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

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: 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

"The solution is very simple and stable."

"It is a cost-effective solution."

"Ability to work collaboratively without having to worry about the infrastructure."

"We can scale the product."

"Databricks' most valuable features are the workspace and notebooks. Its integration, interface, and documentation are also good."

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

"One of the features provides nice interactive clusters, or compute instances that you don't really need to manage often."

"I work in the data science field and I found Databricks to be very useful."

More Databricks pros

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

"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."

"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."

"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."

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

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

"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."

"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."

More H2O.ai pros

Cons

"The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration."

"Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks."

"The product could be improved by offering an expansion of their visualization capabilities, which currently assists in development in their notebook environment."

"I would like to see more documentation in terms of how an end-user could use it, and users like me can easily try it and implement use cases."

"Cluster failure is one of the biggest weaknesses I notice in our Databricks."

"I would like to see the integration between Databricks and MLflow improved. It is quite hard to train multiple models in parallel in the distributed fashions. You hit rate limits on the clients very fast."

"In the next release, I would like to see more optimization features."

"The product needs samples and templates to help invite users to see results and understand what the product can do."

More Databricks cons

"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 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."

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

"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."

"The model management features could be improved."

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

More H2O.ai cons

Pricing and Cost Advice

"Databricks are not costly when compared with other solutions' prices."

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

"The solution requires a subscription."

"I am based in South Africa, where it is expensive adapting to the cloud, and then there is the price for the tool itself."

"The licensing costs of Databricks is a tiered licensing regime, so it is flexible."

"We find Databricks to be very expensive, although this improved when we found out how to shut it down at night."

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

"Price-wise, I would rate Databricks a three out of five."

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 18% 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

October 2025

Download Databricks product report

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 offers a scalable, versatile platform that integrates seamlessly with Spark and multiple languages, supporting data engineering, machine learning, and analytics in a unified environment.

Databricks stands out for its scalability, ease of use, and powerful integration with Spark, multiple languages, and leading cloud services like Azure and AWS. It provides tools such as the Notebook for collaboration, Delta Lake for efficient data management, and Unity Catalog for data governance. While enhancing data engineering and machine learning workflows, it faces challenges in visualization and third-party integration, with pricing and user interface navigation being common concerns. Despite needing improvements in connectivity and documentation, it remains popular for tasks like real-time processing and data pipeline management.

What features make Databricks unique?

Notebook: Enables collaborative work among team members.
Delta Lake: Optimizes data management operations.
Unity Catalog: Provides governance over data assets.
Cloud Integration: Seamlessly connects with major cloud platforms.

What benefits can users expect from Databricks?

Versatility: Supports diverse applications in data science and engineering.
Performance: Delivers efficient handling of large-scale analytics tasks.
Collaboration: Enhances teamwork in data projects.
Unified Environment: Centralizes machine learning and analytics activities.

In the tech industry, Databricks empowers teams to perform comprehensive data analytics, enabling them to conduct extensive ETL operations, run predictive modeling, and prepare data for SparkML. In retail, it supports real-time data processing and batch streaming, aiding in better decision-making. Enterprises across sectors leverage its capabilities for creating secure APIs and managing data lakes effectively.

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

Free Report: Databricks vs. H2O.ai

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