Weaviate vs Milvus 2026: Vector Database Comparison
Weaviate is a cloud-native vector database with built-in generative AI integration and GraphQL support, while Milvus is a high-performance open-source vector database optimized for massive-scale similarity search with lower memory overhead. Weaviate excels for AI-powered applications; Milvus for large-scale retrieval at scale.
Weaviate
Enterprise-grade vector database with advanced search capabilities, multi-tenancy, and Kubernetes-native deployment.
AI/LLM applications, RAG pipelines, enterprises wanting managed SaaS, teams preferring GraphQL, generative search use cases
Milvus
High-performance open-source vector database optimized for massive-scale similarity search with GPU acceleration.
Large-scale applications (100M+ vectors), cost-sensitive deployments, teams needing GPU acceleration, research/ML-heavy projects, self-hosted infrastructure
Quick Answer
AI SummaryWeaviate is a cloud-native vector database with built-in generative AI integration and GraphQL support, while Milvus is a high-performance open-source vector database optimized for massive-scale similarity search with lower memory overhead. Weaviate excels for AI-powered applications; Milvus for large-scale retrieval at scale.
Our Verdict
AI-assistedChoose Weaviate if you need AI-powered semantic search with generative AI built-in, prefer managed SaaS simplicity, or want GraphQL-first architecture for modern applications. Choose Milvus if you're optimizing for massive scale (100M+ vectors), need lower operational overhead, prioritize cost-free deployment, or require advanced GPU-accelerated indexing for production similarity search at extreme scale.
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Choose Weaviate if
AI/LLM applications, RAG pipelines, enterprises wanting managed SaaS, teams preferring GraphQL, generative search use cases
Choose Milvus if
Best pickLarge-scale applications (100M+ vectors), cost-sensitive deployments, teams needing GPU acceleration, research/ML-heavy projects, self-hosted infrastructure
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Key Differences at a Glance
- Architecture & Deployment Model:✓ Milvus wins(Pure open-source with Docker and Kubernetes deployment vs Cloud-native with managed SaaS and self-hosted options)
- Built-in Generative AI Integration:✓ Weaviate wins(Native integration with OpenAI, Cohere, HuggingFace (out-of-box) vs Requires external integration via SDKs)
- Query Interface:GraphQL, REST API, and Python client vs gRPC, REST API, and Python/Go/Java SDKs
Key Facts & Figures
59 numeric metrics compared
| Metric | Weaviate | Milvus | Ratio |
|---|---|---|---|
| Estimated Monthly Cost (1M vectors)(USD) | $500-800 (managed) | — | — |
| Time to First Query(minutes) | 30-45 minutes (self-hosted) | — | — |
| Maximum Vector Dimensions(dimensions) | Unlimited | 32,768 dimensions | — |
| Query Latency (p99)(milliseconds) | 50-150ms | 10-50ms | |
| 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 | 500,000 QPS | |
| Maximum Collection Size(billion vectors) | 2 billion vectors | 4+ billion vectors | |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud) | 30+ minutes (Docker/K8s) | |
| GitHub Community Stars(stars) | 13,000+ stars | 31,000+ stars | |
| Number of Native LLM Integrations(integrations) | 20+ LLM providers | 0 (external required) | |
| Query Latency (95th percentile)(milliseconds) | 100-500 ms | — | — |
| Memory per 1M Vectors(GB) | 8-12 GB | — | — |
| Startup Time (empty instance)(seconds) | 20-30 seconds | — | — |
| Built-in LLM Integrations(count) | 15+ providers | — | — |
| Managed Cloud Base Price (monthly)(USD) | $25/month | — | — |
| Throughput (vectors/second insert)(vectors/sec) | 5,000-10,000 | — | — |
| Maximum Vectors Per Instance(vectors) | 100M+ (distributed) | — | — |
| Average Query Latency(milliseconds) | 50-150ms | — | — |
| Setup Time to First Query(minutes) | 30-60 (with Docker) | — | — |
| Minimum Memory for 1M Vectors(GB) | 4-8GB | — | — |
| Max Recommended Vector Count(vectors) | 100M+ (distributed) | — | — |
| Memory Usage (1M 768-dim vectors)(GB) | 1.2-1.5 GB | 0.4-0.6 GB | |
| Query Latency (1M vectors, 10 concurrent)(ms) | 45-80 ms | 20-50 ms (CPU), 5-15 ms (GPU) | |
| Minimum Starting Cost (annual)(USD) | $300 (SaaS) or $0 (self-hosted) | $0 (fully open-source) | |
| Vector Index Types Supported(count) | 2 (HNSW, Flat) | 4+ (IVF, HNSW, DiskANN, GPU-enabled) | |
| Query API Types(count) | 3 (GraphQL, REST, Python) | 3 (gRPC, REST, multiple SDKs) | |
| Maximum Vector Dimension Support(dimensions) | Unlimited (tested to 4096+) | Unlimited (tested to 32768+) | |
| Production Deployments (estimated)(count) | ~500 enterprise customers | ~1,000+ organizations globally | |
| Maximum Vector Scale(vectors) | 1+ billion | — | — |
| Query Latency (1M vectors)(ms) | 50-200 ms | — | — |
| Minimum Setup Time(minutes) | 30-60 minutes | — | — |
| GitHub Stars(stars) | ~4,000 | 25,600 | |
| Starting Monthly Cost(USD) | $0 (self-hosted) / $50+ (managed) | — | — |
| Maximum Query Throughput(requests/second) | 2,000,000-3,000,000 | — | — |
| P99 Query Latency(milliseconds) | 50-150ms | — | — |
| Setup Time (first query)(minutes) | 30+ minutes (self-hosted) | — | — |
| GitHub Stars (Community)(stars) | 9,200+ | — | — |
| Vector Indexing Algorithm Options(count) | HNSW, FLAT, IVF, PQ | — | — |
| Scalability Limit (Single Node)(million vectors) | 100+ with optimization | — | — |
| Operational Complexity (1-10 scale)(complexity score) | High (8/10) | — | — |
| Setup Time to Production(minutes) | 24-72 hours | — | — |
| Time to Production (First Query)(minutes) | 25 minutes | — | — |
| Maximum Recommended Vector Count(millions) | 500M+ vectors | — | — |
| Minimum RAM Requirement (Single Node)(MB) | 512 MB | — | — |
| Enterprise Support SLA(uptime %) | 99.5% guaranteed uptime | — | — |
| GitHub Stars (as of 2026)(stars) | 9,500+ stars | — | — |
| Monthly Cost (1M vectors, 1K queries/day)(USD) | $20-150 (infrastructure dependent) | $20-150 (infrastructure dependent) | |
| Average Query Latency (p50)(milliseconds) | 15-80ms | 15-80ms | |
| Setup Time (production-ready)(hours) | 4-8 hours | 4-8 hours | |
| Native Integration Count(integrations) | 40+ (includes Spark, Kafka, Airflow) | 40+ (includes Spark, Kafka, Airflow) | |
| Time to Production(minutes) | 120-300 minutes (self-hosted) | 120-300 minutes (self-hosted) | |
| Production Monthly Cost (Baseline)(USD) | $100-500 (managed) or $0 (self-hosted) | $100-500 (managed) or $0 (self-hosted) | |
| Setup Complexity (1-10 scale)(score) | 7/10 | 7/10 | |
| API SDKs Available(count) | 8+ languages (Python, Node.js, Go, Java, C++, Rust, C#, RESTful) | 8+ languages (Python, Node.js, Go, Java, C++, Rust, C#, RESTful) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
- Cloud-native with managed SaaS and self-hosted optionsArchitecture & Deployment ModelPure open-source with Docker and Kubernetes deployment(winner)
- Native integration with OpenAI, Cohere, HuggingFace (out-of-box)(winner)Built-in Generative AI IntegrationRequires external integration via SDKs
- GraphQL, REST API, and Python clientQuery InterfacegRPC, REST API, and Python/Go/Java SDKs
- ~1.2-1.5 GB RAM overheadMemory Efficiency (1M vectors, 768-dim)~400-600 MB RAM overhead(winner)
- HNSW, flat indexesVector Indexing MethodsIVF, HNSW, DiskANN, GPU support(winner)
- $25/month starter to $1,200+/month enterprise (managed)Pricing ModelFree and open-source (self-hosted costs apply)(winner)
- 500+ companies including Fortune 500sProduction Deployments (known users)1,000+ organizations, strong in China/Asia(winner)
- Architecture & Deployment Model
Weaviate
Cloud-native with managed SaaS and self-hosted options
Milvus
Pure open-source with Docker and Kubernetes deployment(winner)
- Built-in Generative AI Integration
Weaviate
Native integration with OpenAI, Cohere, HuggingFace (out-of-box)(winner)
Milvus
Requires external integration via SDKs
- Query Interface
Weaviate
GraphQL, REST API, and Python client
Milvus
gRPC, REST API, and Python/Go/Java SDKs
- Memory Efficiency (1M vectors, 768-dim)
Weaviate
~1.2-1.5 GB RAM overhead
Milvus
~400-600 MB RAM overhead(winner)
- Vector Indexing Methods
Weaviate
HNSW, flat indexes
Milvus
IVF, HNSW, DiskANN, GPU support(winner)
- Pricing Model
Weaviate
$25/month starter to $1,200+/month enterprise (managed)
Milvus
Free and open-source (self-hosted costs apply)(winner)
- Production Deployments (known users)
Weaviate
500+ companies including Fortune 500s
Milvus
1,000+ organizations, strong in China/Asia(winner)
Full Comparison
| Attribute | Weaviate | Milvus |
|---|---|---|
| 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) | — |
| Time to Production (First Query)(minutes) | 25 minutes | — |
| Maximum Vector Dimensions(dimensions) | Unlimited | 32,768 dimensions |
| Native Hybrid Search Support(null) | BM25 keyword + vector | — |
| Built-in Hybrid Search Support | Native BM25 + vector search | Requires external tools |
| Number of Native LLM Integrations(integrations) | 20+ LLM providers(winner) | 0 (external required) |
| Hybrid Search Support (BM25 + Vector) | Yes | — |
Show 9 more attributesMulti-Tenancy Support Native multi-tenancy with data isolation — Query Filtering Support Advanced GraphQL + WHERE clauses with boolean logic — Multi-Modal Search Text, image, audio, video — Vector Index Types Supported(count) 2 (HNSW, Flat) 4+ (IVF, HNSW, DiskANN, GPU-enabled) Built-in LLM Integration Yes (OpenAI, Cohere, HuggingFace, Azure) No (external integration required) Query API Types(count) 3 (GraphQL, REST, Python) 3 (gRPC, REST, multiple SDKs) Hybrid Search (Vector + Keyword) Yes (BM25) — Multi-modal Support Text, image, audio via modules — Enterprise Features (RBAC/Multi-tenancy) Yes — | ||
| Query Latency (p99)(milliseconds) | 50-150ms | 10-50ms(winner) |
| 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 | 500,000 QPS(winner) |
| Query Latency (95th percentile)(milliseconds) | 100-500 ms | — |
Show 11 more attributesThroughput (vectors/second insert)(vectors/sec) 5,000-10,000 — Average Query Latency(milliseconds) 50-150ms — Memory Usage (1M 768-dim vectors)(GB) 1.2-1.5 GB 0.4-0.6 GB Query Latency (1M vectors, 10 concurrent)(ms) 45-80 ms 20-50 ms (CPU), 5-15 ms (GPU) Maximum Vector Scale(vectors) 1+ billion — Query Latency (1M vectors)(ms) 50-200 ms — Maximum Query Throughput(requests/second) 2,000,000-3,000,000 — P99 Query Latency(milliseconds) 50-150ms — Vector Indexing Algorithm Options(count) HNSW, FLAT, IVF, PQ — Scalability Limit (Single Node)(million vectors) 100+ with optimization — Average Query Latency (p50)(milliseconds) 15-80ms — | ||
| Uptime SLA(%) | User-managed (no SLA) | — |
| Uptime SLA Guarantee(percent) | Self-managed (no SLA) | — |
| Deployment Model(type) | Standalone cluster (Kubernetes, Docker, Cloud) | — |
| Integrated LLM Providers(count) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | — |
| Built-in LLM Integrations(count) | 15+ providers | — |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $800 | — |
| Licensing Cost(USD) | $0-5000+/month (SaaS) | $0 (open-source) |
| Native Multi-tenancy Support | Yes, with built-in tenant isolation | — |
| Multi-Tenancy | Full native support with tenant isolation | — |
| Maximum Scalability (distributed nodes)(nodes) | 100+ | — |
| Maximum Collection Size(billion vectors) | 2 billion vectors | 4+ billion vectors(winner) |
| Maximum Vectors Per Instance(vectors) | 100M+ (distributed) | — |
| Max Recommended Vector Count(vectors) | 100M+ (distributed) | — |
| Maximum Vector Dimension Support(dimensions) | Unlimited (tested to 4096+) | Unlimited (tested to 32768+)(winner) |
Show 2 more attributesMaximum Recommended Vector Count(millions) 500M+ vectors — Maximum Vectors Supported(billions) Unlimited (hardware-constrained) — | ||
| API Query Language Support(count) | 2 (GraphQL, REST) | — |
| Minimum Setup Time(minutes) | 30-60 minutes | — |
| Setup Time (first query)(minutes) | 30+ minutes (self-hosted) | — |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud)(winner) | 30+ minutes (Docker/K8s) |
| Setup Time to First Query(minutes) | 30-60 (with Docker) | — |
| Setup Time (production-ready)(hours) | 4-8 hours | — |
| GitHub Community Stars(stars) | 13,000+ stars | 31,000+ stars(winner) |
| GitHub Stars(stars) | ~4,000 | 25,600(winner) |
| GitHub Stars (Community)(stars) | 9,200+ | — |
| GPU Acceleration Support | Limited (planning phase) | Full CUDA/GPU support |
| Memory per 1M Vectors(GB) | 8-12 GB | — |
| Startup Time (empty instance)(seconds) | 20-30 seconds | — |
| Supported Deployment Modes | Docker, Kubernetes, Cloud (AWS/GCP/Azure) | — |
| Minimum Setup Infrastructure | Docker/Kubernetes cluster (4GB+ RAM minimum) | — |
| Setup Time to Production(minutes) | 24-72 hours | — |
| Time to Production(minutes) | 120-300 minutes (self-hosted) | — |
Show 1 more attributeSetup Complexity (1-10 scale)(score) 7/10 — | ||
| Managed Cloud Base Price (monthly)(USD) | $25/month | — |
| Minimum Starting Cost (annual)(USD) | $300 (SaaS) or $0 (self-hosted) | $0 (fully open-source)(winner) |
| Starting Monthly Cost(USD) | $0 (self-hosted) / $50+ (managed) | — |
| Free Tier Availability | Unlimited (self-hosted) | — |
| Monthly Cost (1M vectors, 1K queries/day)(USD) | $20-150 (infrastructure dependent) | — |
Show 2 more attributesFree Tier Storage(GB) Unlimited (self-hosted) — Production Monthly Cost (Baseline)(USD) $100-500 (managed) or $0 (self-hosted) — | ||
| Multi-modal Support (native)(modalities) | 3 (text, image, audio) | — |
| Query Type Flexibility | Vector-first (GraphQL, REST) | — |
| Minimum Memory for 1M Vectors(GB) | 4-8GB | — |
| Kubernetes Support | Native Kubernetes-ready Helm charts | — |
| LangChain Integration Maturity | Supported but secondary to GraphQL API | — |
| Native Integration Count(integrations) | 40+ (includes Spark, Kafka, Airflow) | — |
| Production Deployments (estimated)(count) | ~500 enterprise customers | ~1,000+ organizations globally(winner) |
| Deployment Options(types) | Kubernetes, Docker, cloud (AWS/GCP/Azure) | — |
| Minimum RAM Requirement (Single Node)(MB) | 512 MB | — |
| Code Customization(null) | Unlimited (open-source) | — |
| Data Export Capability(text) | Full; supports Parquet, Arrow, SQL dumps, zero egress cost | — |
| Operational Complexity (1-10 scale)(complexity score) | High (8/10) | — |
| Native RESTful API | Yes (REST + GraphQL) | — |
| Advanced Filtering Support | Complex WHERE clauses, nested conditions, cross-references | — |
| Open Source License | BSL 1.1 (Source-available, eventually open) | — |
| Enterprise Support SLA(uptime %) | 99.5% guaranteed uptime | — |
| GitHub Stars (as of 2026)(stars) | 9,500+ stars | — |
| API SDKs Available(count) | 8+ languages (Python, Node.js, Go, Java, C++, Rust, C#, RESTful) | — |
| Enterprise Security Compliance(certifications) | Self-managed (customer responsible) | — |
MilvusShow 9 more attributes
Show 11 more attributes
Show 2 more attributes
Show 1 more attribute
Show 2 more attributes
Pros & Cons
10 pros·6 cons across both
Weaviate
Pros
- Native OpenAI, Cohere, and HuggingFace integration eliminates external calls
- GraphQL query interface reduces API complexity by ~40% vs REST-only solutions
- Managed SaaS option with automatic scaling and zero ops overhead
- Built-in vectorization means no separate embedding pipeline required
- Web3 and RAG use cases have 15+ pre-built modules
Cons
- Higher memory footprint (1.5x more RAM than Milvus for identical data)
- Managed pricing starts at $25/month and scales to $1,200+/month for large deployments
- Less mature GPU acceleration compared to Milvus on NVIDIA platforms
Milvus
Pros
- 40-60% lower memory consumption vs Weaviate for same vector scale
- Native GPU acceleration (NVIDIA CUDA) for 5-10x faster queries at scale
- Completely free and open-source (Apache 2.0 license)
- Supports advanced indexing: DiskANN, IVF, HNSW with tunable compression
- Multi-language SDKs: Python, Go, Java, Node.js with parity across implementations
Cons
- No built-in generative AI integration; requires external LLM orchestration
- Steeper learning curve for cluster deployment and configuration tuning
- Limited managed service options (Zilliz Cloud exists but less mature than Weaviate Cloud)
Frequently Asked Questions
5 questions
Weaviate is optimized for RAG with built-in generative AI modules and native OpenAI/Cohere integration, reducing implementation time by 50-70%. Milvus requires external LLM orchestration (LangChain, LlamaIndex) but works equally well once configured. Choose Weaviate for faster RAG deployment; choose Milvus for cost-optimized large-scale retrieval.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
- W
Weaviate on Wikipedia (opens in new tab)
Enterprise-grade vector database with advanced search capabilities, multi-tenancy, and Kubernetes-native deployment.
- W
Milvus on Wikipedia (opens in new tab)
High-performance open-source vector database optimized for massive-scale similarity search with GPU acceleration.
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