Qdrant vs Weaviate
A side-by-side comparison of Qdrant and Weaviate, two Vector DB tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Qdrant | Weaviate |
|---|---|---|
| Category | Vector DB | Vector DB |
| Pricing | FREEMIUM | FREEMIUM |
| License | Open core | Open core |
| Deployment | Hybrid | Hybrid |
| Platforms | API | API |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | Qdrant | Weaviate |
| Capabilities |
|
|
The honest brief
Qdrant
Rust single-binary you can self-host, with payload filtering strong enough that teams pick it for metadata-heavy search.
- Open source, written in Rust
- Self-host or managed cloud
- Strong payload/metadata filtering
- Predictable latency at scale
- More ops than fully-managed rivals
- Smaller ecosystem than Pinecone
- Advanced features lean on managed cloud
Weaviate
Built-in vectorizer modules embed text inline — raw text in, vectors out — so you skip running a separate embedding pipeline.
- Hybrid BM25 + vector search
- Self-hostable or managed cloud
- GraphQL and REST APIs
- Resource-heavy at large scale
- Module config has a learning curve
- Managed tier costs add up
- Newer than some lexical engines
When to pick which
Weaviate leans on Embeddings as a headline capability; Qdrant treats it as secondary.
- Embeddings (primary capability)