Qdrant vs Weaviate (Self-Hosted, 2026): Which Vector Database Should You Run?

For self-hosting, Qdrant is the leaner, faster-to-deploy choice — Apache-2.0, written in Rust, one Docker image, native hybrid search, and strong performance claims. Weaviate is the batteries-included alternative — BSD-3-Clause, written in Go, with built-in vectorizer modules and hybrid search, but it leans on Kubernetes + Helm for production deployments. If you want a vector engine that gets out of your way, pick Qdrant; if you want an opinionated platform with modules included, pick Weaviate. This head-to-head compares them through the lens of a team that runs its own infrastructure.

Both are genuinely open-source and both belong on any shortlist — this isn’t a “winner takes all” matchup. For the wider field, see our best self-hosted vector databases guide.

Side-by-side comparison

	Qdrant	Weaviate
License	Apache-2.0	BSD-3-Clause
Core language	Rust	Go
GitHub stars (June 2026)	32.4k	16.3k
Hybrid search	Native — dense + sparse, RRF/fusion in one query	Built-in — vector + BM25 with fusion in one query
Self-host (small)	Single qdrant/qdrant Docker image	Docker / docker-compose
Self-host (production)	Distributed / clustered	Kubernetes + Helm
Managed cloud	Qdrant Cloud — perpetual free tier; paid usage-based	Weaviate Cloud — free Sandbox; Flex from ~$45/mo
Built-in vectorizer modules	No (you bring embeddings)	Yes (module ecosystem)

Star counts are GitHub’s rounded figures as of June 2026 and drift over time; license and language are the stable facts to weight.

License and language

Both licenses are permissive with no copyleft strings — Qdrant is Apache-2.0, Weaviate is BSD-3-Clause — so for downstream commercial use they’re effectively equivalent. You can embed either in a product without obligation.

The language difference is more interesting in practice. Qdrant is written in Rust, which tends to mean tight memory control and predictable performance with no garbage-collector pauses. Weaviate is written in Go, which is easy to deploy as a single static binary and is the lingua franca of cloud-native infrastructure (it’s the language of Kubernetes itself). Neither language matters for using the database via its API — but if you ever read the source or contribute, this is the difference, and it loosely reflects each project’s design temperament: Qdrant the lean performance engine, Weaviate the cloud-native platform.

Hybrid search

Both support hybrid search — combining dense vector similarity with sparse/keyword matching — in a single query, so neither forces you to hand-roll fusion the way pgvector does.

• Qdrant does native dense + sparse vectors with multiple named vectors per point and configurable fusion (e.g. Reciprocal Rank Fusion). This gives you fine-grained control over how multiple vector representations of the same item are stored and combined.
• Weaviate offers built-in vector + BM25 keyword search with fusion ranking in one query — a clean, well-documented hybrid implementation.

This is close to a tie on capability. Qdrant’s named-vectors model is more flexible if you’re doing something sophisticated with multiple embeddings per object; Weaviate’s BM25 hybrid is straightforward and battle-tested. Both are strong.

Performance and latency

The honest caveat first: vector-DB benchmarks are recall-, dataset-, and hardware-dependent, and are usually run by the vendor that wins them. Read them as directional, not as gospel.

• Qdrant publishes benchmarks claiming the highest RPS and lowest latency in most scenarios, roughly 4× RPS on one dataset, and a particular edge on filtered search (combining vector similarity with metadata filters). Note that benchmark data was last refreshed in 2024.
• Weaviate publishes an ANN benchmark reporting end-to-end p99 latency and QPS-vs-recall curves — interactive, with no single headline number. Arguably the more transparent presentation, since it shows the recall/latency trade-off rather than one cherry-picked figure.

Qdrant markets itself harder on raw speed and filtered-search performance; Weaviate presents the full latency-vs-recall picture. For the vast majority of self-hosted workloads (under a few million vectors), both will be comfortably fast on decent hardware, and your own embedding model and chunking choices will affect end-to-end latency more than the database does. Benchmark on your data before treating performance as the deciding factor.

Self-hosting and operations

This is where the two diverge most, and where the choice usually gets made for self-hosters.

• Qdrant ships as a single official Docker image (qdrant/qdrant). It runs single-node out of the box — docker run and you have a working vector DB — and scales to a distributed/clustered deployment when you need it. This is the lighter operational footprint: one service to run, back up, and monitor.
• Weaviate runs via Docker / docker-compose for development, but production deployments are expected to use Kubernetes + Helm. It also has a module ecosystem (vectorizers, rerankers, generative modules) that can generate embeddings for you inside the database. That’s genuine convenience — you don’t necessarily need a separate embedding service — but it’s also more moving parts to understand and operate.

The trade-off in one sentence: Qdrant gives you less to operate; Weaviate gives you more built in. If you already have an embedding pipeline and want a fast, no-nonsense vector store, Qdrant’s single-image model is hard to beat. If you’d rather the database handle vectorization and you’re already comfortable running Kubernetes, Weaviate’s modules earn their keep.

Cost and pricing

Self-hosting either one means the software is free and your cost is the box it runs on — a small-to-medium index for both sits comfortably on a ~$20–30/mo VPS (cheaper on Hetzner-class hosts), since you control the resource envelope. Weaviate’s production Kubernetes posture can imply a slightly heavier baseline (you’re running a cluster), while Qdrant’s single node keeps the floor low.

For reference, their managed clouds (hosted prices, not self-host costs):

• Qdrant Cloud — perpetual free tier (1-node, 0.5 vCPU / 1 GB RAM / 4 GB disk); paid Standard is usage-based via calculator, with no fixed published entry price.
• Weaviate Cloud — free Serverless Sandbox; paid “Flex” from ~$45/mo, pay-as-you-go (vector storage from ~$0.00465 per 1M dimensions, varies).

If a flat, predictable bill is the goal, self-hosting either on your own VPS wins over usage-metered managed pricing — that’s the whole point of search you own.

When to pick which

Pick Qdrant if:

• You want the lightest self-host footprint — one Docker image, single-node to start.
• Raw speed and filtered search performance matter to you.
• You already produce your own embeddings and just need a fast place to store and query them.
• You prefer Rust-grade predictable performance with no GC pauses.

Pick Weaviate if:

• You want vectorization built into the database via its module ecosystem.
• You’re already running Kubernetes and Helm in production.
• You want an opinionated, batteries-included platform rather than a lean engine.
• BSD-3-Clause vs Apache-2.0 is a wash for you (it is for almost everyone).

Verdict

For most self-hosting teams, Qdrant is the more pragmatic default — permissive license, strong (vendor-claimed) performance especially on filtered search, native hybrid, and a single-image deploy that doesn’t require a platform team. Weaviate is the better fit when you want the database to do more for you — built-in vectorizer modules and an opinionated platform — and you’re already comfortable operating Kubernetes. Both are excellent, genuinely open-source, and safe to embed; the decision comes down to whether you value a lean engine you run simply (Qdrant) or a batteries-included platform with modules (Weaviate).

FAQ

Is Qdrant or Weaviate better for self-hosting? For a lean self-host, Qdrant — it ships as a single Docker image, runs single-node out of the box, and has a smaller operational footprint. Weaviate is better if you want built-in vectorizer modules and are already running Kubernetes for production.

Do both Qdrant and Weaviate support hybrid search? Yes. Both combine dense vector similarity with sparse/keyword matching in a single query. Qdrant uses named vectors with configurable fusion (e.g. RRF); Weaviate offers vector + BM25 with fusion ranking.

Is Qdrant faster than Weaviate? Qdrant’s own benchmarks claim higher RPS, lower latency, and an edge on filtered search, but those were last refreshed in 2024 and, like all vendor benchmarks, are dataset- and hardware-dependent. Weaviate publishes recall-vs-latency curves instead of a single number. For most workloads both are fast — benchmark on your own data before deciding on speed alone.

What license do Qdrant and Weaviate use? Qdrant is Apache-2.0; Weaviate is BSD-3-Clause. Both are permissive with no copyleft, so either is safe to embed in a commercial product.

Do I need Kubernetes to run Weaviate? Not for development — Weaviate runs fine via Docker / docker-compose. But production deployments are designed around Kubernetes + Helm. Qdrant, by contrast, runs comfortably as a single Docker container in production at small-to-medium scale.

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Aquila is the independent guide to private, self-hosted AI search — search you own instead of rent. Compare more options in the best self-hosted vector databases guide, check the pgvector vs Qdrant angle if you’re already on Postgres, or browse all comparisons. Own your search.