Decision comparison
Milvus and Zilliz share the same core vector database technology, but they serve fundamentally different operational models. Milvus is the right choice for teams that want full control over their infrastructure and have the engineering resources to manage a self-hosted vector database. Zilliz is the better option for organizations that need production-grade vector search without the operational burden, offering managed infrastructure, enterprise security certifications, and a 10x performance advantage through its Cardinal search engine.
| Decision factor | Milvus | Zilliz |
|---|---|---|
| Deployment Model | Self-hosted open-source with Lite, Standalone, and Distributed deployment options for full infrastructure control | Fully managed cloud service on AWS, Azure, and GCP with serverless and dedicated cluster options |
| Pricing Structure | Contact for pricing | Free (no cost), Standard $0/mo, Enterprise MOST POPULAR $155/mo |
| Scalability | Distributed architecture scales to tens of billions of vectors with separated storage and computation | Scales to 500 CUs serving over 100 billion items with elastic scaling and multi-replica support |
| Operational Overhead | Requires in-house expertise for deployment, tuning, monitoring, and ongoing infrastructure maintenance | Zero infrastructure management with automated scaling, upgrades, monitoring, and built-in embedding pipelines |
| Security & Compliance | Full control over security configuration but requires self-managed encryption, access controls, and compliance | SOC2 Type II, ISO27001, HIPAA-eligible with RBAC, SSO, CMEK, and private endpoint support |
| Performance Optimization | Global Index provides fast vector similarity search with manual tuning required for optimal results | Cardinal search engine delivers 10x quick retrieval compared to open-source Milvus with ai-powered AutoIndex |
Comparable public signals only; they do not establish enterprise adoption, product quality, or total cost. Product Hunt signals reflect launch engagement.
| Metric | Milvus | Zilliz |
|---|---|---|
| PyPI weekly downloads | 1.6M | 1.6M |
As of 2026-08-10 — updated weekly.
| Feature | Milvus | Zilliz |
|---|---|---|
| Search Capabilities | ||
| Hybrid Search | Supports metadata filtering and hybrid search across multiple vector fields with sparse-dense combinations | Full hybrid search with multimodal, sparse-dense, and dense-text combinations plus AI-powered query optimization |
| Similarity Metrics | Standard similarity metrics including Cosine, Euclidean, and Inner Product for classification and clustering | Full suite of similarity metrics (Cosine, Euclidean, IP) with automated optimization for each dataset |
| Indexing | Global Index for fast vector similarity search with manual index configuration and tuning | AI-powered AutoIndex combining IVF and graph techniques with Cardinal search engine for automated optimization |
| Deployment & Infrastructure | ||
| Deployment Options | Milvus Lite for notebooks, Standalone for single-machine production, and Distributed for enterprise-grade horizontal scaling | Fully managed serverless clusters, dedicated clusters starting at a current vendor-published rate, and Bring Your Own Cloud (BYOC) option |
| Cloud Support | Self-hosted on any cloud or on-premises infrastructure with full control over deployment topology | Multi-cloud deployment across AWS, Azure, and Google Cloud in eight regions worldwide |
| Scaling Architecture | Cloud-native stateless components with separated storage and computation for horizontal scaling to billions of vectors | Elastic scaling to 500 CUs with multi-replica support, automated tiered storage, and component-based architecture |
| Security & Compliance | ||
| Access Control | Self-managed authentication and authorization requiring custom configuration of access policies | Granular Role-Based Access Control (RBAC) with SSO via SAML 2.0 and audit logging |
| Data Encryption | Encryption must be configured manually at the infrastructure level for data in transit and at rest | Built-in encryption for data in transit and at rest, with CMEK and full-path in-transit encryption on Business Critical tier |
| Compliance Certifications | No managed compliance certifications; organizations handle their own compliance audits and controls | SOC2 Type II and ISO27001 certified, HIPAA-eligible on Business Critical tier for regulated industries |
| Operations & Management | ||
| Monitoring & Observability | Requires self-managed monitoring stack with community-supported tools and custom dashboards | Built-in advanced metrics and observability with backup, restore, and basic monitoring on all tiers |
| Maintenance & Upgrades | Manual upgrades and maintenance requiring engineering time for patching, scaling, and performance tuning | Automated upgrades, scaling, and monitoring handled entirely by the managed service platform |
| Data Pipelines | No built-in embedding pipelines; requires separate tooling for data preparation and vector transformation | Built-in embedding pipelines for converting unstructured data into searchable vectors, covering chunking and model selection |
| Pricing & Support | ||
| Free Tier | Completely free open-source under Apache 2.0 with unlimited usage and no feature restrictions | Free tier with 5 GB storage, 2.5M vCUs per month, and up to 5 collections on Google Cloud |
| Enterprise Pricing | No licensing costs; total cost is infrastructure plus in-house engineering for deployment and operations | Enterprise tier from a current vendor-published rate with 99.95% uptime SLA, multi-replica scaling, and enterprise support included |
| Support Options | Community support through GitHub, documentation, and an active contributor community with 43,000+ stars | Enterprise support included on paid tiers with priority support and rapid incident response on Business Critical |
Hybrid Search
Similarity Metrics
Indexing
Deployment Options
Cloud Support
Scaling Architecture
Access Control
Data Encryption
Compliance Certifications
Monitoring & Observability
Maintenance & Upgrades
Data Pipelines
Free Tier
Enterprise Pricing
Support Options
Milvus and Zilliz share the same core vector database technology, but they serve fundamentally different operational models. Milvus is the right choice for teams that want full control over their infrastructure and have the engineering resources to manage a self-hosted vector database. Zilliz is the better option for organizations that need production-grade vector search without the operational burden, offering managed infrastructure, enterprise security certifications, and a 10x performance advantage through its Cardinal search engine.
Choose Milvus if:
Engineering teams with strong infrastructure expertise who need full control over their vector database deployment. Choose Milvus when you have strict data sovereignty requirements, need custom infrastructure configurations, or want to avoid recurring SaaS costs. Milvus is particularly strong for organizations already running Kubernetes clusters who can absorb the operational overhead of managing a distributed stateless system.
Choose Zilliz if:
Product and AI teams building production applications who want to focus on their core business logic rather than database operations. Choose Zilliz when you need enterprise-grade security certifications like SOC2 Type II and HIPAA compliance, require guaranteed uptime SLAs of 99.95%, or want faster vector retrieval through the Cardinal search engine. Zilliz is the stronger choice for teams that value reduced total cost of ownership over raw infrastructure control.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Zilliz Cloud is the fully managed version of Milvus, built and operated by the same team that created the open-source Milvus project. Milvus is available as a free, open-source vector database under the Apache 2.0 license, while Zilliz provides a managed cloud service that adds enterprise features like automated scaling, built-in embedding pipelines, the Cardinal search engine for retrieval, and compliance certifications including SOC2 Type II and ISO27001. Think of it as the difference between running your own PostgreSQL server and using a managed database service.
Milvus itself is free and open-source, but self-hosting requires infrastructure costs (compute, storage, networking) plus engineering time for deployment, tuning, and maintenance. Zilliz Cloud offers a free tier with 5 GB storage and 2.5M vCUs per month, a Standard tier starting at a current vendor-published rate for serverless and a current vendor-published rate for dedicated clusters, and an Enterprise tier from a current vendor-published rate with a 99.95% uptime SLA. Zilliz claims up to 70% lower total cost of ownership compared to self-managed deployments by eliminating operational overhead. The actual cost comparison depends on your team size, scale, and how much engineering time you allocate to infrastructure management.
Yes, because Zilliz Cloud is built directly on the Milvus codebase, migration is straightforward. Zilliz maintains API compatibility with Milvus, and the same SDKs (Python, Java, Go, Node.js) work with both platforms. Organizations that start with self-hosted Milvus for prototyping or development can migrate to Zilliz Cloud for production workloads without rewriting their application code. Several Zilliz customers, including Shulex, have reported successful transitions from open-source Milvus to Zilliz Cloud with improved performance and reduced operational costs.
Both Milvus and Zilliz support billion-scale vector search, but they achieve it differently. Milvus Distributed uses a cloud-native architecture with separated storage and computation that can scale to tens of billions of vectors. Zilliz Cloud takes this further with elastic scaling to 500 CUs capable of serving over 100 billion items, plus the Cardinal search engine that delivers 10x quicker vector retrieval. For organizations handling massive-scale workloads, Zilliz offers a turnkey path to billion-scale performance, while Milvus gives you full control over the scaling architecture at the cost of increased operational complexity.