Cube vs Matillion Data Productivity Cloud comparison

Cube Dev and Matillion are both solutions in the AI Data Analysis category. Cube Dev is ranked #19 with an average rating of 8.4, while Matillion is ranked #23 with an average rating of 7.3. Cube Dev holds a 0.3% mindshare in ADA, compared to Matillion’s 0.6% mindshare. Additionally, 100% of Cube Dev users are willing to recommend the solution, compared to 92% of Matillion users who would recommend it.

Cube

Read 5 Cube reviews

1,242 Views
994 Comparison Views

100% willing to recommend

Matillion Data Productivity...

Read 28 Matillion Data Productivity Cloud reviews

7,598 Views
1,672 Comparison Views

92% willing to recommend

Cube

Matillion Data Productivity...

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Executive Summary

We performed a comparison between Cube and Matillion Data Productivity Cloud based on real PeerSpot user reviews.

Find out in this report how the two AI Data Analysis solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Cube vs. Matillion Data Productivity Cloud Report (Updated: June 2026).

Buyer's Guide

Cube vs. Matillion Data Productivity Cloud

June 2026

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Helped 909,725 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

Cube

Ranking in AI Data Analysis

19th

Average Rating

8.4

Reviews Sentiment

6.2

Number of Reviews

Ranking in other categories

Embedded BI (10th)

Matillion Data Productivity...

Ranking in AI Data Analysis

23rd

Average Rating

8.4

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

Cloud Data Integration (13th)

Mindshare comparison

As of August 2026, in the AI Data Analysis category, the mindshare of Cube is 0.3%. The mindshare of Matillion Data Productivity Cloud is 0.6%, down from 3.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Data Analysis Mindshare Distribution
Product	Mindshare (%)
Cube	0.3%
Matillion Data Productivity Cloud	0.6%
Other	99.1%

AI Data Analysis

Featured Reviews

Amir Hassan

Data Scientist / Data Analytics Consultant at Fiverr International Ltd

Managing complex data hierarchies has improved analytics but still needs richer hierarchy types

To evaluate Cube's efficiency in handling large data volumes, I use metrics including F1 scores, AUC (Area Under the Curve), and RMC (Root Mean Squares), along with RSE (Root Square Errors). These factors help me gauge the efficiency of the algorithms clearly. Regarding improving Cube, I believe that when we are building hierarchies, I should enhance the type of hierarchies. For instance, in a few countries lacking state systems, there could be a hierarchy without proper categorization—such as continent, country, and city. Therefore, it would be beneficial if I could improve the hierarchies to retrieve data as quickly as possible from Cube.

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Jitendra Jena

Director Axtria - Ingenious Insights! at Axtria - Ingenious Insights

Easy integration and workflow proposals streamline processes

The predefined connectors eliminate the need to write code for connectivity. If you have a predefined connector, it is easy to use with plug and play functionality. The processing time and ease of use are significant benefits. As everyone is moving into AI integration, it will definitely help. When creating workflows, they can propose solutions directly.

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

"Implementation was super smooth, and within two weeks we were up and running and the metrics were exposed in our app."

"NPS improved to approximately eight out of ten for our feature, and internally ticket handling times decreased, allowing reallocation of resources to higher-impact projects."

"Cube completes my tasks very easily and takes less time, allowing me to deliver any project in a timely manner to our clients."

"Cube's caching mechanism impacts my database query loads and response times significantly."

"The loading of data is the most valuable feature of Matillion ETL."

"We allow non-technical people to use Matillion to load data into our data warehouse for reporting. Thus, it is easy enough to use that we don't always have to get a technical person involved in setting up a data movement (ETL)."

"Matillion's technical support is excellent."

"The tool's middle-dimensional structure significantly simplifies obtaining the right data at the appropriate level. This feature makes deploying our applications easier since we utilize a single source without publishing data from various sources."

"It has made our lives much easier, this is what my teammates who were using the other stuff before have said, then they moved it to Snowflake and now it is much easier and faster to use than before."

"The most valuable feature of Matillion ETL is the ETL. The solution is open-source which provides advantages, such as good performance and high efficiency. Additionally, it supports three data types which eliminates predefining the data, and we can write script models in Python."

"Matillion ETL helps manage data movement, ingestion, and transformation through pipelines."

"I would recommend Matillion ETL for any cloud-based operations."

More Matillion Data Productivity Cloud pros

Cons

"Cube's interface can be challenging for non-technical users, needing clearer use-case examples to ease integration into workflows."

"When it comes to the initial setup of Cube, I faced some challenges, including server issues when uploading data from local sources to the Unipi servers."

"I did not see any return on investment from Cube."

"There is no way to create a real template that is not exposed directly in the UI."

"Cube can be improved by enhancing data refresh over multiple tabs."

"There are certain functions that are available in other ETL tools which are still not present in Matillion ETL. It would be good to have more features."

"The product must enhance its near-real-time data capture feature."

"The cost of the solution is high and could be reduced."

"Scalability in Matillion Data Productivity Cloud has some limitations. Depending on the nature of data sets, volume, and mixture of different data, the scalability could be improved as manual code writing is still required."

"Sometimes, we have issues with the solution's stability and need to restart it for three weeks or more."

"It needs integration with more data sources."

"Going forward, I would like them to add custom jobs, since we still have to run these outside of Matillion."

"The product's scalability needs improvement. Perhaps adding more connectors would be beneficial."

More Matillion Data Productivity Cloud cons

Pricing and Cost Advice

Information not available

"Purchasing it through the AWS Marketplace is pretty convenient. There is a little bit of back and forth in terms of the licensing based on the machine size, but it seems to have worked out well. it is convenient to have it all as part of our AWS billing."

"Its price depends on what you expect. You pay on a monthly basis, but there is a possibility to have special contracts depending on the installation."

"The absence of licensing commitments makes it easy to experiment with the tool, and if we decide it's not suitable, we can simply stop the ETL instance and cease incurring charges."

"The AWS pricing and licensing are a cost-effective solution for data integration needs."

"I think it is cost conscious. It used to be very cheap and they have more recently bumped up the pricing, so it is competitive now."

"A rough estimation of the cost is around 20,000 dollars a month, however, this is dependent on the machine used and how Matillion ETL is used."

"I have heard from my manager and other higher ups, "This product is cheaper than other things on the market," and they have done the research."

"The price of Matillion ETL is reasonable."

More Matillion Data Productivity Cloud pricing and cost advice

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Top Industries

By visitors reading reviews

No data available

Construction Company

11%

Financial Services Firm

Computer Software Company

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	6
Midsize Enterprise	10
Large Enterprise	11

Questions from the Community

What is your experience regarding pricing and costs for Cube?

The cost is around $1,500 per month. The exact number is not coming to my mind, but it is approximately $1,500 or $200 per month.

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What needs improvement with Cube?

See all answers

What is your primary use case for Cube?

In my recent projects with Cube, I was tasked with finding crashes and the reasons behind them using three datasets: people, crash, and vehicles. I had to merge them and preprocess them, clean and ...

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What is your experience regarding pricing and costs for Matillion ETL?

The pricing is managed by the tooling team. The pricing is moderate, neither expensive nor cheap.

See all answers

What needs improvement with Matillion ETL?

The main areas for improvement are AI features and scalability.

See all answers

What is your primary use case for Matillion ETL?

For the ETL, we are using Matillion Data Productivity Cloud. We have skilled resources for Matillion Data Productivity Cloud, which is why we are using it. The infrastructure is provided by the cus...

See all answers

Comparisons

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Compared 54% of the time

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Compared 4% of the time

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Product Reports

Buyer's Guide

Cube

August 2026

Download Cube product report

Buyer's Guide

Matillion Data Productivity Cloud

August 2026

Matillion Data Productivity Cloud report

Download Matillion Data Productivity Cloud product report

Also Known As

No data available

Matillion ETL for Redshift, Matillion ETL for Snowflake, Matillion ETL for BigQuery

Overview

Cube offers a dynamic business intelligence platform tailored for efficient data transformation and analytics. Engineered for scalability and performance, Cube adapts to complex data environments, enhancing data accessibility and operational insights.

Cube facilitates seamless integration into existing data ecosystems, bringing enhanced data processing capabilities to businesses. Utilized by companies seeking streamlined analytical processes, Cube's architecture supports custom data transformations while ensuring consistent data delivery. Its flexibility allows implementation across varied data sources, improving decision-making and operational efficiency.

What are the key features of Cube?

Data Modeling: Tailor data structures for business needs with ease.
Scalability: Handles increasing data loads efficiently.
Custom APIs: Provides REST and GraphQL APIs for versatile data interaction.
Caching Layer: Speeds up query performance significantly.
Security: Offers robust access controls for data protection.

What benefits should be expected from using Cube?

Improved Efficiency: Streamlines data management and analytics tasks.
Cost Reduction: Lowers operational costs by enhancing data processing.
Enhanced Decision-Making: Provides actionable insights faster.
Operational Flexibility: Adapts to evolving business requirements.

In industries like finance and retail, Cube is implemented to optimize data flow and analytics processing. Its features support complex data requirements, allowing these industries to improve market responsiveness and operational strategies.

Cube Dev

Matillion Data Productivity Cloud offers a user-friendly platform for seamless integration and dynamic data handling, favored for simplifying ETL processes with minimal coding and ensuring robust performance in complex data tasks.

Matillion Data Productivity Cloud integrates effortlessly with platforms like AWS, Snowflake, and SQL databases, providing tools for efficient data migration, transformation, and cloud warehousing. It supports large datasets with swift management, making it valued for its graphical interface that eases ETL processes for non-technical users. Automation features ensure scalability and dynamic data handling across diverse sources, while security and cost-effectiveness enhance its appeal. Enhancements in database connectivity, interface design, and multi-environment support would refine user experience, with growing demands for real-time data capture, SAP connectivity, and frequent API updates.

What are the most important features?

User-friendly GUI: Simplifies ETL processes with minimal coding.
Seamless Integration: Compatible with AWS, Snowflake, and more.
Dynamic Data Handling: Supports diverse data sources with scalability.
Change Data Capture: Facilitates efficient data updates.
Error Reporting: Ensures workflow accuracy and troubleshooting.

What benefits and ROI can users expect?

Cost Effectiveness: Offers competitive pricing for extensive features.
Security Assurance: Provides strong data protection measures.
Efficiency in Large Datasets: Fast data processing and management.
Minimal Coding Requirement: Enhances accessibility for non-technical users.

In industries like finance, healthcare, and retail, Matillion Data Productivity Cloud is implemented for transforming data operations. Companies leverage it for its speed in data processing and integration capability, facilitating rapid adaptation to data-driven insights crucial in these sectors.

Matillion

Sample Customers

Information Not Available

Thrive Market, MarketBot, PWC, Axtria, Field Nation, GE, Superdry, Quantcast, Lightbox, EDF Energy, Finn Air, IPRO, Twist, Penn National Gaming Inc

Buyer's Guide

Cube vs. Matillion Data Productivity Cloud

June 2026

Free Report: Cube vs. Matillion Data Productivity Cloud

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