Google Gemini (formerly Bard) is an AI assistant, presented as a creative and helpful collaborator. Gemini for Workspace is available via two plans: a Gemini Enterprise add-on, and a Gemini Business add-on.
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H2O.ai
Score 6.4 out of 10
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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.
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Pricing
Google Gemini
H2O.ai
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Google Gemini
H2O.ai
Free Trial
No
No
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Google Gemini
H2O.ai
Considered Both Products
Google Gemini
Verified User
Anonymous
Chose Google Gemini
I like the fact that Gemini gives you 3 options of possible answers, and if they don't fit your needs, you are able to have other 3, until you get the best result. I have seen that the results that Gemini provides are more accurate than others. It also evolving frequently and …
Gemini seems very simple to use, veyr similar to ChatGPT, I wish they did have a capability such as ChatGPT projects one, so one can separate topics easily, it's very customizable, where I believe it defeats the others is that, is already very simple to use all of Google …
Google Gemini, when compared to other AI Tools, can be rated as follows. - Creativity: Best - Product Research: Best - User Interface: Best - Features: Very Good - Code Generation: Very Good - Documentation: Very Good - Accuracy: Good - Speed: Good Considering the above …
Google is free and included in my workspace subscription. Claude has no image generation. Google Gemini is also a Google product, so I assume it will be able to have a better understanding of the areas on Google I am trying to market. Overall, I think it's better than Claude, …
Hootsuite's OwlyGPT is great for social listening data, but Gemini is far ahead in terms of caption writing and other writing needs. Even for content creation ideas, I'd rather take the social listening insights then feed that to Gemini.
Gemini can fix its hallucination and generic output problem to get at part with Perplexity. Additionally, to beat in web search, it can produce citation after every information given. By this it can gain users trust. Sometimes, it doesn't give output only. such instances can be …
Google Gemini has the best context window in the market, the parameters are incredible and the speed is fantastic. It will lose for ChatGPT by seniority. It has the same tools now and the integration with the Google environment makes adding AI to any project, seamless. It is …
I like the UI of Google Gemini way more, and I also love the inbuilt integrations it has with open google docs and sheets. ChatGPT does not have (AFAIK) a Deep research section. Google Gemini Gems is also an awesome addition which helps to automate mundane/repetitive tasks. I …
Best at productivity based tasks that I encounter in the workplace. I spend about 20% of my time developing, and a majority of it doing overhead or operational tasks. Google Gemini is the best for analyzing spreadsheets, performing forecasting tasks, etc. If I was more …
Google Gemini stands tall in this league of AI chat tools. It has a good clean interface and good ability to answer questions quickly on questions related to research and development. However, It lacks integration with IDE tools such as Chat GPT's integration with Microsoft …
Google Gemini does pretty well against ChatGPT in regards to the information sourced and accuracy. Gemini's user interface is about the same, however I find it a bit cleaner, especially the way information is outputted. We use a lot of the Google Suite products, so access to …
Google Gemini has the advantage of being integrated with the Google family of products, very well know and used world abroad. Like, using workspace, Gemini can read my email and make a daily summary, search for urgent and important stuff, etc. Also, Gemini allows me to do …
I have used Knime, RapidMiner, and Weka before I heard about H2O, but amongst all I really liked H2O. However, nowadays Googles AutoML and AWS SageMaker AutoML platform are really competitive, but more costly than H2O.
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 …
H2O provided all the needed features such as Linear Modeling, Targeted Learning, Predictive Analytics including GLM, Trees, Neural networks and ensemble with ease. We are also able to pick and choose what we want without deploying all the bulky tools unlike others. Able to …
Google Gemini AI features a Deep Research feature that helped us conduct thorough product research. We wanted to minimize the costs incurred by using SSL certificates in our organization, but we lacked knowledge on the subject. Google Gemini Deep Research did a thorough analysis and suggested ways to cut costs by switching vendors and using DV-type and/or wildcard SSL certificates. We also use Google Gemini for assistance during software development. However, Google Gemini seems to have limitations when suggesting code snippets for the Microsoft ecosystem.
Use H2O.ai whenever you need easy to use tool, when you must be cost efficient (you can not charge the client extra money for software licenses used), need a tool with lots of algorithms that are normally used in data analytics, or need to work on one machine (it is either not allowed to move data to cloud storage or simply not necessary to connect to Hadoop, etc.). Also, you can call H2O directly from Python which makes analysis more efficient.
Deep research for getting first business research draft from Gemini, post which i use series of prompts to improve it and use my understanding to refine it further
Canvas to produce structured business topic research and newsletter. Direct edits to the sections and making client ready reports
Learning mode to get help on step by step automation of AI workflows
Currently the document database caps out at 10, requiring us to condense some of our policies
It's large context window is a blessing and a curse. Sometimes it stops generating half way through a very ambitious request as it delivers page after page of content
There is no way to share Gems currently, so we have to publish guides to our employees on how to best configure them
This is not really a drawback, but rather a warning - the Drivereless AI is not a replacement for a data scientist yet, and will not replace data scientists in the next decade neither. The Driverless AI feature delivers reliable results only if the analyst is sure about the meaning of input data. The data quality is usually a major issue and no tool can detect the meaning of data in the input. Data scientists are also required for business interpretation of the findings. So be careful, and do not rely on this feature without a good understanding of what it really does in each step.
It is simple, has the same standard industry format, all the tools are accessible and recognizable. Whenever we are in the browser we can switch from one request to another while the first is still running. Little hallucination and the context window has no competitor on the market right now. The pricing is also the biggest advantage.
Gemini seems very simple to use, veyr similar to ChatGPT, I wish they did have a capability such as ChatGPT projects one, so one can separate topics easily, it's very customizable, where I believe it defeats the others is that, is already very simple to use all of Google ecosystem, such as Drive, docs, sheets and else
I have used Knime, RapidMiner, and Weka before I heard about H2O, but amongst all I really liked H2O. However, nowadays Googles AutoML and AWS SageMaker AutoML platform are really competitive, but more costly than H2O.
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
