📖 The AI Tool Bible

Quivr vs Weaviate

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

 
Quivr
RAG
Weaviate
RAG
TaglineOpen-source RAG framework for building custom AI assistants over your own documents in a few lines of Python.Open-source vector DB with hybrid search and modules.
CategoryRAGRAG
PricingFree· Open source (pip install quivr-core); pay only for LLM/vector-store usageFreemium· Free open-source; cloud from $25/mo
ModelMulti-model (OpenAI, Anthropic, Mistral, Gemma)Hosted vector DB (not an LLM)
Editorial score—8.4 / 10
Use cases
document-qacustom-knowledge-baserag-pipelineinternal-assistantschat-with-pdf
self-hosted RAGhybrid search
Pros
  • Genuinely open source and pip-installable, no vendor lock-in
  • Model-agnostic: OpenAI, Anthropic, Mistral, and Gemma supported
  • Minimal boilerplate to get a working RAG assistant running
  • Pairs with Megaparse for tougher PDF and document ingestion
  • Customizable pipeline with tools and web search when you need more
  • Hybrid search built in
  • Self-host or cloud
  • Module ecosystem
  • GraphQL + REST APIs
Cons
  • Python library, not a hosted product or UI
  • You manage infra, vector store, and evals yourself
  • Documentation site is sparse compared to larger RAG frameworks
  • LLM and embedding costs are on you
  • More ops than Pinecone if self-hosted
  • Smaller community
Websitecore.quivr.comweaviate.io
Pick Quivr if
  • ✅ Genuinely open source and pip-installable, no vendor lock-in
  • ✅ Model-agnostic: OpenAI, Anthropic, Mistral, and Gemma supported
  • ✅ Minimal boilerplate to get a working RAG assistant running
  • ✅ Pairs with Megaparse for tougher PDF and document ingestion
Pick Weaviate if
  • ✅ Hybrid search built in
  • ✅ Self-host or cloud
  • ✅ Module ecosystem
  • ✅ GraphQL + REST APIs
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