Machine Learning · head to head
H2O.ai vs Milvus

H2O.ai
Machine Learning
AI Cloud for building and deploying AI applications
- From
- Free
- Rated
- -

Milvus
Machine Learning
Open-source vector database for scalable similarity search
- From
- Free
- Rated
- -
The short version
- Each has a real cost: H2O.ai java is always required to run H2O-3 even when working from R or Python, and only a 64-bit JRE or JDK is supported; Milvus vector dimensions are capped at 32,768
- They diverge on capability: H2O.ai covers AutoML, Milvus covers Billion-scale vectors.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which H2O.ai and Milvus actually diverge.
Identical on both: starting price (Free), pricing model (freemium), free tier (Yes), user rating (Not yet rated), category (Machine Learning).
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 H2O.ai
- AutoML
- Distributed computing
- Feature engineering
- Model explainability
- Time series forecasting
- Spark
- Hadoop
- Python
Only in Milvus
- Billion-scale vectors
- Multiple index types
- GPU acceleration
- Hybrid search
- Data partitioning
- PyTorch
- TensorFlow
- Hugging Face
Both cover
- Linux support
- Mac support
- Windows support
- Web support
What people use each for
The jobs each tool is most often brought in to do.
H2O.ai
- Distributed in-memory machine learning over large datasetsnot Milvus
- Training and productionising models from R or Python against a shared H2O clusternot Milvus
Milvus
- Self hosting a vector database for semantic searchnot H2O.ai
- Storing and querying embeddings for retrieval augmented generationnot H2O.ai
- Similarity search over images, audio or text at scalenot H2O.ai
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
H2O.ai
- Java is always required to run H2O-3 even when working from R or Python, and only a 64-bit JRE or JDK is supported
- Supported Java versions stop at Java SE 17; newer versions only run by forcing an unsupported version flag and are guaranteed for experiments rather than production
- H2O-3 only supports numpy below version 2, so a numpy 2 environment must be downgraded
- Supported Python versions are limited to 3.7 through 3.11
- The Flow web UI requires an internet browser and is the only graphical interface
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
H2O.ai
Free- H2O-3 Open SourceFree
- Core algorithms
- AutoML
- Community support
- Driverless AIFree
- Automatic feature engineering
- Model explainability
- Enterprise support
Milvus
Free- Open SourceFree
- Full features
- Self-hosted
- Community support
- Zilliz CloudFree
- Managed service
- Free tier available
Which should you pick?
Choose H2O.ai if
- You need automl.
- You want to start without paying.
- You work on Web, Cloud.
- You also want distributed computing.
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 H2O.ai or Milvus better?
- Neither clearly leads. H2O.ai 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, H2O.ai or Milvus?
- H2O.ai starts at Free and Milvus at Free.
- Does H2O.ai or Milvus run on more platforms?
- H2O.ai runs on Web, Cloud. Milvus runs on Linux, Mac, Windows, Web.
- Can I use H2O.ai for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is H2O.ai best used for?
- H2O.ai is most often used for distributed in-memory machine learning over large datasets, training and productionising models from r or python against a shared h2o cluster. Of those, distributed in-memory machine learning over large datasets and training and productionising models from r or python against a shared h2o cluster are not what Milvus is typically brought in for.
- What can H2O.ai do that Milvus cannot?
- H2O.ai covers AutoML, Distributed computing, Feature engineering, Model explainability. Milvus covers Billion-scale vectors, Multiple index types, GPU acceleration, Hybrid search. Both handle Linux support, Mac support, Windows support, Web support.
Answered from the vendors’ own pages
H2O.ai: Is H2O open source and free?
Yes. H2O-3 OSS is free and Apache-licensed, designed for self-managed and experimental workflows. H2O.ai also offers enterprise cloud solutions with additional features.
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.
SourceH2O.ai: How many companies use H2O's open source platform?
Over 18,000 companies across Finance, Insurance, Healthcare, Retail, Telco, Sales, and Marketing use H2O's open-source machine learning platform.
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.
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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