AI · head to head
LangGraph vs Milvus

Milvus
Machine Learning
Open-source vector database for scalable similarity search
- From
- Free
- Rated
- -
The short version
- Each has a real cost: LangGraph steeper learning curve compared to high-level abstractions; Milvus vector dimensions are capped at 32,768
- They diverge on capability: LangGraph covers Human-in-the-loop controls, Milvus covers Billion-scale vectors.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which LangGraph and Milvus actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in LangGraph
- Human-in-the-loop controls
- Customizable workflows
- Memory management
- Token-by-token streaming
- Low-level control
- Multi-agent support
Only in Milvus
- Billion-scale vectors
- Multiple index types
- GPU acceleration
- Hybrid search
- Data partitioning
- PyTorch
- TensorFlow
- Hugging Face
What people use each for
The jobs each tool is most often brought in to do.
LangGraph
- Building production AI agents with auditable workflowsnot Milvus
- Designing multi-agent systems for complex tasksnot Milvus
- Implementing human oversight in autonomous systemsnot Milvus
- Creating reliable agentic applications at scalenot Milvus
Milvus
- Self hosting a vector database for semantic searchnot LangGraph
- Storing and querying embeddings for retrieval augmented generationnot LangGraph
- Similarity search over images, audio or text at scalenot LangGraph
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
LangGraph
- Steeper learning curve compared to high-level abstractions
- Requires understanding of graph-based architecture
- Debugging complex workflows can be challenging
- Not optimized for simple, one-off use cases
Milvus
- Vector dimensions are capped at 32,768
- A collection is limited to 64 fields, 1,024 partitions and 16 shards
- Only 1 index is allowed per field
- Search returns at most 16,384 vectors as top-k, and nq is capped at 16,384
- Input and output per RPC is capped at 64 MB for insert, search and query
- VARCHAR values are limited to 65,535 characters
- Data loaded into query nodes cannot exceed 90% of available memory
- An instance supports at most 65,536 collections
Pricing, plan by plan
LangGraph
Free- Open SourceFree
- MIT-licensed framework
- Self-hosted deployment
- Full API access
- LangGraph Platform$35/month
- Managed hosting
- Enterprise deployment
- Integrated tooling
Milvus
Free- Open SourceFree
- Full features
- Self-hosted
- Community support
- Zilliz CloudFree
- Managed service
- Free tier available
Which should you pick?
Choose LangGraph if
- You need human-in-the-loop controls.
- You want to start without paying.
- You work on Python, JavaScript, Web.
- You also want customizable workflows.
Choose Milvus if
- You need billion-scale vectors.
- You want to start without paying.
- You work on Linux, Mac, Windows, Web.
- You also want multiple index types.
Questions people ask
- Is LangGraph or Milvus better?
- Neither clearly leads. LangGraph starts at Free and Milvus at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, LangGraph or Milvus?
- LangGraph starts at Free and Milvus at Free.
- Does LangGraph or Milvus run on more platforms?
- LangGraph runs on Python, JavaScript, Web. Milvus runs on Linux, Mac, Windows, Web.
- Can I use LangGraph for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is LangGraph best used for?
- LangGraph is most often used for building production ai agents with auditable workflows, designing multi-agent systems for complex tasks, implementing human oversight in autonomous systems, creating reliable agentic applications at scale. Of those, building production ai agents with auditable workflows and designing multi-agent systems for complex tasks are not what Milvus is typically brought in for.
- What can LangGraph do that Milvus cannot?
- LangGraph covers Human-in-the-loop controls, Customizable workflows, Memory management, Token-by-token streaming. Milvus covers Billion-scale vectors, Multiple index types, GPU acceleration, Hybrid search.
Answered from the vendors’ own pages
LangGraph: Is LangGraph free to use?
Yes. The core LangGraph framework is MIT-licensed and completely free. You only pay if you use the optional managed LangGraph Platform for hosting.
SourceMilvus: How much does Milvus cost?
Milvus is open-source and free to use and modify. The self-hosted version has no licensing cost. Zilliz Cloud (the managed SaaS version) does not publish pricing on the website.
SourceLangGraph: What programming languages does LangGraph support?
LangGraph provides first-class support for Python and JavaScript, enabling cross-platform agent development.
SourceMilvus: Is there a free or open-source version of Milvus?
Yes, Milvus is fully open-source and available for free. Milvus Lite is a lightweight option for learning and prototyping that can be installed via pip.
SourceLangGraph: Can I deploy LangGraph in production?
Yes. LangGraph can be self-hosted on your own infrastructure or deployed through LangGraph Platform with enterprise support and SLA guarantees.
SourceMilvus: Does Milvus offer a managed cloud service?
Yes, Zilliz Cloud is a fully managed Milvus cloud offering with serverless and dedicated cluster options. Pricing must be requested from the company as it is not listed on the public website.
SourceRelated pages
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