Milvus
Open-source vector database for similarity search and retrieval that scales to billions of embeddings with high availability cloud options and an Apache-2.0 license.
Overview
Milvus is a high-performance vector database used to build search, recommendation, RAG, and anomaly detection systems. It stores embeddings from models, indexes them with algorithms such as IVF, HNSW, and DiskANN, and executes nearest-neighbor queries with predictable latency as data grows. 0 or choose managed offerings from Zilliz Cloud for simplified operations, autoscaling, and backups.
The ecosystem includes client SDKs, tutorials, and integrations with LangChain, LlamaIndex, and popular model hubs. Production features—partitioning, hybrid search with scalar filters, and streaming ingestion—support real apps at scale. With a large community and vendor backing, Milvus remains a dependable core for GenAI retrieval workloads from prototypes to tens of billions of vectors.
Key features
- Apache 2.0 licensed core enabling free self hosted deployments that fit security requirements and cost control for startups and enterprises
- Multiple index types including IVF HNSW and DiskANN chosen per workload to balance recall latency memory and storage under changing traffic
- Hybrid search combining vector similarity with scalar filters and metadata making retrieval precise and useful for real application constraints
- Horizontal scaling with partitions replicas and GPU acceleration options so datasets can grow to tens of billions of vectors reliably
- Streaming and batch ingestion with durability and background compaction keeping write heavy workloads steady under constant updates
- SDKs for Python Java and Go plus REST and integrations with LangChain and LlamaIndex to speed up app builds and experiments
- Observability metrics dashboards and logs so teams tune index params recall and latency with evidence not guesswork
- Managed option via Zilliz Cloud adding backups autoscaling and operational SLAs for teams that prefer hosted control planes
Best for
- Build RAG systems that answer with context by retrieving citations from private corpora with tight latency SLAs
- Power visual similarity search across large image catalogs for e commerce discovery and deduplication
- Run recommendation candidates by embedding user and item signals then filtering by metadata for relevance
- Detect anomalies by tracking vector distances and neighbors across sensor or event streams with streaming ingestion
- Index fine tuned embeddings from domain models to lift retrieval quality in specialized tasks
- Prototype quickly with local deployment then move to managed cloud when traffic and uptime demands rise
- Support A B tests by tuning index params and measuring recall latency and cost impacts with monitoring
- Unify multimodal embeddings for text image and audio search in one system with hybrid filters
Capabilities
Indexes and Partitions
Choose IVF HNSW or DiskANN with partitioning and replicas to match recall latency and cost targets as datasets grow.
Similarity and Filters
Run kNN queries with metadata filters to balance precision and speed for production workloads and user experiences.
Batch and Streaming
Insert data continuously or in batches with compaction durability and tools for backfills and reindexing.
Observability and Cloud
Monitor metrics logs and dashboards or adopt Zilliz Cloud for backups autoscaling and simpler operations.
Frequently Asked Questions
What does Milvus cost and is there a free tier?
Self-hosted Milvus is $0 under Apache-2.0. Managed Milvus via Zilliz Cloud typically starts near $99 per month for dedicated capacity with free tiers available in some regions.
How does Milvus compare with vector search add-ons in SQL stores?
Purpose built vector indexes and query planners usually deliver better recall and latency at scale though SQL add-ons can be fine for small workloads.
Can I use Milvus with LangChain or LlamaIndex?
Yes, official connectors exist so you can plug Milvus into RAG pipelines quickly and swap components as needs change.
How do I choose an index type for my data?
Start with HNSW for high recall then evaluate IVF or DiskANN for memory or disk tradeoffs using your dataset and latency budget.
Is there a managed option if we do not want to run clusters?
Zilliz Cloud provides managed Milvus with backups autoscaling and SLAs which many teams adopt for production.
