Sold by: Cube Dev, Inc.
Connect siloed data, drive consistent metrics, and power your AI and analytics with context.
4.5
Overview
Cube Cloud is a universal semantic layer for data and app development teams that makes it easy to connect siloed data and create consistent metrics that are accessible to any data consumer-AI, BI, spreadsheets, and embedded analytics. The solution provides unmatched integrations and interoperability, supporting a robust set of deployment options, data connectivity, coding languages, and native APIs so that you can build AI and analytics solutions to fit your unique modern data stack requirements.
With Cube Cloud, business data becomes consistent, accurate, easy to access, and most importantly, trusted. Data engineers and application developers use Cube Cloud's code-first, developer-oriented platform to:
- Organize and govern data from cloud data warehouses into centralized, consistent, and
reusable data models and business definitions.
- Apply software engineering best practices and processes to data management: CI/CD,
isolated environments, and version control with Git integration.
- Use intelligent capabilities like data model code generation and front-end embedded
analytics code generation to increase productivity.
- Optimize query performance and save on cloud data usage with pre-aggregation caching
capabilities.
- Deliver data to any downstream tool via data APIs: SQL, REST, GraphQL, AI, and MDX.
Cube accelerates trusted data-driven decisions, delivering better experiences to employees inside the organization, customers outside the organization, and even machines with our native OpenAI integrations. Build Generative AI experiences with the AI API. For internal BI use cases, Cube Cloud provides a semantic catalog and Generative AI capabilities to simplify discovery, exploration, and access to modeled data and downstream, connected BI content for data analysts and business users. Add unlimited named user accounts to allow anyone to search and reuse trusted data products and perform natural language queries in a simplified, business user friendly interface.
When you choose Cube Cloud on AWS, you can easily integrate with the following AWS services and more:
- Redshift, Athena, Aurora, RDS
- QuickSight
Highlights
- Easily deliver a lightning fast embedded analytics experience to delight your customers
- Sync and connect all your BI tools to drive consistency across your company
- Bring context to your LLMs, AI agents, and bots by adding on a semantic layer
Details
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Delivery method
Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Premium | Cube Cloud Premium ($10K+ annual minimum commitment) | $10,000.00 |
Enterprise | Cube Cloud Enterprise ($20K+ annual minimum commitment) | $20,000.00 |
Enterprise Premier | Cube Cloud Enterprise ($40K+ annual minimum commitment) | $40,000.00 |
Vendor refund policy
All fees are non-refundable and non-cancellable except as required by law.
Legal
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
Email support services are available from Monday to Friday
support@cube.dev
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By Cube Dev, Inc.
By MicroStrategy, Inc.
By Pyramid Analytics BV
Top
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In Data Analysis
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In Financial Services, Business Intelligence & Advanced Analytics
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In Amazon Redshift, Analytic Platforms
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Semantic Data Modeling
Code-first platform for organizing and governing data from cloud data warehouses into centralized, consistent, and reusable data models with business definitions.
Multi-Protocol Data API Support
Delivers data to downstream tools via multiple API types including SQL, REST, GraphQL, and MDX protocols.
Query Performance Optimization
Implements pre-aggregation caching capabilities to optimize query performance and reduce cloud data warehouse usage costs.
Version Control and CI/CD Integration
Applies software engineering best practices including CI/CD pipelines, isolated environments, and Git integration for data management.
Cloud Data Warehouse Connectivity
Supports native integrations with major cloud data warehouses including Redshift, Athena, Aurora, and RDS.
Generative AI Integration
Conversational AI and GenAI-powered workflows enable users to analyze data, build visualizations, and create dashboards through natural language interactions without requiring advanced technical skills.
Cloud-Native Architecture
Microservices and cloud container-based infrastructure with RESTful APIs and rich SDKs support continuous iterations, fast development, and higher fault tolerance while preventing vendor lock-in.
Multi-Source Data Integration
Support for connecting to diverse data sources including Snowflake and Databricks with capability to blend multiple data sources together and process both structured and unstructured data.
Semantic Graph Technology
Industry-leading semantic graph combined with AI ensures data transparency, reliability, and governance at enterprise scale for large-scale analytics applications.
Embedded Analytics Capability
Seamless integration of analytics into custom applications through embedded analytics functionality and developer-friendly APIs for creating enterprise applications and custom product experiences.
Automatic Data Modeling and Preparation
Automatic data modeling, data preparation and formulas with data science integration into interactive visualizations, dashboards and reports
In-Place Analytics Architecture
Cloud-scale in-place analytics that leverages underlying data sources for real-time data access without pre-loading data into platform memory, enabling analysis of massive datasets
No-Code Visual Interface
Intuitive no-code visual user experience enabling self-service data analysis and content creation for business users and non-technical users
Role-Based Access Control and Governance
Role-based data and content access security with centralized control through governed data discovery and multi-tenancy capabilities
Multi-Source Data Integration
Direct integration with AWS and cloud data sources including Amazon Redshift, RDS, EMR, S3, Athena, SAP HANA, SAP BW4/HANA, Exasol, and Snowflake
Standard contract
No
Customer reviews
Alejo B.
Powerful Semantic Layer, Poor Cloud Support
Reviewed on Feb 03, 2026
Review provided by G2
What do you like best about the product?
I find Cube to be a very sophisticated product that allows me to design complex metrics and dimensions in a scalable and composable way. I also appreciate the useful caching (preaggregation) layer. Having a strong common semantic layer is key for us as a data product company serving dashboards and insights to our customers.
What do you dislike about the product?
The service provided by the cloud team is horrible. We are a paying customer, with an annual commitment, and are having big issues when scaling that are not being solved by them. We don't have visibility into what is happening.
What problems is the product solving and how is that benefiting you?
Cube closes the gap between our data model and business metrics, supporting complex metric design with a scalable semantic layer and a useful caching layer.
Real Estate
Super Easy Access to the DEV team
Reviewed on Sep 18, 2025
Review provided by G2
What do you like best about the product?
Cube is a platform that presents the data in a very clear way. When we do the selection of column, it can also automatically generate sql code for SSMS. The query history is also great for tracking the error. When there's any problem, cube dev team is super easy to access, and they respond very fast. They really provide a strong tech support.
What do you dislike about the product?
Sometimes they query history loaded longer than expected.
What problems is the product solving and how is that benefiting you?
It can show data very clearly and allow me to select any column to see the data in an easy way.
Commercial Real Estate
Great for AI applications and general data apps!
Reviewed on Jan 21, 2025
Review provided by G2
What do you like best about the product?
I'm using CubeJS for a lot of data apps on my company and it is really incredible for leveraging data for my applications. I also use CubeJS daily for manual analytics, like taking a look at the charts or copying some data to perform some stronger statistical analysis.
The interface simplicity of consumption for REST APIs is really powerful for integrating in any language and maintaining a standardized consumption around the tech ecosystem.
This can also be used as a "Feature Store" of some sorts, together with the preAggregations layer, for ML and AI applications, making it really really simple to roll them out.
I've already used the Customer Support and their response was really fast and the person helping me was also really proactive.
The ability to test changes to a cube or creating a cube via Git branches is **really powerful**! It really helps!
Finally, implementing cubes, measures, and also configuring dimensions is way too simple: we use it to connect to BigQuery and maintaining the semantics of metrics on CubeJS, which makes it so easy to understand data.
The interface simplicity of consumption for REST APIs is really powerful for integrating in any language and maintaining a standardized consumption around the tech ecosystem.
This can also be used as a "Feature Store" of some sorts, together with the preAggregations layer, for ML and AI applications, making it really really simple to roll them out.
I've already used the Customer Support and their response was really fast and the person helping me was also really proactive.
The ability to test changes to a cube or creating a cube via Git branches is **really powerful**! It really helps!
Finally, implementing cubes, measures, and also configuring dimensions is way too simple: we use it to connect to BigQuery and maintaining the semantics of metrics on CubeJS, which makes it so easy to understand data.
What do you dislike about the product?
I still think documentation is a bit confusing for some features.
I've faced some issues with preAggregations where some measuers were they were being summed instead of being averaged over for `avg` types. Some logic for the `rollup` timeout is a bit confusing.
Although the REST API interface doens't really need any framework, I feel that a simple SDK providing the objects for serialization would be nice to have.
Some more complex charts in the Playground would also be nice to have, together with a simple export like CSV/JSON from the Playground (I do understand that the Playground is... a Playground... but sometimes I use it as an analytics tool to fetch some informations).
I think that we also lack a propert LSP (or a type checker) for Cube on Javascript. Sometimes I makes some mistakes that are related to Cube JS syntax (use of AI on the Code Editor also makes it more prone to error). This would be really helpful. Some features can only be tested once in production (like preAggregations syntax), which makes it a bit harder to integrate.
I've faced some issues with preAggregations where some measuers were they were being summed instead of being averaged over for `avg` types. Some logic for the `rollup` timeout is a bit confusing.
Although the REST API interface doens't really need any framework, I feel that a simple SDK providing the objects for serialization would be nice to have.
Some more complex charts in the Playground would also be nice to have, together with a simple export like CSV/JSON from the Playground (I do understand that the Playground is... a Playground... but sometimes I use it as an analytics tool to fetch some informations).
I think that we also lack a propert LSP (or a type checker) for Cube on Javascript. Sometimes I makes some mistakes that are related to Cube JS syntax (use of AI on the Code Editor also makes it more prone to error). This would be really helpful. Some features can only be tested once in production (like preAggregations syntax), which makes it a bit harder to integrate.
What problems is the product solving and how is that benefiting you?
CubeJS helps me solve the issue of integrating data from multiple sources to Applications: from general software to AI-powered applications, CubeJS allows us to have low latency data fetching and only worrying with what to do with the data.
Rafael L.
What a modern semantic layer solution should look like
Reviewed on Oct 09, 2023
Review provided by G2
What do you like best about the product?
Using CubeJS Cloud to build our semantic layer has been great! It really just takes a lot of development effort off our shoulders. This is especially great for small teams that don't have DevOps engineers available to help create and monitor a platform. The development and maintenance have been simple to maintain. Their support is also great where I could solve a build issue with a live chat in a short time. There are many features including different endpoints to consume data, which makes embedding analytics into our product straightforward.
What do you dislike about the product?
I still feel that integration with BI tools is not totally straightforward yet, some query aggregations fail and require some tweaks. I still have to test the new "Semantic Layer Sync" feature. Also, a thing that was very great about dbt metrics that I miss here in CubeJS is the ability to create a new metric based on other metrics as long as they have the same time granularity, CubeJS could find a way to implement something flexible as this as well.
What problems is the product solving and how is that benefiting you?
We use all major features: serving metrics on endpoints, embedded analytics in our product front-end, and feed BI tools.
Research
Easy to setup analytics
Reviewed on Sep 21, 2023
Review provided by G2
What do you like best about the product?
- declarative schema definitions make it easy to built UI interfaces on top of your data
- built-in cache
- extensibility through express middlewares
- great slack community
- easy to get setup
- built-in cache
- extensibility through express middlewares
- great slack community
- easy to get setup
What do you dislike about the product?
we ran into an issue with cube cloud processes restarting too frequently which meant the entire schema needed to be reloaded on subsequent requests which took nearly 5s and triggered our latency alarms. luckily due to cube's extensibility we were able to implement a custom express middleware to trigger loading of the schema when the instance started up which worked around the problem for us.
the schema compiler is a little quirky and has too much magic which makes it a little more difficult to define schemas in typescript, but not insurmountable.
personally I don't like the online schema editor since we discourage folks from making changes outside of the version control. I wish we could have disabled this feature.
out-of-the-box scripts to scaffold out schemas would be nice. we ended up rolling our own.
cube-sql is interesting, but the lack of ability to do basic math in queries made it unusable for us.
the schema compiler is a little quirky and has too much magic which makes it a little more difficult to define schemas in typescript, but not insurmountable.
personally I don't like the online schema editor since we discourage folks from making changes outside of the version control. I wish we could have disabled this feature.
out-of-the-box scripts to scaffold out schemas would be nice. we ended up rolling our own.
cube-sql is interesting, but the lack of ability to do basic math in queries made it unusable for us.
What problems is the product solving and how is that benefiting you?
Cube makes it easy to build analytics tools on your data.