Qdrant vs Weaviate
A head-to-head comparison of two leading vector databases for AI-powered growth. See how they stack up on pricing, performance, and capabilities.
Qdrant
Pricing: Free tier (1GB), then $25/mo cloud; open-source self-hosted
Best for: Performance-sensitive workloads with complex filtering needs
Weaviate
Pricing: Free sandbox, then $25/mo Serverless; open-source self-hosted
Best for: Hybrid search use cases and teams wanting built-in vectorization
Head-to-Head Comparison
| Criteria | Qdrant | Weaviate |
|---|---|---|
| Setup Complexity | Low cloud; moderate self-hosted (single binary or Docker) | Moderate — module-based config, schema required |
| Cost at 1M Vectors | ~$25/mo cloud; free self-hosted | ~$25/mo serverless; free self-hosted |
| Query Latency | ~1-10ms p99 (Rust, SIMD optimized) | ~5-25ms p99 (Go + Java modules add overhead) |
| Hybrid Search | Named vectors for multi-vector hybrid; sparse native support | BM25 + vector hybrid natively; multiple search modes |
| Scaling Ceiling | Billions of vectors; horizontal sharding | Billions of vectors; strong multi-tenancy |
The Verdict
Qdrant is the performance leader — its Rust core and SIMD-optimized distance calculations deliver the lowest latency of any open-source vector database, making it ideal for latency-sensitive applications. Weaviate offers a richer feature surface: its BM25 hybrid search is more mature and its module system enables automatic vectorization at ingest without a separate embedding step. Teams optimizing for raw throughput should lean Qdrant; teams that want an all-in-one search platform with less embedding pipeline code should lean Weaviate.
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