botkeeper vs. H2O.ai

Well, Suited Scenarios are: Automating the data entry, analysis, bookkeeping, managing finances fairly on this basis, and pulling out burndown charts, reports, and other analytical chapters. hence it helps in understanding the business, cash flows, or issues related to that which surely end up with the ideas of revenue growth directly and indirectly as well. And the most important thing in all this is done by AI-powered tools so management of resources overhead is no more. Scenarios where it is less appropriate: When it comes to a strong comprehensive accounting then the bookkeeper fails there and we need to integrate with third-party service providers to achieve the goal. So this is the worst scenario. And for detailed resources, one needs a vast knowledge base, and that's not the case with the Botkeeper as its knowledge base appears to be limited to basic.

Incentivized

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.

Incentivized

• They have a clean and easy portal that I can log into and link all accounts so they have access to what they need
• They are so good at what they do that I only need to respond to 1 or 2 emails a month for uncategorized transactions or clarifications
• They process all of our AR, AP, year end reports, sales tax, and bookkeeping. And it's all very accurate and on time.

Incentivized

• 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

Incentivized

• their current interface
• the scoping tool (makes sense once you're using botkeeper)

Incentivized

• Better documentation
• Improve the Visual presentations including charting etc

Incentivized

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

Incentivized

Botkeeper is quite new to accounting solutions. The tech base is quite new and cutting edge. Service support is quite fast and good. Integration support is open to another third party to enhance in all possible ways. Eliminates overhead of hiring permanent resident accountants. Great user experience and well-optimized tools.

Incentivized

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.

Incentivized

• 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