Databricks Data Intelligence Platform vs. H2O.ai

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Databricks Data Intelligence Platform
Score 8.5 out of 10
N/A
Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data platforms. Users can manage full data journey, to ingest, process, store, and expose data throughout an organization. Its Data Science Workspace is a collaborative environment for practitioners to run…
$0.07
Per DBU
H2O.ai
Score 6.1 out of 10
N/A
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.N/A
Pricing
Databricks Data Intelligence PlatformH2O.ai
Editions & Modules
Standard
$0.07
Per DBU
Premium
$0.10
Per DBU
Enterprise
$0.13
Per DBU
No answers on this topic
Offerings
Pricing Offerings
Databricks Data Intelligence PlatformH2O.ai
Free Trial
NoNo
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Databricks Data Intelligence PlatformH2O.ai
Considered Both Products
Databricks Data Intelligence Platform
Chose Databricks Data Intelligence Platform
Databricks was picked among other competitors. Closest competition in our organization was H2O.ai and Databricks came out to be more useful for ROI and time to market in our internal research.
We could have used AWS products, however Databricks notebooks and ability to launch …
H2O.ai

No answer on this topic

Top Pros
Top Cons
Best Alternatives
Databricks Data Intelligence PlatformH2O.ai
Small Businesses

No answers on this topic

Astra DB
Astra DB
Score 8.3 out of 10
Medium-sized Companies
Snowflake
Snowflake
Score 9.0 out of 10
Astra DB
Astra DB
Score 8.3 out of 10
Enterprises
Snowflake
Snowflake
Score 9.0 out of 10
Dataiku
Dataiku
Score 8.6 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Databricks Data Intelligence PlatformH2O.ai
Likelihood to Recommend
8.4
(17 ratings)
8.1
(3 ratings)
Usability
9.5
(3 ratings)
-
(0 ratings)
Support Rating
8.6
(2 ratings)
9.0
(1 ratings)
Contract Terms and Pricing Model
8.0
(1 ratings)
-
(0 ratings)
Professional Services
10.0
(1 ratings)
-
(0 ratings)
User Testimonials
Databricks Data Intelligence PlatformH2O.ai
Likelihood to Recommend
Databricks
If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.
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H2O.ai
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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Pros
Databricks
  • Process raw data in One Lake (S3) env to relational tables and views
  • Share notebooks with our business analysts so that they can use the queries and generate value out of the data
  • Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
  • Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers
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H2O.ai
  • 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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Cons
Databricks
  • Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
  • Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
  • Visualization in MLFLOW experiment can be enhanced
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H2O.ai
  • Better documentation
  • Improve the Visual presentations including charting etc
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Usability
Databricks
Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX
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H2O.ai
No answers on this topic
Support Rating
Databricks
One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.
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H2O.ai
The overall experience I have with H2O is really awesome, even with its cost effectiveness.
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Alternatives Considered
Databricks
Compared to Synapse & Snowflake, Databricks provides a much better development experience, and deeper configuration capabilities. It works out-of-the-box but still allows you intricate customisation of the environment. I find Databricks very flexible and resilient at the same time while Synapse and Snowflake feel more limited in terms of configuration and connectivity to external tools.
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H2O.ai
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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Return on Investment
Databricks
  • The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
  • DB has the ability to terminate/time out instances which helps manage cost.
  • The ability to quickly access typical hard to build data scenarios easily is a strength.
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H2O.ai
  • 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
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ScreenShots