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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Sumo Logic
Score 8.8 out of 10
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Sumo Logic is a log management offering from the San Francisco based company of the same name.
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.
SumoLogic is a fantastic log aggregator and analysis tool, a fine alternative to Splunk. Searching is powerful and mostly intuitive and results come fast. If you have application logs in clusters or Kubernetes pods that lose their logs every time they're restarted, Sumo is the solution for you
Sumo Logic allowed for our InfoSec team to ingest logs from our CDN directly, in real-time, instead of massive compressed archives that were sent every two-hours (the only alternative at the time). Sumo Logic had an app for these logs, that allowed us to easily get an immediate payoff from the data, with canned dashboard and saved searches.
Sumo Logic has a fairly extensive REST API when it comes to log sources, source configurations, dashboard data, searches, etc. Their wiki for the API is usually kept up to date.
Sumo Logic, during the period of time I had used their product, had added the ability to configure agents via configuration files. This allowed customers to configure their endpoints, and modify the endpoints, with configuration management tools like Chef / Puppet / Salt. Beforehand, the only option was to always make changes either via the web portal or REST API.
The solutions engineers were extremely helpful, and easily reachable when issues would occur.
Users at our company found it easy to get started, working on new dashboards, scheduled searches, and alerting. The alerting worked well with our third-party paging tool.
Sumo Logic is very powerful but definitely requires some configuration work to get the most out of it. You can get a certification related to this, but it is definitely not something you can just throw together.
I would give this rating because I attended a free Sumo Logic training at a WeWork in Chicago. I found the training very useful, and I learned a lot of features that I was not aware of before I went to the training. I like the idea that SumoLogic provides free training seminars. I am certified in level1, and I plan on certifying to level2.
I was satisfied with the implementation, as at the time, it was the best way to implement the product with the available feature sets in Sumo Logic. User creation and management became more of an issue during continued use, instead of it being an issue related to deploying the product in our environment.
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.
Sumo Logic works very well out of the gate. For a small business it has given us what we need. I worked at a larger company previously, and we produced so many logs we had to create a custom logging service to handle them all. Cost and availability are big issues when deciding between the different services, whether self maintained and hosted, or provided by another company.
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
