Weaviate

Open-source vector database with best-in-class hybrid search

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Vector Databases Open Source freemium Free Tier established

Our Take

Weaviate stands out with built-in vectorization modules — you can ingest raw text and images without a separate embedding pipeline. Native hybrid search runs BM25 and dense vector queries in parallel, with Relative Score Fusion that preserves score magnitudes instead of just ordinal ranks. Written in Go with a custom LSM-tree storage engine. Four index types (HNSW, Flat, Dynamic, HFresh) and tenant-aware classes providing hard physical isolation via dedicated shards — far stronger than namespace-based logical separation. Binary Quantization cuts memory costs up to 70%. BlockMax WAND (GA 2025) makes keyword search 10x faster. ~16,000 GitHub stars, 1M+ Docker pulls/month. Flex cloud starts at $45/month. The tradeoff: self-hosted deployments demand real DevOps investment (Kubernetes, sharding, monitoring), HNSW indexes are memory-intensive, and cold-start latency peaks at 1.3s (vs Qdrant's 163ms). ~$68M raised (including $50M Series B, $16M Series A, and seed funding).

Pros

  • + Built-in vectorization — no external embedding pipeline needed
  • + Superior native hybrid search with BM25F and Relative Score Fusion
  • + Open source (BSD 3-Clause) with flexible deployment (cloud, self-hosted, BYOC, embedded)
  • + Hard multi-tenant isolation via dedicated shards per tenant
  • + Binary Quantization cuts memory costs up to 70%
  • + BlockMax WAND makes keyword search 10x faster
  • + Full RBAC, SSO (OIDC), and ACL security model

Cons

  • - Self-hosted deployments require significant DevOps expertise
  • - HNSW indexes are memory-intensive
  • - Cold-start latency peaks at 1.3 seconds
  • - GraphQL/v4 client API has a steeper learning curve than Pinecone
  • - Cloud pricing tiers can be complex to navigate

Details

Pricing Model
freemium
Starting Price
$0
Self-Hosted
Yes
Cloud Hosted
Yes
Founded
2019
Repository
GitHub →

Best For

  • • Hybrid search (BM25 + vector with Relative Score Fusion)
  • • Built-in vectorization at import time
  • • Multi-tenant applications with storage tiering
  • • Self-hosted vector infrastructure

Integrations

LangChain LlamaIndex OpenAI Cohere Hugging Face Google Vertex AI

Articles featuring Weaviate

Best Of Best Vector Databases in 2026
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