Decision comparison
Milvus vs Weaviate
Milvus and Weaviate are both strong open-source vector databases, but they serve different needs. We recommend Milvus for teams that need extreme scale with tens of billions of vectors and prefer a cloud-native distributed architecture with separated storage and computation. We recommend Weaviate for teams building RAG and AI-powered search applications who want transparent pricing, built-in ML model integrations, and a managed cloud service with clear tier options starting at $45 per month.
Direct comparison. These are reviewed substitutes bought for the same job, so the differences below are the ones that decide between them.
All 2 are vector databases.
Quick Comparison
| Decision factor | Milvus | Weaviate |
|---|---|---|
| Best For | Teams needing maximum scale with tens of billions of vectors and custom infrastructure | Teams building RAG and AI search apps who want managed infrastructure with transparent pricing |
| Pricing Model | Milvus is free and open source, and free to self-host. Fully managed Milvus is a separate product, Zilliz Cloud, which publishes its own serverless and dedicated tiers and quotes enterprise deployments. | Open source for self-hosting; Weaviate Cloud Free is always $0/month. Flex starts at $45/month (monthly pay-as-you-go); Premium starts at $400/month (prepaid). Flex and Premium minimums include the baseline cluster, vector dimensions, and storage; backups are additional. |
| Deployment Options | Milvus Lite for prototyping, Standalone for single-machine production, Distributed for enterprise scale | Cloud managed (Free, Flex, and Premium plans), self-hosted via Docker or Kubernetes, BYOC on Premium |
| Search Capabilities | Global Index for vector similarity, metadata filtering, hybrid search, and multi-vector support | Built-in hybrid search combining vector and BM25 keyword search, plus out-of-the-box RAG |
| Scalability | Cloud-native distributed architecture with stateless components scaling to tens of billions of vectors | Billion-scale architecture with native multi-tenancy, vector index compression, and auto-scaling |
| Ease of Getting Started | Install with pip, create collections and run searches in seconds with Python SDK | Spin up a cloud cluster in minutes with SDKs for Python, Go, TypeScript, and JavaScript |
Milvus
- Best For:
- Teams needing maximum scale with tens of billions of vectors and custom infrastructure
- Pricing Model:
- Milvus is free and open source, and free to self-host. Fully managed Milvus is a separate product, Zilliz Cloud, which publishes its own serverless and dedicated tiers and quotes enterprise deployments.
- Deployment Options:
- Milvus Lite for prototyping, Standalone for single-machine production, Distributed for enterprise scale
- Search Capabilities:
- Global Index for vector similarity, metadata filtering, hybrid search, and multi-vector support
- Scalability:
- Cloud-native distributed architecture with stateless components scaling to tens of billions of vectors
- Ease of Getting Started:
- Install with pip, create collections and run searches in seconds with Python SDK
Weaviate
- Best For:
- Teams building RAG and AI search apps who want managed infrastructure with transparent pricing
- Pricing Model:
- Open source for self-hosting; Weaviate Cloud Free is always $0/month. Flex starts at $45/month (monthly pay-as-you-go); Premium starts at $400/month (prepaid). Flex and Premium minimums include the baseline cluster, vector dimensions, and storage; backups are additional.
- Deployment Options:
- Cloud managed (Free, Flex, and Premium plans), self-hosted via Docker or Kubernetes, BYOC on Premium
- Search Capabilities:
- Built-in hybrid search combining vector and BM25 keyword search, plus out-of-the-box RAG
- Scalability:
- Billion-scale architecture with native multi-tenancy, vector index compression, and auto-scaling
- Ease of Getting Started:
- Spin up a cloud cluster in minutes with SDKs for Python, Go, TypeScript, and JavaScript
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Milvus | Weaviate |
|---|---|---|
| Docker Hub pulls(Product adoption) | 78.9M | 22.1M |
| GitHub commits, 90d(Product adoption) | 718 | 3.6k |
| GitHub stars(Product adoption) | 46,000+ | 16,000+ |
| Search interest(Market interest) | 2 | 1 |
| Hacker News mentions, 90d(Community interest) | 0 | 3 |
| npm weekly downloads(Developer adoption) | 133.5k | 264.5k |
| PyPI weekly downloads(Developer adoption) | 883.0k | 3.2M |
| Stack Overflow questions(Community interest) | 209 | 160 |
| Hugging Face downloads(Product adoption) | Not available | 231 |
| Hugging Face likes(Product adoption) | Not available | 13 |
| Product Hunt comments(Community interest) | Not available | 4 |
| Product Hunt rating(Community interest) | Not available | 4.9/5 |
| Product Hunt reviews(Community interest) | Not available | 13 |
| Product Hunt votes(Community interest) | Not available | 12 |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Milvus
September 21, 2026Package vulnerabilities
npm · @zilliz/milvus2-sdk-node@3.0.6 · PyPI · pymilvus@3.0.2
0 vulnerabilities
across 2 packages
Repository security score
Not available
Weaviate
September 21, 2026Package vulnerabilities
npm · weaviate-client@3.14.0 · PyPI · weaviate-client@4.23.1
0 vulnerabilities
across 2 packages
Repository security score
Not available
Interface Preview
Milvus
Weaviate
Feature Comparison
| Feature | Milvus | Weaviate |
|---|---|---|
| Search and Query | ||
| Vector Similarity Search | Global Index delivers blazing-fast vector similarity search across massive datasets with minimal performance loss at scale | Vector search via near_vector and near_text queries with semantic understanding powered by connected ML models |
| Hybrid Search | Supports hybrid search combining vector similarity with metadata filtering for refined results | Built-in hybrid search merging vector and BM25 keyword search with configurable alpha weighting for result ranking |
| Metadata Filtering | Feature-rich metadata filtering capabilities for narrowing search results across structured attributes | Advanced filtering applies complex filters across large datasets in milliseconds with no extra overhead |
| AI and ML Integration | ||
| RAG Support | Provides guided RAG notebooks and examples developed by the community for building GenAI applications | Out-of-the-box RAG using proprietary data to securely interact with ML models without custom pipelines |
| Model Integration | Integrates with popular AI development tools and frameworks for building GenAI apps | 20+ ecosystem integrations with ML models and frameworks, plus built-in vectorizer modules for embedding generation |
| AI Agent Support | Focused on vector storage and retrieval as the foundation layer for AI agent architectures | Pre-built Database Agents that interact with and improve data, plus 30,000 Query Agent requests per month on Flex |
| Architecture and Scalability | ||
| Distributed Architecture | Cloud-native design with storage and computation separated; all components are stateless for elastic scaling | Billion-scale architecture that adapts to any workload and scales seamlessly as data grows |
| Multi-Tenancy | Supports collection-based isolation for multi-tenant deployments across distributed clusters | Native multi-tenancy with horizontal scaling, efficient resource consumption, and strict tenant isolation |
| Vector Compression | Optimized indexing with Global Index designed to maintain performance at tens of billions of vectors | Vector index compression with HNSW graph index and rotational quantization achieving 4x memory reduction |
| Deployment and Operations | ||
| Self-Hosted Options | Three tiers: Milvus Lite for notebooks and laptops, Standalone for single-machine production, Distributed for enterprise | Full-featured open-source database deployable via Docker, Kubernetes, or bare metal with no storage or query limits |
| Managed Cloud | Zilliz Cloud offers fully managed Milvus with serverless and dedicated cluster options plus BYOC deployment | Weaviate Cloud with a Free tier, Flex starting at $45/mo, and Premium from $400/mo tiers including automated upgrades |
| Backup and Recovery | Backup capabilities available through Zilliz Cloud managed service with enterprise-grade data protection | Configurable backups with zero downtime; 7-day retention on Flex, 45-day retention on Premium tier |
| Security and Compliance | ||
| Access Control | Enterprise-grade security features available through Zilliz Cloud managed deployments | Built-in RBAC across all tiers including the free sandbox, with SSO/SAML available on Premium |
| Compliance Certifications | Enterprise security and compliance available through Zilliz Cloud with SaaS and BYOC options | Enterprise-ready with SOC 2 and HIPAA compliance available on Premium tier deployments |
| Data Isolation | BYOC deployment option through Zilliz Cloud keeps data within the customer's own cloud environment | Strict tenant isolation for security, plus BYOC on Premium for data residency in your own AWS, GCP, or Azure account |
Search and Query
Vector Similarity Search
Hybrid Search
Metadata Filtering
AI and ML Integration
RAG Support
Model Integration
AI Agent Support
Architecture and Scalability
Distributed Architecture
Multi-Tenancy
Vector Compression
Deployment and Operations
Self-Hosted Options
Managed Cloud
Backup and Recovery
Security and Compliance
Access Control
Compliance Certifications
Data Isolation
Which to choose
Milvus and Weaviate are both strong open-source vector databases, but they serve different needs. We recommend Milvus for teams that need extreme scale with tens of billions of vectors and prefer a cloud-native distributed architecture with separated storage and computation. We recommend Weaviate for teams building RAG and AI-powered search applications who want transparent pricing, built-in ML model integrations, and a managed cloud service with clear tier options starting at $45 per month.
Best-fit scenarios
Choose Milvus if:
Choose Milvus if your primary requirement is scaling vector search to tens of billions of vectors with minimal performance loss. Its cloud-native architecture with stateless components and separated storage and computation makes it the stronger choice for large-scale infrastructure teams. The three deployment tiers from Lite to Distributed give you a clear upgrade path, and Zilliz Cloud provides a managed option when you need enterprise-grade support without self-hosting overhead.
Choose Weaviate if:
Choose Weaviate if you want transparent, predictable pricing and a batteries-included developer experience for AI applications. The Flex plan at $45 per month is a low-commitment entry point for production workloads, and Weaviate's 20+ ML model integrations, built-in hybrid search, out-of-the-box RAG, and native multi-tenancy reduce the custom code you need to write. The community of over 50,000 AI builders and SDKs for Python, Go, TypeScript, and JavaScript make onboarding straightforward.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
What is the main difference between Milvus and Weaviate?
The main difference is in their approach to vector database architecture and developer experience. Milvus uses a cloud-native distributed design with separated storage and computation, making all components stateless for elastic scaling to tens of billions of vectors. Weaviate takes a batteries-included approach with built-in hybrid search combining vector and BM25 keyword search, out-of-the-box RAG, and 20+ ML model integrations. Milvus focuses on raw performance and scale, while Weaviate prioritizes reducing the custom code developers need to build AI-powered applications.
How does Milvus pricing compare to Weaviate pricing?
Weaviate offers more transparent pricing with clearly published tiers: a Free tier, Flex starting at $45 per month, and Premium from $400 per month, plus free open-source self-hosting. Milvus is open-source and free to self-host, but its managed Zilliz Cloud service uses enterprise pricing that requires contacting sales. Both databases offer free self-hosted deployment, so the pricing difference primarily affects teams choosing managed cloud services. Weaviate's pay-as-you-go Flex plan provides a lower barrier to entry for production workloads.
Can Milvus and Weaviate handle billion-scale vector datasets?
Both databases are designed for large-scale vector workloads, but they approach it differently. Milvus explicitly supports tens of billions of vectors with minimal performance loss through its Global Index and distributed architecture with stateless components. Weaviate advertises billion-scale architecture with native multi-tenancy and vector index compression using HNSW with rotational quantization that achieves 4x memory reduction. For datasets exceeding tens of billions of vectors, Milvus has a stronger track record. For datasets up to several billion vectors with mixed search requirements, both are capable choices.
Which vector database is better for building RAG applications?
We recommend Weaviate for most RAG use cases because it provides out-of-the-box RAG capabilities that let you use proprietary data to interact with ML models without building custom pipelines. Weaviate includes built-in vectorizer modules for generating embeddings, 20+ ML model integrations, and Database Agents that reduce manual work. Milvus also supports RAG workflows and provides guided notebooks for building GenAI applications, but requires more integration work. If your RAG application needs to scale to tens of billions of vectors, Milvus's distributed architecture gives it an edge at that extreme scale.
What deployment options do Milvus and Weaviate offer?
Both databases provide flexible deployment options. Milvus offers three self-hosted tiers: Milvus Lite as a pip-installable library for notebooks and laptops, Milvus Standalone for single-machine production workloads with up to millions of vectors, and Milvus Distributed for enterprise-grade horizontal scaling. The managed Zilliz Cloud service adds serverless and dedicated cluster options. Weaviate offers free open-source self-hosting via Docker, Kubernetes, or bare metal, plus cloud offerings including a Free tier, Flex starting at $45 per month, and Premium from $400 per month.