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 #18 with an average rating of 7.7. Databricks holds a 13.9% mindshare in Data Science Platforms, compared to H2O.ai’s 1.7% 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

20,775 Views
8,933 Comparison Views

96% willing to recommend

H2O.ai

Read 10 H2O.ai reviews

1,179 Views
1,108 Comparison Views

100% willing to recommend

Databricks

H2O.ai

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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: September 2025).

Buyer's Guide

Databricks vs. H2O.ai

September 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

18th

Average Rating

7.6

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Model Monitoring (4th)

Mindshare comparison

As of October 2025, in the Data Science Platforms category, the mindshare of Databricks is 13.9%, down from 19.2% compared to the previous year. The mindshare of H2O.ai is 1.7%, up from 1.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Market Share Distribution
Product	Market Share (%)
Databricks	13.9%
H2O.ai	1.7%
Other	84.4%

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

"In the manufacturing industry, Databricks can be beneficial to use because of machine learning. It is useful for tasks, such as product analysis or predictive maintenance."

"The Delta Lake data type has been the most useful part of this solution. Delta Lake is an opensource data type and it was implemented and invented by Databricks."

"The solution is easy to use and has a quick start-up time due to being on the cloud."

"Databricks is a robust solution for big data processing, offering flexibility and powerful features."

"Databricks gives us the ability to build a lakehouse framework and do everything implicit to this type of database structure. We also like the ability to stream events. Databricks covers a broad spectrum, from reporting and machine learning to streaming events. It's important for us to have all these features in one platform."

"Databricks helps crunch petabytes of data in a very short period of time."

"Imageflow is a visual tool that helps make it easier for business people to understand complex workflows."

"I like the ability to use workspaces with other colleagues because you can work together even without seeing the other team's job."

More Databricks pros

"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."

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

"Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O."

"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."

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

"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."

"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."

More H2O.ai pros

Cons

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

"Scalability is an area with certain shortcomings. The solution's scalability needs improvement."

"I'm not the guy that I'm working with Databricks on a daily basis. I'm on the management team. However, my team tells me there are limitations with streaming events. The connectors work with a small set of platforms. For example, we can work with Kafka, but if we want to move to an event-driven solution from AWS, we cannot do it. We cannot connect to all the streaming analytics platforms, so we are limited in choosing the best one."

"Doesn't provide a lot of credits or trial options."

"I would like it if Databricks adopted an interface more like R Studio. When I create a data frame or a table, R Studio provides a preview of the data. In R Studio, I can see that it created a table with so many columns or rows. Then I can click on it and open a preview of that data."

"I would like it if Databricks made it easier to set up a project."

"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."

"Overall it's a good product, however, it doesn't do well against any individual best-of-breed products."

More Databricks cons

"The model management features could be improved."

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

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

"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."

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

"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."

"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."

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

More H2O.ai cons

Pricing and Cost Advice

"I would rate Databricks' pricing seven out of ten."

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

"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."

"The licensing costs of Databricks depend on how many licenses we need, depending on which Databricks provides a lot of discounts."

"The cost for Databricks depends on the use case. I work on it as a consultant, so I'm using the client's Databricks, so it depends on how big the client is."

"I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly."

"Databricks' cost could be improved."

"The price is okay. It's competitive."

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

18%

Computer Software Company

Manufacturing Company

Healthcare Company

Financial Services Firm

16%

Computer Software Company

14%

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

Comparisons

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Compared 9% of the time

Dataiku vs Databricks

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Informatica PowerCenter vs Databricks

Compared 7% of the time

Dremio vs Databricks

Compared 5% of the time

Tableau Enterprise vs Databricks

Compared 4% of the time

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Dataiku vs H2O.ai

Compared 24% of the time

Amazon SageMaker vs H2O.ai

Compared 19% of the time

Microsoft Azure Machine Learning Studio vs H2O.ai

Compared 15% of the time

KNIME Business Hub vs H2O.ai

Compared 15% 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

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Buyer's Guide

Data Science Platforms

September 2025

Download H2O.ai product report

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

September 2025

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