Database / Qdrant Vector DB Interview questions
What is the difference between Qdrant and Weaviate?
Both are open-source vector databases with self-hosted and managed cloud options, but they differ in core data modeling philosophy and some architectural emphases.
| Qdrant | Weaviate |
| Rust core; collections of points with flexible JSON payloads. | Go core; schema-driven classes with defined properties. |
| Payload schema is flexible; fields are typically indexed opt-in. | Encourages more explicit upfront schema definition per class. |
| Built-in scalar, binary, and product quantization options. | Also supports quantization, with its own configuration model. |
| Hybrid search via the Query API's fusion methods (RRF, DBSF). | Hybrid search natively supported, with its own fusion/ranking approach. |
Both are commonly shortlisted together for production RAG systems, and the practical choice often comes down to team familiarity, specific quantization or filtering feature needs, and how a team prefers to model its data — more schema-flexible (Qdrant's payload style) versus more explicitly schema-driven (Weaviate's class style).
What language is Weaviate's core primarily written in?
What data modeling style does Weaviate encourage compared to Qdrant?
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