AI StackPicker

Qdrant vs Weaviate (2026)

A neutral, data-driven comparison — live metrics, use-case guidance and FAQ to help you pick.

At a glance

Qdrant ★32,389
Weaviate ★16,337
Qdrant ⬇23,963,183
Weaviate ⬇160,772,415
QdrantWeaviate
GitHub stars32,38916,337
PyPI downloads / mo23,963,183160,772,415
LicenseApache-2.0BSD-3-Clause
Typedatabasedatabase
Self-hostableYesYes
Managed optionQdrant CloudWeaviate Cloud
Latest releasev1.18.2v1.38.0
Try Qdrant Cloud → Try Weaviate Cloud →

Which should you choose?

Choose Qdrant if…

  • You want a lean, fast, single-binary vector DB (Rust core)
  • Advanced metadata filtering / payload search is central to your use case
  • You prefer minimal moving parts when self-hosting

Choose Weaviate if…

  • You want built-in vectorization modules (text2vec, multimodal) — no separate embedding step
  • Native hybrid (keyword + vector) search out of the box
  • You like a GraphQL API and a batteries-included feel

Both are production-grade and strong choices for RAG. Qdrant leans lean-and-fast; Weaviate leans batteries-included. If you already run your own embedding pipeline, Qdrant keeps the stack simple; if you want the database itself to handle vectorization and hybrid search, Weaviate does more out of the box.

💡 Engineer's take: A few operational notes from running these: Qdrant is the simpler one day-to-day — reach for it when you want high-performance, low-latency vector search, heavy metadata filtering, or a Go-friendly stack. Two gotchas: Qdrant needs a POSIX-compliant filesystem, and on Windows the config-file path must avoid spaces and non-ASCII (e.g. Chinese) characters; give it at least ~2 GB of available RAM for stable operation. Pick Weaviate when you need knowledge-graph-style relations, multimodal (text + image) data, or want the database to handle vectorization itself to simplify your architecture — but keep an eye on its disk usage, since running low can flip the cluster into read-only mode, which will bite you in production.

What they are

Qdrant: Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

Weaviate: Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.

FAQ

Is Qdrant faster than Weaviate?

Both are high-performance, built in compiled languages (Qdrant in Rust, Weaviate in Go). Real-world latency depends heavily on dataset size, vector dimensions, filtering and hardware — benchmark on your own workload rather than trusting any single published number.

Which is better for RAG?

Either works well. Weaviate ships built-in vectorization and hybrid search, which can simplify a RAG stack; Qdrant pairs cleanly with your own embedding pipeline and is strong at filtered search. Both offer a managed cloud.

Can I self-host both Qdrant and Weaviate?

Yes. Both are open-source — Qdrant under Apache-2.0, Weaviate under BSD-3-Clause — and both also offer a managed cloud (Qdrant Cloud, Weaviate Cloud).

Qdrant vs Weaviate — which is more popular?

By GitHub stars, Qdrant currently leads. But popularity is not the same as fit — see the use-case breakdown above to match the tool to your project.

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