Weaviate vs Qdrant
Weaviate
Enterprise-ready, distributed vector database with GraphQL API, advanced filtering, and multi-modal search capabilities.
AI teams building generative applications, RAG pipelines, and multi-modal search systems where development velocity and out-of-box AI features outweigh latency concerns.
Qdrant
High-performance, production-grade vector search engine written in Rust with enterprise-class reliability and scalability.
Production systems prioritizing performance, cost efficiency, and simplicity—ideal for semantic search, recommendation engines, and high-throughput retrieval tasks where minimal latency is critical.
Short Answer
Weaviate excels in multi-modal AI integration with built-in generative modules and broader LLM ecosystem support, while Qdrant offers superior performance with faster query speeds (10-50ms vs 100-500ms) and lower memory overhead, making it ideal for high-throughput production systems.
Our Verdict
AI-assistedChoose Weaviate if you need seamless AI/LLM integration, multi-modal search capabilities, and pre-built generative modules that accelerate development for AI-first applications. Choose Qdrant if you prioritize raw performance, minimal resource consumption, cost efficiency, and need a lightweight vector database for high-traffic production systems with strict latency requirements.
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Choose Weaviate if
AI teams building generative applications, RAG pipelines, and multi-modal search systems where development velocity and out-of-box AI features outweigh latency concerns.
Choose Qdrant if
Production systems prioritizing performance, cost efficiency, and simplicity—ideal for semantic search, recommendation engines, and high-throughput retrieval tasks where minimal latency is critical.
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Key Differences at a Glance
Key Facts & Figures
| Metric | Weaviate | Qdrant | Diff |
|---|---|---|---|
| Estimated Monthly Cost (1M vectors)(USD) | $500-800 (managed) | — | — |
| Time to First Query(minutes) | 30-45 minutes (self-hosted) | 20 minutes | +90% |
| Query Latency (p99)(milliseconds) | 50-150ms | — | — |
| Indexing Methods Supported(count) | 3 methods (HNSW, flat, dynamic) | — | — |
| Average Query Latency (1M vectors, 384-dim)(milliseconds) | 75ms | — | — |
| Integrated LLM Providers(count) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | — | — |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $800 | — | — |
| Maximum Scalability (distributed nodes)(nodes) | 100+ | — | — |
| API Query Language Support(count) | 2 (GraphQL, REST) | — | — |
| Query Throughput(operations per second (QPS)) | 100,000 QPS | — | — |
| Maximum Collection Size(billion vectors) | 2 billion vectors | — | — |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud) | — | — |
| GitHub Community Stars(stars) | 13,000+ stars | — | — |
| Number of Native LLM Integrations(integrations) | 20+ LLM providers | — | — |
| Query Latency (95th percentile)(milliseconds) | 100-500 ms | 10-50 ms | +900% |
| Memory per 1M Vectors(GB) | 8-12 GB | 2-4 GB | +233% |
| Startup Time (empty instance)(seconds) | 20-30 seconds | 2-5 seconds | +614% |
| Built-in LLM Integrations(count) | 15+ providers | 0 (custom only) | — |
| Managed Cloud Base Price (monthly)(USD) | $25/month | $10/month | +150% |
| Throughput (vectors/second insert)(vectors/sec) | 5,000-10,000 | 50,000-100,000 | -90% |
| Maximum Vectors Per Instance(vectors) | 100M+ (distributed) | — | — |
| Average Query Latency(milliseconds) | 50-150ms | — | — |
| Setup Time to First Query(minutes) | 30-60 (with Docker) | — | — |
| GitHub Stars | ~9,500 stars (as of 2026) | 28,000+ stars | -66% |
| Minimum Memory for 1M Vectors(GB) | 4-8GB | — | — |
| Setup Time (First Query)(minutes) | 30-60 minutes | — | — |
| Max Recommended Vector Count(vectors) | 100M+ (distributed) | — | — |
| Estimated Monthly Cost at 100GB(USD) | $25-100 (managed cloud) | $25-100 (managed cloud) | — |
| Vector Dimension Limit(dimensions) | 65,536 | 65,536 | — |
| GitHub Stars/Community Size(stars) | 18,000+ stars | 18,000+ stars | — |
| Query Latency (1M vectors, single query)(milliseconds) | 10-50ms | 10-50ms | — |
| Maximum Practical Dataset Size(vectors) | Billions+ | Billions+ | — |
| Memory Footprint (at rest, 1M vectors)(MB) | ~200MB | ~200MB | — |
| Number of Supported Languages(languages) | Python, JavaScript, Go, Java, Rust, C++, .NET | Python, JavaScript, Go, Java, Rust, C++, .NET | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
Weaviate
100-500ms
Qdrant
10-50ms🏆
Weaviate
Yes (OpenAI, Cohere, HuggingFace modules)🏆
Qdrant
No (requires custom implementation)
Weaviate
8-12 GB
Qdrant
2-4 GB🏆
Weaviate
Open source + managed cloud ($25-500/month)
Qdrant
Open source + managed cloud ($10-300/month)🏆
Weaviate
Yes (text, image, audio via modules)🏆
Qdrant
Limited (vector-only, requires preprocessing)
Weaviate
2,500+ enterprises🏆
Qdrant
1,800+ enterprises
Weaviate
20-30 minutes (higher config)
Qdrant
5-10 minutes (lightweight setup)🏆
Full Comparison
| Attribute | Weaviate | Qdrant |
|---|---|---|
| Free Tier Vector Limit(vectors) | Unlimited (self-hosted) | — |
| Estimated Monthly Cost (1M vectors)(USD) | $500-800 (managed) | — |
| Time to First Query(minutes) | 30-45 minutes (self-hosted) | 20 minutes |
| Maximum Vector Dimensions(dimensions) | Unlimited | — |
| Query Latency (p99)(milliseconds) | 50-150ms | — |
| Indexing Methods Supported(count) | 3 methods (HNSW, flat, dynamic) | — |
| Average Query Latency (1M vectors, 384-dim)(milliseconds) | 75ms | — |
| Query Throughput(operations per second (QPS)) | 100,000 QPS | — |
| GPU Acceleration Support | Limited (planning phase) | — |
Show 4 more attributesQuery Latency (95th percentile)(milliseconds) 100-500 ms 10-50 ms Throughput (vectors/second insert)(vectors/sec) 5,000-10,000 50,000-100,000 Average Query Latency(milliseconds) 50-150ms — Query Latency (1M vectors, single query)(milliseconds) 10-50ms — | ||
| Uptime SLA(percent) | Not guaranteed (self-hosted) | — |
| SLA Uptime Guarantee(%) | Varies by self-hosted setup | — |
| Native Hybrid Search Support(null) | BM25 keyword + vector | — |
| Built-in Hybrid Search Support | Native BM25 + vector search | — |
| Number of Native LLM Integrations(integrations) | 20+ LLM providers | — |
| Hybrid Search Support (BM25 + Vector) | Yes | — |
| Multi-tenancy Support | Native with isolation | — |
Show 3 more attributesQuery Filtering Support Advanced GraphQL + WHERE clauses with boolean logic — Multi-Modal Search Text, image, audio, video — Metadata Filtering Complexity Advanced boolean/range queries — | ||
| Deployment Model | Cloud-managed SaaS + Self-hosted Docker/Kubernetes | — |
| Integrated LLM Providers(count) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | — |
| Built-in LLM Integrations(count) | 15+ providers | 0 (custom only) |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $800 | — |
| Licensing Cost(USD) | $0-5000+/month (SaaS) | — |
| Native Multi-tenancy Support | Yes, with built-in tenant isolation | — |
| Maximum Scalability (distributed nodes)(nodes) | 100+ | — |
| Maximum Collection Size(billion vectors) | 2 billion vectors | — |
| Maximum Vectors Per Instance(vectors) | 100M+ (distributed) | — |
| Max Recommended Vector Count(vectors) | 100M+ (distributed) | — |
| Maximum Practical Dataset Size(vectors) | Billions+ | — |
| API Query Language Support(count) | 2 (GraphQL, REST) | — |
| Setup Time (First Query)(minutes) | 30-60 minutes | — |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud) | — |
| Setup Time to First Query(minutes) | 30-60 (with Docker) | — |
| GitHub Community Stars(stars) | 13,000+ stars | — |
| Memory per 1M Vectors(GB) | 8-12 GB | 2-4 GB |
| Memory Footprint (at rest, 1M vectors)(MB) | ~200MB | — |
| Startup Time (empty instance)(seconds) | 20-30 seconds | 2-5 seconds |
| Supported Deployment Modes | Docker, Kubernetes, Cloud (AWS/GCP/Azure) | — |
| Minimum Setup Infrastructure | Docker/Kubernetes cluster (4GB+ RAM minimum) | — |
| Managed Cloud Base Price (monthly)(USD) | $25/month | $10/month |
| Multi-modal Support (native)(modalities) | 3 (text, image, audio) | 1 (vectors only) |
| GitHub Stars | ~9,500 stars (as of 2026) | 28,000+ stars |
| Minimum Memory for 1M Vectors(GB) | 4-8GB | — |
| Kubernetes Support | Native Kubernetes-ready Helm charts | — |
| LangChain Integration Maturity | Supported but secondary to GraphQL API | — |
| Pricing Model | Self-hosted free or managed from $25/mo | — |
| Estimated Monthly Cost at 100GB(USD) | $25-100 (managed cloud) | — |
| Vector Dimension Limit(dimensions) | 65,536 | — |
| GitHub Stars/Community Size(stars) | 18,000+ stars | — |
| Self-Hosting Available | Yes (open-source) | — |
| Number of Supported Languages(languages) | Python, JavaScript, Go, Java, Rust, C++, .NET | — |
| Open Source License | AGPL v3 (copyleft with commercial option) | — |
| Kubernetes-Native Deployment | Yes; Helm charts, StatefulSet support | — |
| Complex Metadata Filtering Support | Nested fields, range, AND/OR/NOT, geo-spatial | — |
Show 4 more attributes
Show 3 more attributes
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
Weaviate
Pros
- Native integration with 15+ LLM providers (OpenAI, Cohere, HuggingFace, Llama2) eliminates custom pipeline work
- Multi-modal capabilities: search across text, images, and audio without preprocessing
- RESTful and GraphQL APIs provide flexibility for diverse integration patterns
- Generous free tier supports up to 1M vectors in managed cloud
- Strong community (45K GitHub stars) with extensive documentation and tutorials
Cons
- Higher memory consumption (8-12 GB per 1M vectors vs Qdrant's 2-4 GB) increases infrastructure costs
- Query latency 5-10x slower (100-500ms p95) makes real-time applications challenging
- Steeper learning curve due to schema design complexity and module configuration
Qdrant
Pros
- Industry-leading query latency of 10-50ms p95 handles sub-100ms SLA requirements reliably
- 4-6x lower memory footprint (2-4 GB per 1M vectors) reduces cloud infrastructure spend by 30-50%
- Simple, flat-file setup deploys in 5-10 minutes without complex configuration
- Cost-competitive managed pricing ($10-300/month) undercuts Weaviate by 30-40%
- Rust-based architecture provides thread-safe, reliable performance under high concurrent load
Cons
- No built-in generative AI modules require custom LLM integration logic and additional engineering effort
- Limited native multi-modal support necessitates external embedding models and preprocessing pipelines
- Smaller ecosystem (28K GitHub stars) with fewer pre-built integrations and community templates
Frequently Asked Questions
Qdrant significantly outperforms Weaviate with 10-50ms query latency (p95) compared to Weaviate's 100-500ms. For applications requiring <100ms response times (search UIs, recommendation feeds, chatbots), Qdrant is the clear choice. Weaviate's latency is acceptable for batch processing and non-interactive workflows.
Resources & Learn More
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