Milvus
What is Milvus?
Discover how Milvus can enhance your workflow
Key Capabilities
What makes Milvus powerful
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.
Key Features
What makes Milvus stand out
- 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
Use Cases
How Milvus can help you
- 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
Perfect For
ML engineers platform teams data scientists and search engineers building high scale retrieval systems that demand open source control or managed SLAs
Plans & Pricing
Free self-hosted / Zilliz Cloud from $99 per month
Visit official site for current pricing
Quick Information
Compare Milvus with Alternatives
See how Milvus stacks up against similar tools
Frequently Asked Questions
What does Milvus cost and is there a free tier?
How does Milvus compare with vector search add-ons in SQL stores?
Can I use Milvus with LangChain or LlamaIndex?
How do I choose an index type for my data?
Is there a managed option if we do not want to run clusters?
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