Gavagai vs. H2O.ai

Gavagai is well suited for a B2C business that receives a lot of customer feedback in a form of open-ended text. It makes life easier for the customer experience team to efficiently identify the strengths and areas of improvement for the business. It saves a lot of time and also the hassle of analysing text data manually. It is not just a word cloud tool that shows you the words with the most number of mentions. Gavagai directs you towards actionability.

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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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• Simplifies text data.
• Lets you customise your dashboard.
• Sentiment analysis
• Supports 47+ languages

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• 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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• Auto generated filters based on the data uploaded.
• Adding more Indian regional languages.
• Competitive pricing

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Independently Invited by TrustRadius

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• Better documentation
• Improve the Visual presentations including charting etc

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The overall experience I have with H2O is really awesome, even with its cost effectiveness.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

I didn't evaluate many options while choosing Gavagai, I had explored a few local vendors whose capabilities were either incomplete or were not up to the mark. Their customer support was also quite poor. Also, the tool was debugged enough which led to frequent crashing. Alchmer although is not a direct competitor to Gavagai, since it's more of a customer feedback tool with additional capabilities of text analytics. I found Alchemer to be extremely expensive. Zonka on the other hand was quite welcoming to feedback from me and promised to develop additional capabilities for my specific requirements although the plan didn't go through due to internal reasons.

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Independently Invited by TrustRadius

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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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Independently Invited by TrustRadius

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• Helps save man hours.
• Reduces the effort required to analyse big data.
• Helps to act upon areas of improvement faster.
• Indicates the strengths or things that are particularly liked by your customers.

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Independently Invited by TrustRadius

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• 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

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info