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Weaviatevs
Qdrant
Weaviate vs Qdrant
A detailed look at how Weaviate compares to Qdrant.
WeaviateQdrantKey Differentiators
Key Weaviate Strengths
- Built-in vectorization modules: text2vec, img2vec, multi2vec for automatic embedding.
- GraphQL + REST APIs with powerful hybrid BM25 + vector search.
- Generative search module for RAG directly within queries.
- Multi-tenancy support designed for SaaS applications.
Key Qdrant Strengths
- Written in Rust for memory safety and high performance.
- Named vectors: multiple vector spaces per point for multi-modal data.
- Rich payload filtering with nested objects, geo, and full-text match.
- Flexible quantization options (scalar, product, binary) for cost optimization.
Weaviate and Qdrant are both excellent open-source vector databases with managed cloud offerings. Weaviate differentiates with built-in vectorization modules and generative search. Qdrant differentiates with Rust-based performance, named multi-vectors, and rich payload filtering. Both are production-ready.
Weaviate vs. Qdrant
Architecture & Design
| Feature / Dimension | Weaviate | Qdrant |
|---|---|---|
| Language | Go (with C extensions for HNSW) | Rust |
| License | BSD-3-Clause | Apache 2.0 |
| API Style | GraphQL (primary) + REST + gRPC | REST + gRPC |
| Storage | Custom LSM-tree based storage engine | Custom storage with memory-mapped files and WAL |
| Replication | Raft-based replication (v1.25+) | Raft-based replication across distributed cluster |
| Multi-Tenancy | Native multi-tenant classes with per-tenant CRUD and activity management | Via payload-based filtering or separate collections; no native tenant isolation |
Features & Capabilities
| Feature / Dimension | Weaviate | Qdrant |
|---|---|---|
| Built-in Vectorization | Yes - text2vec-openai, text2vec-cohere, text2vec-huggingface, img2vec-neural, multi2vec-clip | No - bring your own embeddings (integrations via FastEmbed library) |
| Generative Search | Built-in generative module (RAG within queries) | Not built-in; implement RAG in application layer |
| Named Vectors | Supported (v1.24+) but newer feature | Core feature since early versions; mature multi-vector support |
| Filtering | Where filters on properties; supports nested refs | Rich payload filtering: nested objects, geo, datetime, full-text, range |
| Quantization | Product quantization (PQ), binary quantization (BQ) | Scalar quantization, product quantization, binary quantization |
| Hybrid Search | BM25 keyword + vector with fusion algorithms (ranked, relative score) | Sparse + dense vectors; combine via search API with prefetch |
Pricing & Deployment
| Feature / Dimension | Weaviate | Qdrant |
|---|---|---|
| Self-Hosted | Free (open-source); Docker, Kubernetes, Helm charts | Free (open-source); Docker, Kubernetes, single binary |
| Managed Cloud | Weaviate Cloud: starts at ~$25/mo for serverless; dedicated from ~$180/mo | Qdrant Cloud: starts at ~$9/mo (0.5GB RAM); scales per resource usage |
| Free Cloud Tier | Sandbox cluster (14 days, limited) | 1GB free cluster (no time limit) |
| Enterprise | Weaviate Enterprise with dedicated support, SLA, BYOC | Qdrant Enterprise with dedicated clusters, SLA, on-premises options |
| Cost at 10M Vectors | Self-hosted: infra only (~$50-200/mo); Cloud: ~$100-400/mo | Self-hosted: infra only (~$40-150/mo); Cloud: ~$60-250/mo |
Ecosystem & Community
| Feature / Dimension | Weaviate | Qdrant |
|---|---|---|
| GitHub Stars | 12K+ stars | 21K+ stars |
| SDKs | Python, TypeScript, Go, Java | Python, TypeScript/JS, Rust, Go, Java, .NET |
| LLM Frameworks | LangChain, LlamaIndex, Haystack, Semantic Kernel | LangChain, LlamaIndex, Haystack, Semantic Kernel, CrewAI |
| Community | Active Slack community, regular blog posts and podcasts | Active Discord community, regular blog posts and tutorials |
Bottom Line: Weaviate vs. Qdrant
| Feature / Dimension | Weaviate | Qdrant |
|---|---|---|
| Choose Weaviate if | You want built-in vectorization, generative search, and native multi-tenancy for SaaS | Not ideal if you need bring-your-own-embedding simplicity or Rust-level memory efficiency |
| Choose Qdrant if | Not ideal if you want built-in embedding generation and generative modules | You want maximum performance per node, rich filtering, named vectors, and flexible quantization |
| Performance | Strong at scale with efficient resource usage | Consistently high single-node throughput due to Rust implementation |
| Bottom Line | More batteries-included with modules for vectorization and generation | More focused on being the best vector store with rich data modeling |
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