Weaviate

Strong

by Weaviate B.V. · founded 2019 · updated Jul 2026

Weaviate is an open-source vector database with built-in hybrid search, multi-tenancy and a module system that plugs into every major embedding provider. Run it yourself or use Weaviate Cloud; a strong middle path between control and convenience.

Vector DatabasesOpen SourceEstablishedFrom Free (self-hosted) / Cloud from $25 per month
81.9TIP Score
Strong

The score, taken apart

Fixed weights, sources attached, the formula

Cap 82Val 85Sec 82Int 86Mat 80Mom 76
Capability(30%)82
Value for Money(15%)85
Security & Compliance(15%)82
Integrations & Ecosystem(15%)86
Maturity & Reliability(10%)80
Momentum(15%)76

Best for

  • Hybrid search applications
  • SaaS multi-tenant vector workloads
  • Teams wanting open source + cloud option

Key integrations

LangChainLlamaIndexOpenAI/Cohere/Hugging Face modulesKubernetesGraphQL & REST APIs

Strengths

  • True open source with a first-class managed cloud
  • Native hybrid (BM25 + vector) search and reranking
  • Multi-tenancy designed for SaaS builders

Limitations

  • Operating large self-hosted clusters requires expertise
  • GraphQL API adds a learning curve
  • Resource-hungry at very large scale without tuning

Evidence behind this score

Every TIP Score is backed by verifiable claims. Data is a curated snapshot, always confirm current terms with the vendor.

ClaimSourceAs of
BSD-3 licensed core with 12K+ GitHub starsGitHub repositorySep 2025
SOC 2 Type 2 for Weaviate CloudWeaviate trust pageApr 2025
Native multi-tenancy supports millions of tenants per clusterWeaviate documentationMar 2025

Intelligence on Weaviate

Fundingmedium impactNov 20, 2025

Vector database market bifurcates: managed convenience vs. open performance

Funding and adoption data show Pinecone consolidating compliance-sensitive enterprise workloads while Qdrant and Weaviate grow fastest among self-hosting startups. Choose by ops capacity, not hype.

PineconeQdrantWeaviateAI TIP market analysis
Researchmedium impactNov 5, 2025

RAG quality studies highlight parsing as the biggest lever

Multiple evaluations found document parsing quality moves retrieval accuracy more than embedding model choice, validating investment in parsing layers like LlamaParse before swapping vector stores.

LlamaIndexPineconeWeaviateApplied research publications

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