Weaviate

Open-source self-hostable vector database with hybrid search and module ecosystem.

VISIT WEAVIATE

Key takeaway: When hybrid retrieval has to be a first-class feature rather than something you bolt together, Weaviate is the open-source engine to start with. Keyword and vector scoring fuse in one query, and vectorizer modules mean the database can do the embedding for you. Expect to feed it more memory than Qdrant asks for.

Quick facts

  • CategorySelf-hosted
  • EngineGo
  • PricingFreemium
  • LicenseBSD-3-Clause
  • Created2019
  • GitHub stars14.6k
  • Hybrid searchNative
  • Edge-readyNo
  • Multi-tenantNative
  • Max dimensions65,535

What it is

Weaviate is an open-source vector database with native hybrid search (vector + keyword), module ecosystem (transformers, OpenAI, Cohere as built-in vectorizers), and GraphQL + REST APIs. Self-host or use Weaviate Cloud. Strong on multimodal and on tight integration with LangChain / LlamaIndex.

Best for

  • Self-hosted vector workloads at scale
  • Apps that want hybrid search (vector + BM25) as a first-class primitive
  • Multi-tenant deployments with isolation requirements
  • Teams comfortable operating Go-based services

When not to pick it

Skip Weaviate if you want fully-managed without the open-source self-host complexity, Pinecone is simpler. Skip for very small workloads where pgvector is sufficient.

My take

Weaviate is the strongest open-source vector engine in 2026. Pick it when self-hosting is non-negotiable and the workload is past pgvector's scale ceiling.

Hybrid search and vectorizer modules

Two things separate Weaviate from the leaner engines. The first is that BM25 keyword scoring and vector similarity fuse into a single ranked result, with one parameter controlling the balance. That matters more than teams expect, because pure semantic search reliably fails on part numbers, error codes, and proper nouns, and hybrid is the standard fix. The second is the module system. Weaviate can call an embedding provider itself at write and query time, so you can hand it raw text and skip building an embedding pipeline, or switch that off and supply your own vectors. Add generative modules and the database will run the retrieval-augmented generation step too. That breadth is convenient and it is also a coupling decision, because the more of your pipeline lives inside the database, the more a future migration has to touch.

Operating it, honestly

Weaviate is a Go service you deploy, monitor, upgrade, and give enough RAM to. HNSW indexes are memory-hungry, and while compression and offloading options exist, the naive configuration will surprise you on a large collection, so capacity planning is not optional. Multi-tenancy is a genuine strength: tenants get their own shards, and inactive ones can move to cheaper storage, which suits SaaS products carrying many small customer corpora. The GraphQL-flavoured query interface is a matter of taste and a mild learning curve if your team has not used it before. Where it gives ground is heavily filtered search, which is the specific thing Qdrant's filterable index was built for. If you want a lighter service to run, Qdrant is that; Weaviate Cloud exists if you want the engine without the operations.

Frequently asked questions

Is Weaviate free?

The database is open source under a BSD licence and free to self-host, with no feature paywall on the core engine. Weaviate Cloud is the paid managed option and there are commercial enterprise offerings above it. Self-hosting costs you compute and memory, and HNSW indexes want considerably more memory than most teams first budget for on a large collection.

Weaviate or Qdrant?

Weaviate when hybrid keyword-and-vector search or built-in vectorizer modules are decisive, and when heavy multi-tenancy is part of the design from day one. Qdrant when you want a lighter service to operate, the strongest filtered-search performance, and you are happy running your own embedding step. Both are open source and both offer managed hosting if you want it.

Does Weaviate handle multi-tenant SaaS?

Better than most. Tenants get their own shards rather than a filter over one shared index, which keeps queries fast as tenant count grows and makes deleting a single customer's data straightforward. Inactive tenants can be offloaded to cheaper storage and brought back on demand, which is what makes thousands of small corpora affordable to serve at once.

Links

Compare Weaviate side-by-side

Weaviate vs Qdrant → Weaviate vs Pinecone → Weaviate vs Milvus →

Similar tools you should also consider

Qdrant

Rust-fast open-source vector engine. Cleaner API than Weaviate, smaller footprint.

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Milvus

Distributed vector DB built for billion-scale workloads. Heavy, capable, China-AI-aligned.

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Pinecone

The original managed vector database. Polished SDK, predictable latency, expensive at scale.

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If Weaviate is your pick, the next conversation is short

The 30-min call is where your vector-DB choice becomes a real RAG architecture, a chunking + reranking strategy that actually works for your corpus, and a price range you can take to your stakeholders. Describe your data shape, your query patterns, your latency budget. I tell you whether Weaviate is genuinely your fit.