Dremio vs H2O.ai comparison

Dremio and H2O.ai are both solutions in the Data Science Platforms category. Dremio is ranked #11 with an average rating of 8.7, while H2O.ai is ranked #15 with an average rating of 7.7. Dremio holds a 2.3% mindshare in Data Science Platforms, compared to H2O.ai’s 1.9% mindshare. Additionally, 100% of Dremio users are willing to recommend the solution, compared to 100% of H2O.ai users who would recommend it.

Dremio

Read 11 Dremio reviews

3,361 Views
1,983 Comparison Views

100% willing to recommend

H2O.ai

Read 10 H2O.ai reviews

1,331 Views
1,238 Comparison Views

100% willing to recommend

Dremio

H2O.ai

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Nov 9, 2025

H2O.ai and Dremio are competing in the data management and analytics space. H2O.ai holds a slight advantage in pricing and support, while Dremio is frequently considered superior due to its advanced features.

Features: H2O.ai provides a robust machine learning platform focusing on machine learning capabilities with automated machine learning, a scalable architecture, and comprehensive tools for data scientists. Dremio offers advanced data query optimization through its data virtualization technology, seamless integration with major cloud platforms, and enhanced data access and performance for enterprises. These features make Dremio a popular choice for organizations prioritizing data insights and analytics performance.

Room for Improvement: H2O.ai could improve in areas such as intuitive user interfaces, enhanced cloud compatibility, and advanced data visualization tools. Dremio might focus on simplifying its deployment process, better user-friendly documentation, and reducing initial setup complexity to enhance its accessibility to smaller enterprises.

Ease of Deployment and Customer Service: H2O.ai's deployment is relatively straightforward, supported by comprehensive documentation and a strong community. It offers responsive customer service, which contributes to its supportive deployment environment. Dremio, while complex to deploy and requiring additional expertise, balances this with excellent service and thorough guidance within its extensive environment.

Pricing and ROI: H2O.ai is recognized for its cost-effectiveness, making it attractive to budget-conscious entities due to its favorable cost-to-benefit ratio. Dremio's higher initial costs are offset by substantial returns on investment, driven by its efficiency and scalability that appeal to larger enterprises seeking comprehensive data solutions. The pricing structure, although steep, aligns with Dremio's advanced capabilities, ensuring long-term value for suitable organizations.

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

Buyer's Guide

Dremio vs. H2O.ai

December 2025

Download the complete report

Helped 881,114 peers since 2012

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

Dremio

Ranking in Data Science Platforms

11th

Average Rating

8.4

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (5th)

H2O.ai

Ranking in Data Science Platforms

15th

Average Rating

7.6

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Model Monitoring (4th)

Mindshare comparison

As of January 2026, in the Data Science Platforms category, the mindshare of Dremio is 2.3%, down from 4.2% compared to the previous year. The mindshare of H2O.ai is 1.9%, 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 (%)
Dremio	2.3%
H2O.ai	1.9%
Other	95.8%

Data Science Platforms

Featured Reviews

Corrr Moray

SR BI developer at BRQ Digital Solutions

Has simplified complex data integration workflows and supported consistent reporting across multiple sources

We also have a close relationship with the team that does the Dremio maintenance for the database, like upgrading the versions and they know about some specific problems we had in the past, such as a memory leak. We had a memory leak on some versions, which sometimes stopped the service. Since we are using Dremio installed like a server, not a SaaS solution, many times we need to stop and restart the service to clear all the cache and all that, and this is the thing I should add. I see that many times the new versions of Dremio have not fixed old bugs, and in some new versions, old problems that were previously fixed come back again, so I think the upgrade part could use improvement. I remember using some features in the past, like pivot tables, which proved to be really difficult, but I know this is a fault also for other vendors. Pivoting, transposing, and unpivoting are often not so good. CTEs also many times prove to be not so good, so I think these two main items could be improved significantly if they standardize them.

Read full review

Muhammad Adnan

Senior Manager - AI at Shamal Holding

Have improved machine learning model automation and reduced decision-making time

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. H2O.ai could benefit from enhanced integration with real-time versus offline data sources, as well as improvements in productionalization solutions, including better deployment options on platforms like Azure and CI/CD integration. One of the features I'd like to see included in upcoming releases of H2O.ai pertains to the growing trend of Generative AI, with applications for LLM-based models and vector databases. I would like to see a solution similar to Azure AI Foundry, which provides the flexibility to integrate different LLMs into applications, including H2O-GPT and other models for varied applications.

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 most valuable feature of Dremio is it can sit on top of any other data storage, such as Amazon S3, Azure Data Factory, SGFS, or Hive. The memory competition is good. If you are running any kind of materialized view, you'd be running in memory."

"Overall, you can rate it as eight out of ten."

"Everyone uses Dremio in my company; some use it only for the analytics function."

"Dremio allows querying the files I have on my block storage or object storage."

"Dremio gives you the ability to create services which do not require additional resources and sterilization."

"Dremio enables you to manage changes more effectively than any other data warehouse platform. There are two things that come into play. One is data lineage. If you are looking at data in Dremio, you may want to know the source and what happened to it along the way or how it may have been transformed in the data pipeline to get to the point where you're consuming it."

"We primarily use Dremio to create a data framework and a data queue."

"Dremio has positively impacted my organization as nowadays we are connected to multiple databases from multiple environments, multiple APIs, and applications, and Dremio organizes everything in an amazing way for me."

More Dremio 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."

"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."

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

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

"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."

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

"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."

More H2O.ai pros

Cons

"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."

"They have an automated tool for building SQL queries, so you don't need to know SQL. That interface works, but it could be more efficient in terms of the SQL generated from those things. It's going through some growing pains. There is so much value in tools like these for people with no SQL experience. Over time, Dermio will make these capabilities more accessible to users who aren't database people."

"Dremio doesn't support the Delta connector. Dremio writes the IT support for Delta, but the support isn't great. There is definitely room for improvement."

"They need to have multiple connectors. Starburst is rich in connectors, however, they are lacking Salesforce connectivity as of today."

"We had a memory leak on some versions, which sometimes stopped the service."

"Dremio could be improved by making it easier for data cataloging, especially when working with open table formats, as you have to choose a data format and then go into it."

"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."

"There are performance issues at times due to our limited experience with Dremio, and the fact that we are running it on single nodes using a community version."

More Dremio cons

"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."

"The model management features could be improved."

"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."

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

"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."

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

More H2O.ai cons

Pricing and Cost Advice

"Dremio is less costly competitively to Snowflake or any other tool."

"Right now the cluster costs approximately $200,000 per month and is based on the volume of data we have."

"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."

See which vendors are best for you

Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.

See recommendations

881,114 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

28%

Computer Software Company

Manufacturing Company

Healthcare Company

Financial Services Firm

15%

Computer Software Company

11%

Educational Organization

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	1
Midsize Enterprise	5
Large Enterprise	5

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

Questions from the Community

What do you like most about Dremio?

Dremio allows querying the files I have on my block storage or object storage.

See all answers

What is your experience regarding pricing and costs for Dremio?

I don't have information about pricing, setup cost, and licensing for Dremio, so I am not entitled to discuss it.

See all answers

What needs improvement with Dremio?

I wouldn't say there is anything Dremio can be improved on. If I could change something, I would say many developers and programmers, when they are starting to work in this specific field or area, ...

See all answers

What needs improvement with H2O.ai?

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. Cu...

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

Databricks vs Dremio

Compared 33% of the time

Snowflake vs Dremio

Compared 14% of the time

Starburst Enterprise vs Dremio

Compared 6% of the time

Dataiku vs Dremio

Compared 6% of the time

Amazon SageMaker vs Dremio

Compared 5% of the time

More Dremio Competitors

Databricks vs H2O.ai

Compared 29% of the time

Dataiku vs H2O.ai

Compared 12% of the time

Amazon SageMaker vs H2O.ai

Compared 9% of the time

KNIME Business Hub vs H2O.ai

Compared 8% of the time

Microsoft Azure Machine Learning Studio vs H2O.ai

Compared 8% of the time

More H2O.ai Competitors

Product Reports

Buyer's Guide

Dremio

January 2026

Download Dremio product report

Buyer's Guide

Data Science Platforms

January 2026

Download H2O.ai product report

Also Known As

Dremio AWS - BYOL

No data available

Overview

Dremio offers a comprehensive platform for data warehousing and data engineering, integrating seamlessly with data storage systems like Amazon S3 and Azure. Its main features include scalability, query federation, and data reflection.

Dremio's core strength lies in its ability to function as a robust data lake query engine and data warehousing solution. It facilitates the creation of complex queries with ease, thanks to its support for Apache Airflow and query federation across endpoints. Despite challenges with Delta connector support, complex query execution, and expensive licensing, users find it valuable for managing ad-hoc queries and financial data analytics. The platform aids in SQL table management and BI traffic visualization while reducing storage costs and resolving storage conflicts typical in traditional data warehouses.

What are Dremio's most valuable features?

Native Error Interfaces: Simplifies error detection and troubleshooting
Integration with Apache Airflow: Enhances workflow automation capabilities
Scalability: Effortlessly handles growing data demands
Role-Based Access Management: Provides secure data access control
Data Reflection: Optimizes query performance automatically
Query Federation: Supports queries across multiple endpoints seamlessly
Data Cataloging and Virtualization: Offers streamlined data organization

What benefits and ROI should users look for?

Reduced Data Storage Costs: Efficiently manages data to minimize storage expenses
Improved Query Performance: Enhances processing speed for large datasets
Flexibility in Integration: Easily connects with storage solutions like Oracle and MySQL
Enhanced Data Management: Supports comprehensive data analysis workflows

Dremio is primarily implemented in industries requiring extensive data engineering and analytics, including finance and technology. Companies use it for constructing data frameworks, efficiently processing financial analytics, and visualizing BI traffic. It acts as a viable alternative to AWS Glue and Apache Hive, integrating seamlessly with multiple databases, including Oracle and MySQL, offering robust solutions for data-driven strategies. Despite some challenges, its ability to reduce data storage costs and manage complex queries makes it a favorable choice among enterprise users.

Dremio

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

UBS, TransUnion, Quantium, Daimler, OVH

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

Buyer's Guide

Dremio vs. H2O.ai

December 2025

Free Report: Dremio vs. H2O.ai

Find out what your peers are saying about Dremio vs. H2O.ai and other solutions. Updated: December 2025.

DOWNLOAD NOW

881,114 professionals have used our research since 2012.

See our Dremio vs. H2O.ai report.

See our list of best Data Science Platforms vendors.

We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.