy for large collections; PostgreSQL with the pgvector extension keeps embeddings next to existing relational data with no new service.
Vendor
Weaviate
Website →Tags
Details
- Maintained
- Yes
- DB model
- Vector
- Query language
- Custom
- Hosting
- Cloud & Self-hosted
- ACID compliant
- No
- Replication
- Yes
- GitHub stars
- 16.9k
- Stars updated
- 2026-10-09