Databricks vs H2O.ai comparison

Databricks and H2O.ai are both solutions in the Data Science Platforms category. Databricks is ranked #1 with an average rating of 8.4, while H2O.ai is ranked #16 with an average rating of 7.7. Databricks holds a 14.5% mindshare in Data Science Platforms, compared to H2O.ai’s 1.8% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 100% of H2O.ai users who would recommend it.

Databricks

Read 91 Databricks reviews

22,527 Views
9,912 Comparison Views

96% willing to recommend

H2O.ai

Read 10 H2O.ai reviews

1,199 Views
1,127 Comparison Views

100% willing to recommend

Databricks

H2O.ai

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

Databricks and H2O.ai are competing platforms in data analytics and machine learning. Databricks has an edge in scalability and integration, making it suitable for handling vast amounts of data, while H2O.ai excels with specialized AI capabilities, appealing to users needing comprehensive machine learning functions.

Features: Databricks offers superior integration with Apache Spark and cloud services, robust data processing capabilities, and a collaborative environment. It also supports seamless use of multiple programming languages and provides a user-friendly notebook interface. H2O.ai delivers strong machine learning capabilities, including AutoML for automating model training, a centralized AI platform to streamline development, and support for Jupyter Notebooks to enhance collaborative efforts.

Room for Improvement: Databricks could enhance its deep learning support and simplify certain advanced machine learning tasks. An increase in available customer support channels would also be beneficial. H2O.ai may improve its scalability and ease of integration with external systems. Offering a wider range of built-in algorithms could attract users looking for more out-of-the-box solutions.

Ease of Deployment and Customer Service: Databricks provides cloud-native deployment, ensuring quick setup and scalability with strong technical support and training resources. H2O.ai offers flexible deployment options, including both cloud and on-premise solutions, along with comprehensive support focused on deep learning applications and AI-specific guidance.

Pricing and ROI: Databricks offers flexible pricing models that can align well with its scalable infrastructure, often yielding favorable ROI for large-scale data operations. H2O.ai tends to have a higher initial cost, but its advanced AI features can lead to substantial returns, especially for applications that benefit from specialized machine learning.

To learn more, read our detailed Databricks vs. H2O.ai Report (Updated: July 2025).

Buyer's Guide

Databricks vs. H2O.ai

July 2025

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Databricks

Ranking in Data Science Platforms

1st

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (9th), Streaming Analytics (1st)

H2O.ai

Ranking in Data Science Platforms

16th

Average Rating

7.6

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Model Monitoring (4th)

Mindshare comparison

As of September 2025, in the Data Science Platforms category, the mindshare of Databricks is 14.5%, down from 19.8% compared to the previous year. The mindshare of H2O.ai is 1.8%, up from 1.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Market Share Distribution
Product	Market Share (%)
Databricks	14.5%
H2O.ai	1.8%
Other	83.7%

Data Science Platforms

Featured Reviews

ShubhamSharma7

Data Engineer at a engineering company with 1,001-5,000 employees

Capability to integrate diverse coding languages in a single notebook greatly enhances workflow

Databricks offers various courses that I can use, whether it's PySpark, Scala, or R. I can leverage all these courses in a single notebook, which is beneficial for clients as they can access various tools in one place whenever needed. This is quite significant. I usually work with PySpark based on client requirements. After coding, I feed the Databricks notebooks into the ADF pipeline for updates. Databricks' capability to process data in parallel enhances data processing speed. Furthermore, I can connect our Databricks notebook directly with Power BI and other visualization tools like Qlik. Once we develop code, it allows us to transform raw data into visualizations for clients using analysis diagrams, which is very helpful.

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Abhay Vyas

Technical Architect Data Engineering at Cognida.ai

Advanced model selection and time efficiency meet needs but documentation and fusion model support are needed

Even though H2O.ai provides the best model, there could be improvements in certain areas. For instance, when you want to work with fusion models, H2O.ai doesn't provide that kind of information. Currently, it provides individual models as outcomes. If it could offer combinations of models, such as suggesting using XGBoost along with SVM for wonderful results, that fusion model concept would be a good option for developers. I hope the fusion model concept will be implemented soon in H2O.ai. Regarding documentation, I faced challenges as I didn't see much information from a documentation perspective. When I was trying to learn how to train and test H2O.ai, there was limited documentation available. If they could improve in that area, it would be really beneficial.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"Databricks serves as a single platform that can handle numerous end-to-end machine learning tasks."

"The most valuable feature of Databricks is the notebook, data factory, and ease of use."

"The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes."

"I like that Databricks is a unified platform that lets you do streaming and batch processing in the same place. You can do analytics, too. They have added something called Databricks SQL Analytics, allowing users to connect to the data lake to perform analytics. Databricks also will enable you to share your data securely. It integrates with your reporting system as well."

"Databricks offers various courses that I can use, whether it's PySpark, Scala, or R."

"The solution's features are fantastic and include interactive clusters that perform at top speed when compared to other solutions."

"The time travel feature is the solution's most valuable aspect."

"Databricks is hosted on the cloud. It is very easy to collaborate with other team members who are working on it. It is production-ready code, and scheduling the jobs is easy."

More Databricks pros

"The most valuable feature of H2O.ai is that it is plug-and-play."

"I have utilized the AutoML feature in H2O.ai, which is one of the very powerful features where you don't need to worry about which algorithm is best for your model."

"The ease of use in connecting to our cluster machines."

"The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."

"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."

"H2O.ai provides better flexibility where I could examine more models and obtain results, and based on these results, I could make the next set of decisions."

"It is helpful, intuitive, and easy to use. The learning curve is not too steep."

"One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm."

More H2O.ai pros

Cons

"Pricing is one of the things that could be improved."

"The integration of data could be a bit better."

"The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets."

"Would be helpful to have additional licensing options."

"There should be better integration with other platforms."

"Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with."

"The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well."

"Databricks doesn't offer the use of Python scripts by itself and is not connected to GitHub repositories or anything similar. This is something that is missing. if they could integrate with Git tools it would be an advantage."

More Databricks cons

"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."

"One improvement I would like to see in H2O.ai is regarding the integration capabilities with different data sources, as I've seen platforms like DataIQ and DataBricks offer great integration with various data sources."

"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."

"The model management features could be improved."

"The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability."

"I would like to see more features related to deployment."

"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."

"H2O.ai can improve in areas like multimodal support and prompt engineering."

More H2O.ai cons

Pricing and Cost Advice

"The billing of Databricks can be difficult and should improve."

"We have only incurred the cost of our AWS cloud services. This is because during this period, Databricks provided us with an extended evaluation period, and we have not spent much money yet. We are just starting to incur costs this month, I will know more later on the full cost perspective."

"My smallest project is around a hundred euros, and my most expensive is just under a thousand euros a week. That is based on terabytes of data processed each month."

"We pay as we go, so there isn't a fixed price. It's charged by the unit. I don't have any details detail about how they measure this, but it should be a mix between processing and quantity of data handled. We run a simulation based on our use cases, which gives us an estimate. We've been monitoring this, and the costs have met our expectations."

"We're charged on what the data throughput is and also what the compute time is."

"The pricing depends on the usage itself."

"There are different versions."

"I would rate the tool’s pricing an eight out of ten."

More Databricks pricing and cost advice

"We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff."

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

10%

Manufacturing Company

Healthcare Company

Financial Services Firm

16%

Computer Software Company

15%

Manufacturing Company

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	25
Midsize Enterprise	12
Large Enterprise	56

By reviewers
Company Size	Count
Small Business	2
Midsize Enterprise	3
Large Enterprise	7

Questions from the Community

Which do you prefer - Databricks or Azure Machine Learning Studio?

Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...

See all answers

How would you compare Databricks vs Amazon SageMaker?

We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

See all answers

Which would you choose - Databricks or Azure Stream Analytics?

Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...

See all answers

What needs improvement with H2O.ai?

See all answers

What is your primary use case for H2O.ai?

I used H2O.ai on several POCs for my previous company, and it helped me find the best model. I needed to determine which model was performing better for job portal data. At that time, H2O.ai was ev...

See all answers

What advice do you have for others considering H2O.ai?

For larger datasets, model computation or model training and testing typically takes considerable time because with individual models, you need to train and test each one. With H2O.ai, these concer...

See all answers

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Compared 25% of the time

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Compared 19% of the time

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Compared 15% of the time

KNIME Business Hub vs H2O.ai

Compared 14% of the time

Altair RapidMiner vs H2O.ai

Compared 8% of the time

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Product Reports

Buyer's Guide

Databricks

September 2025

Download Databricks product report

Buyer's Guide

Data Science Platforms

August 2025

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Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

Overview

Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.

Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.

What are the key features of Databricks?

User-friendly interface: Simplifies operations and usability.
Built-in machine learning libraries: Facilitates machine learning tasks.
Support for multiple programming languages: Enhances flexibility.
Scalability: Efficiently handles growing data needs.
Fast processing: Improves performance and speed.
Automated optimization: Reduces manual efforts.
Data visualization: Provides insightful visuals.
Collaborative features: Enhances teamwork.
Delta Lake performance: Boosts data management.
Seamless cluster management: Simplifies system operations.

What are the benefits or ROI to look for in Databricks reviews?

Efficiency in handling large datasets: Ensures smooth processing.
Interactive workspace environment: Improves user collaboration.
Integration with platforms: Provides connectivity benefits.
Performance optimization: Enhances overall system performance.
Support for data governance and security: Ensures data integrity and protection.

Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.

Databricks

H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.

H2O.ai

Sample Customers

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware

poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco

Buyer's Guide

Databricks vs. H2O.ai

July 2025

Free Report: Databricks vs. H2O.ai

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