Qdrant vs Weaviate
On the evidence we track, Weaviate leads this comparison with a composite score of 63/100. Scores are only directly comparable because these tools share a category; the full breakdown and every source is below.
Capabilities
Feature-by-feature on the axes that matter for vector databases. “-” means undocumented, not absent.
What each one is
The product in its own terms, so the numbers below have context.
Qdrant
A database system designed for storing and searching vectors with comprehensive filtering capabilities. It provides production-ready APIs and tools for semantic similarity matching and embedding-based applications.
Weaviate
LeaderA vector database that enables semantic search by storing both objects and vectors, with built-in support for combining vector similarity search with traditional keyword search, retrieval-augmented generation, and reranking capabilities.
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
Qdrant
- Slack
- Adobe
- Hubspot
- Arize
- Google DeepMind
- Qualcomm
- Cohere
- AWS
- GCP
- Azure
- LangChain
- FastEmbed
- Google Cloud Storage
- Azure Blob Storage
Weaviate
LeaderNot documented yet.
Qdrant or Weaviate: which one depends on you
A composite score cannot know your constraints. Describe them and both get re-weighted against what you actually need, with the evidence behind every position.
Free to run, no account needed to start. How the evaluation works
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.