Milvus: Open-source vector database to analyze and search billions of vectors with millisecond latency at enterprise scale.
A powerful open-source database for AI applications that handles large-scale vector search, recommendations, and retrieval.
Milvus is a free Apache 2.0 open-source vector database for large-scale similarity search, with paid managed deployment available through Zilliz Cloud; it is best for teams that need distributed vector infrastructure, metadata filtering, and production retrieval over millions to billions of embeddings.
Milvus uses a disaggregated architecture with separate components for coordination, data storage, query execution, and indexing. This design allows independent scaling of each component, such as adding more query capacity without changing the storage layer. The system supports multiple index families including IVF, HNSW, DiskANN, and GPU-oriented options, giving teams ways to tune recall, latency, memory use, and infrastructure cost.
The data model in Milvus is collection-based with a schema definition that specifies fields, data types, and index parameters. Unlike simpler vector stores, Milvus supports multiple vector fields per collection, scalar field filtering, dynamic schemas, and partition-based data organization. Partitions are useful for multi-tenant AI applications where each customer's data needs to be isolated or searched efficiently.
For AI agent stacks, Milvus integrates with LangChain, LlamaIndex, Haystack, and other frameworks through connectors and community integrations. The PyMilvus SDK provides direct Python access, and Milvus Lite offers a lightweight local path for development before teams move to full Milvus or managed Zilliz Cloud for production workloads.
As of 2026, Zilliz Cloud positions Milvus as the open-source engine behind its managed vector database. Its Free plan includes 5 GB of storage, up to 2.5 million vCUs per month, and up to 5 collections. Zilliz Cloud Standard starts from $0/month for Serverless and from $126/GB/month for Dedicated, while Dedicated Enterprise starts from $197/month and adds production controls such as 99.95% uptime SLA, audit logs, SAML 2.0 SSO, granular RBAC, private networking, and enterprise support. Dedicated cluster guidance for 768-dimensional vectors lists performance-optimized capacity at about 2 million vectors per CU with 500-1500 search QPS and pricing from $63 per million vectors/month; capacity-optimized capacity at about 8 million vectors per CU with 100-300 QPS and pricing from $16 per million vectors/month; and tiered-storage capacity at about 40 million vectors per CU with 10-50 QPS and pricing from $5 per million vectors/month. The 2026 pricing guide also notes that storage for performance-optimized and capacity-optimized clusters and backup storage became $0.040/GB/month effective January 2026, with the first 100 GB of data transfer free and public internet egress starting at $0.09/GB in North America and Europe.
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Milvus is a heavyweight option for large-scale vector search with enterprise-grade distributed architecture. Overkill for small deployments but strong when you need serious scale and can handle the operational complexity.
Milvus is built to search very large vector collections, including datasets that can reach billions of vectors when deployed with suitable infrastructure. This makes it a strong fit for production RAG, recommendations, and semantic search workloads where a lightweight embedded store may not scale far enough.
Milvus supports index options including IVF, HNSW, DiskANN, and GPU-oriented indexes. These choices let teams tune for speed, recall, memory footprint, and infrastructure cost depending on whether the workload is latency-sensitive, memory-constrained, or too large to keep fully in RAM.
Milvus can combine vector similarity search with scalar metadata filters. This is essential for production applications that need to filter by tenant, permissions, product category, timestamp, region, or other structured attributes before returning results.
Milvus separates coordination, storage, query execution, and indexing so individual components can scale independently. That architecture supports larger deployments, but it also means self-hosted teams need the operational skill to manage a distributed system and its dependencies.
Milvus Lite provides an embedded, single-process environment for development and testing, while full Milvus and Zilliz Cloud support production deployment paths. This helps teams prototype locally and later move to a larger architecture without abandoning the Milvus API.
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Recent Milvus releases have emphasized production vector search improvements, including continued work on hybrid search, sparse vector support, GPU-oriented acceleration options, dynamic schema capabilities, and Milvus Lite for local development. Teams should verify exact version-specific release timing against the Milvus release notes before relying on a specific feature in production.
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