head to head · open source
Qdrant vs Weaviate
Qdrant has 34,638 GitHub stars, 2,680 forks, 704 open issues and last shipped yesterday. Weaviate has 16,818 stars, 1,403 forks, 738 open issues and last shipped yesterday. Qdrant leads on adoption by 106% (34,638 vs 16,818 stars). Qdrant is written in Rust under Apache-2.0; Weaviate is written in Go under a custom or non-standard licence. Qdrant has attracted 8% as many forks as stars, Weaviate 8%. Weaviate was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 6 topic tags (hnsw, hybrid-search, image-search, mlops), so they are genuine substitutes rather than adjacent tools.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
← all 8884 open source comparisons
Side by side
| Qdrant | Weaviate | |
|---|---|---|
| GitHub stars | ★ 35K | ★ 17K |
| License | Apache-2.0 | Custom / other |
| Written in | Rust | Go |
| Last push | 2026-09-17 | 2026-09-17 |
| Forks | ⑂ 2.7K | ⑂ 1.4K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick Qdrant if
- You weight community size — 35K stars and counting
- You want the Apache-2.0 license terms
- Your stack matches Rust
- You value the larger contributor base for long-term maintenance
pick Weaviate if
- You want the Weaviate feature set and don't need the biggest community
- You prefer the Custom / other license terms
- Your stack matches Go
- You evaluated both and Weaviate fits your workflow better
About Qdrant
Qdrant is a vector similarity search engine and database built in Rust, designed for production use in AI applications. It stores, manages, and retrieves high dimensional vectors alongside associated metadata (payloads), supporting fast nearest neighbor search with filtering. It solves the problem of efficiently performing semantic or neural based matching at scale—such as finding similar images, recommending content, or powering retrieval augmented generation (RAG)—by replacing slow or inflexible brute force or index based approaches with a purpose built engine optimized for vector operations.…
read the full Qdrant overview →
About Weaviate
Weaviate open source vector database. Stores objects and vectors together. Lives in cloud native database and AI search ecosystem. Enables semantic search at scale. Combines vector similarity search with keyword filtering, retrieval augmented generation, and reranking in single query interface.
read the full Weaviate overview →
More in Infrastructure & Operations
Related comparisons
More Databases projects
Compare either of these against the rest of the Databases field.
Frequently asked questions
Is Qdrant or Weaviate more popular?
Qdrant has 34,638 GitHub stars and Weaviate has 16,818. Qdrant has the larger community by that measure.
Are Qdrant and Weaviate free?
Both are open source. Qdrant is licensed under Apache-2.0, and Weaviate has no licence declared in this registry. Both are free to self-host.
What is the difference between Qdrant and Weaviate?
Qdrant is written in Rust and Weaviate in Go. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, Qdrant or Weaviate?
Choose Qdrant if you want the larger community (34,638 stars) or its Apache-2.0 licence terms. Choose Weaviate if its feature set, stack or Custom / other licence fits better. Both are self-hostable.