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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Splunk Cloud
Score 8.3 out of 10
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A data platform service thats help users search, analyze, visualize and act on data. The service can go live in as little as two days, and with an IT backend managed by Splunk experts, users can focus on acting on data. Search any kind of data in real-time to detect and prevent issues before they happen with access to streaming and machine learning capabilities. Search any kind of data in real-time to detect and prevent issues before they happen with access to the latest streaming and machine…
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.
Splunk is excellent when all your data is in one location. Its ability to correlate all that data is intuitive (once the hurdle of learning the query language is overcome). It is also easy to standardize the presentation of information to the company. When data is siloed/standalone, other systems can be cheaper and faster to implement.
This SIEM consolidates multiple data points and offers several features and benefits, creating custom dashboards and managing alert workflows.
Splunk Cloud provides a simple way to have a central monitoring and security solution. Though it does not have a huge learning curve, you should spend some time learning the basics.
Splunk Cloud enables me to create and schedule statistical reports on network use for Management.
Overall, it is very usable. I would like if recent searches were saved for longer because I always have to refer to my notes when I'm looking for something specific and it's been a few weeks. But that's a small issue, and the actual search and browsing interface is easy to use and powerful.
Splunk Cloud support is sorely lacking unfortunately. The portal where you submit tickets is not very good and is lacking polish. Tickets are left for days without any updates and when chased it is only sometimes you get a reply back. I get the feeling the support team are very understaffed and have far too much going on. From what I know, Splunk is aware of this and seem to be trying to remedy it.
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.
Splunk Cloud blows Sumo Logic out of the water. The experience is night and day. We went from several highly stressed IT security professionals who were unsure if the data they were getting was valuable, to very happy IT security professionals who can now be more proactive and get all the information they need.
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
