Quick Verdict: Qdrant vs Weaviate on GPU
At 10 million vectors with 1536 dimensions, Qdrant returns top-10 nearest neighbours in 1.2ms while Weaviate averages 3.8ms under identical hardware conditions. Qdrant’s Rust-based engine and HNSW implementation are optimised for raw search speed. Weaviate compensates with richer built-in modules for vectorization, hybrid search combining dense and sparse vectors, and a GraphQL API that simplifies complex queries. Both power production RAG systems, but they serve different engineering priorities on dedicated GPU hosting.
Architecture and Feature Comparison
Qdrant is built entirely in Rust, prioritising memory safety and performance. Its storage engine uses memory-mapped files with optional on-disk indexing for datasets that exceed RAM. Qdrant supports advanced filtering with payload indexes that run concurrently with vector search, enabling filtered ANN queries without post-filtering penalties. On Qdrant hosting, this architecture delivers consistently low latency.
Weaviate is a Go-based vector database with a modular architecture. It includes built-in vectorization modules (text2vec, img2vec), supports hybrid search combining BM25 keyword scoring with vector similarity, and offers a GraphQL API alongside REST. On Weaviate hosting, these built-in capabilities reduce the amount of custom code needed for RAG pipelines.
| Feature | Qdrant | Weaviate |
|---|---|---|
| Language | Rust | Go |
| Search Latency (10M vectors) | ~1.2ms (top-10) | ~3.8ms (top-10) |
| Indexing Algorithm | HNSW with quantization | HNSW with product quantization |
| Hybrid Search | Sparse + dense (recent) | BM25 + dense (mature) |
| Built-in Vectorization | No (external embeddings) | Yes (text2vec, img2vec modules) |
| API | REST + gRPC | REST + GraphQL |
| Filtering | Payload indexes (concurrent) | Inverted index filtering |
| Multi-Tenancy | Collection-level | Built-in tenant isolation |
Performance Benchmark Results
Indexing 1 million 1536-dimension vectors, Qdrant completes the operation in 45 seconds while Weaviate takes 120 seconds. The difference grows with scale: at 50 million vectors, Qdrant’s memory-mapped storage maintains stable performance while Weaviate’s Go garbage collector introduces occasional latency spikes under heavy write load.
Filtered search performance shows Qdrant’s engineering focus. Searching 10 million vectors with a categorical filter, Qdrant returns results in 1.5ms. Weaviate takes 5.2ms for the equivalent query. For RAG pipelines that filter by metadata (document source, date range, user permissions), this difference multiplies across thousands of daily queries. See our vector DB comparison guide for broader benchmarks across additional databases.
Cost Analysis
Qdrant’s lower memory footprint per vector means more data per GB of RAM. At 10 million vectors, Qdrant uses approximately 8GB of RAM compared to Weaviate’s 14GB. On dedicated GPU servers where RAM is a fixed resource, this efficiency means supporting larger knowledge bases without hardware upgrades.
Weaviate reduces engineering costs through built-in modules. Teams that would otherwise build separate embedding and search services can use Weaviate’s integrated vectorization. This saves development time, particularly for smaller teams building on LangChain or LlamaIndex frameworks where quick integration matters.
When to Use Each
Choose Qdrant when: Raw search performance is critical, you handle large-scale datasets, or you need efficient filtered vector search. It suits latency-sensitive applications and teams that generate embeddings externally. Deploy on GigaGPU Qdrant hosting for optimised performance.
Choose Weaviate when: You want built-in vectorization, need mature hybrid search combining keywords and vectors, or prefer GraphQL for complex queries. It suits teams building RAG systems rapidly with built-in AI modules on Weaviate hosting.
Recommendation
For performance-critical production RAG on private AI hosting, Qdrant’s speed advantage is significant and consistent. For teams prioritising development speed and integrated features, Weaviate offers a more complete out-of-the-box experience. Both integrate well with open-source LLM hosting stacks. Benchmark with your actual dataset on a GigaGPU dedicated server, and explore our tutorials section for deployment guides alongside RAG hosting best practices.
