H2O.ai vs. Hugging Face

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
H2O.ai
Score 6.3 out of 10
N/A
An open-source end-to-end GenAI platform for air-gapped, on-premises or cloud VPC deployments. Users can Query and summarize documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project. And the commercially available Enterprise h2oGPTe provides information retrieval on internal data, privately hosts LLMs, and secures data.N/A
Hugging Face
Score 9.4 out of 10
N/A
Hugging Face is an open-source provider of natural language processing (NLP) technologies.
$9
per month
Pricing
H2O.aiHugging Face
Editions & Modules
No answers on this topic
Pro Account
$9
per month
Enterprise Hub
$20
per month per user
Offerings
Pricing Offerings
H2O.aiHugging Face
Free Trial
NoNo
Free/Freemium Version
YesYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details——
More Pricing Information
Community Pulse
H2O.aiHugging Face
Top Pros

No answers on this topic

Top Cons

No answers on this topic

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All AlternativesView all alternativesView all alternatives
User Ratings
H2O.aiHugging Face
Likelihood to Recommend
8.1
(3 ratings)
9.3
(6 ratings)
Support Rating
9.0
(1 ratings)
-
(0 ratings)
User Testimonials
H2O.aiHugging Face
Likelihood to Recommend
H2O.ai
Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use. If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.
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Hugging Face
If an organisation has more access to data and have access to high end computers like GPUs it’s recommended to use Hugging face as it will give better accuracy than any other models. If an organisation having less data and has less access to GPUsis looking for decent performance then traditional algorithms are more appropriate than hugging face
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Pros
H2O.ai
  • Excellent analytical and prediction tool
  • In the beginning, usage of H20 Flow in Web UI enables quick development and sharing of the analytical model
  • Readily available algorithms, easy to use in your analytical projects
  • Faster than Python scikit learn (in machine learning supervised learning area)
  • It can be accessed (run) from Python, not only JAVA etc.
  • Well documented and suitable for fast training or self studying
  • In the beginning, one can use the clickable Flow interface (WEB UI) and later move to a Python console. There is then no need to click in H20 Flow
  • It can be used as open source
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Hugging Face
  • Model APIs
  • Hugging Face Spaces for deploying demo apps
  • Latest updated models available easily
  • Vast support for language parsing and other relevant tasks
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Cons
H2O.ai
  • Better documentation
  • Improve the Visual presentations including charting etc
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Hugging Face
  • Most of the Hugging face models are of big size, hence difficult to work if there is no access to high computational system like GPU.
  • It’s good to have some visualization tool in hugging face for viewing model architecture.
  • I recommend to implement hugging face lite version so that it can run on any system with less specifications.
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Support Rating
H2O.ai
The overall experience I have with H2O is really awesome, even with its cost effectiveness.
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Hugging Face
No answers on this topic
Alternatives Considered
H2O.ai
Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a competitor but rather a complementary tool. The use case is usually not only about the algorithms, but also about the data model and data logistics and accessibility. H2O is more accessible due to its UI. Also, both can be accessed from Python. The community around TensorFlow seems larger than that of H2O.
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Hugging Face
There are some other services offer similar capacity as to Hugging Face, but not entirely the same. For example, amazon web services have a machine learning service called Comprehend, which offer a set of easy to use APIs to do machine translation and entity recognition and some other common NLP use case.
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Return on Investment
H2O.ai
  • Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction
  • Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors
  • Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product
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Hugging Face
  • Hugging Face is cost and time saving.
  • Pay is less, you pay what you use, doesn't affect much.
  • Overall positive impact on business.
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ScreenShots

Hugging Face Screenshots

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