Milvus
Milvus is an open-source vector database that handles billion-scale embeddings with GPU acceleration for AI-powered search and retrieval.
Milvus is a powerful, open-source vector database designed for billion-scale similarity search and AI applications.
Key Features:
- Billion-Scale Vectors - Store and query massive embedding collections with consistent low latency.
- GPU Acceleration - Leverage GPU-powered indexing for dramatically faster vector operations.
- Hybrid Search - Combine vector similarity with scalar filtering for precise retrieval.
- Multiple Index Types - Choose from IVF, HNSW, and other algorithms to optimize for your workload.
- Cloud-Native Architecture - Scale compute and storage independently in containerized deployments.
Whether you're building semantic search over scraped content, powering RAG pipelines, or clustering extracted data, Milvus provides the performance and scale your AI applications require.
Web scraping workflow
How to use Milvus in a web scraping pipeline
Milvus stores and searches vector representations with associated scalar data. In web scraping, it belongs after extraction, where collected text or images have already been converted into searchable records.
A practical workflow
- A separate crawler and document parser produce chunks with page URLs and stable identifiers.
- Your embedding stage creates vectors and loads them into a Milvus collection together with the source metadata needed to interpret a match.
- A retrieval service searches the collection and returns matching chunks or media references to the user.
When it fits
Consider it for a collected corpus that needs similarity retrieval rather than only exact keyword lookup. Keep a consistent embedding model across indexing and queries, and measure relevance against representative questions from the intended users.
Boundaries
Milvus is not a web-search or SERP extraction API and does not discover the source pages for you. Acquisition, chunking, model selection and deletion of stale records are separate responsibilities. Choose indexes and deployment resources for the actual corpus; vector proximity alone does not establish correctness or permission to reuse content.
Quick verdict
Milvus is for AI teams that need open-source vector search at serious scale. It is not a scraper or extraction tool, so ingestion still has to come from elsewhere.
- Best for
- Large embedding collections
- RAG search pipelines
- Hybrid vector search
- Open-source search infrastructure
- Not ideal for
- Web scraping APIs
- Browser automation
- Lightweight local projects
- Last verified
- Jun 26, 2026
Key facts
- SDKsPython, JavaScript, Go, and Java SDKsProject docs
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Our productRepository
milvus-io/milvus
Stars
46.4KForks
4.3KLast commit
2026-10-15License
Apache-2.0Language
Go
Metrics recorded 2026-10-16
Repository details
License source: Repository license file
Frequently asked questions
What is Milvus used for?
Milvus stores and searches vector embeddings to find similar items. Applications use it for semantic retrieval and other similarity-search workflows, including retrieval for AI systems. Deployment options include Milvus Lite, Standalone and Distributed.
Sources (checked ): Milvus overview; Milvus deployment options; Milvus current deployment overview
Is Milvus free to use?
The open-source Milvus software is available under Apache 2.0 without a software subscription fee. Infrastructure and maintenance costs depend on the deployment. Zilliz Cloud is a separate managed service with its own plans.
Sources (checked ): Milvus Apache 2.0 license; Milvus deployment options; Milvus current deployment overview
Sources
Verified against the milvus-io/milvus repository and the PyPI package.
Source notes
Compiled from public repository and package data; check Milvus documentation for current details.