Cube

Overview

Cube is an analytics platform for teams that want business metrics to stay consistent across dashboards, embedded products and AI agents. It is best suited to data teams building a shared model for internal or customer-facing analytics; the request quotas and short history retention make plan choice important.

Cube Cloud pairs a production-scale platform with the open-source Cube Core project. Core can process API requests against pre-aggregated data in Cube Store or raw data in connected databases, giving teams a way to serve analytics through an API as well as through workbooks and dashboards.

Cube Dev, Inc. was founded in 2019. Its co-founders are Artyom Keydunov and Pavel Tiunov.

Key features

• Governed semantic model: Teams encode business definitions once so dashboards, embedded apps and AI agents draw on the same metrics. That is a strong fit when consistency across several analytics surfaces matters, but it requires teams to maintain a shared model rather than define terms independently in each tool.
• Analytics workspace: Even the Free plan includes a semantic layer IDE, workbooks, dashboard publishing, semantic model and workbook agents, and analytics chat. The breadth makes it useful for evaluating a workflow, though five workbooks and 1,000 daily requests limit how far a team can take it without upgrading.
• Data and transformation integrations: Listed data sources include PostgreSQL, Snowflake, ClickHouse, Databricks, BigQuery, MySQL, Oracle, Redshift, MongoDB and Trino. Cube also integrates with dbt, Dagster, Prefect, Airflow and Coalesce.io for transformation and orchestration. This range suits teams working across common databases and data pipelines; the fit depends on whether their stack is represented.
• API serving and caching: Cube Core serves API requests from pre-aggregations held in Cube Store or from raw data in connected databases; Cube Store stores those pre-aggregations. This supports API-based analytics delivery, with a choice of prepared data or direct database queries.
• Security and deployment: Cube says data is encrypted at rest, API access uses tokens, and application traffic is encrypted over SSL/TLS 1.2. Cube Cloud states compliance with SOC 2 Type II, HIPAA and GDPR. It can run as a managed multi-tenant service on AWS or GCP, in a VPC on AWS, GCP or Azure, or on a customer's cloud through BYOC. Those deployment and compliance options are relevant to teams with specific hosting or governance needs, but the stated security measures alone do not establish fit for every organization.

Pricing

Cube uses a freemium model, with paid plans starting at $40 a month per developer. The Free plan costs nothing, billed Free forever. It allows up to 5 workbooks, 1,000 daily requests and 1 day of query history retention, with community support. That is a useful entry point for a small evaluation, but the workbook and request caps are restrictive for broader use.

Starter costs $40 a month per developer. It includes 10K daily requests shared or 50K dedicated, 1 day of query history retention, and limited chat and email support. It is the more practical step for teams that have outgrown the free request allowance, though history retention stays at one day.

Premium costs $80 a month per developer. It includes unlimited queries, 7 days of query history retention, and chat and email support. Explorer and Viewer roles are priced separately, so teams should account for those roles when estimating the cost of broader access. Premium suits teams that need higher query capacity and longer history, but its per-developer price and separately priced roles matter as usage expands.

Enterprise has custom pricing and custom query history retention. It includes a dedicated single-tenant installation and a dedicated CSM, making it the option for organizations that need those deployment and support arrangements. Free support is community-based; Starter has limited chat and email support, while Premium adds chat and email support.

Platforms

Cube is available via API, Linux, self-hosted deployment and the web. Its hybrid deployment model gives teams managed, VPC and bring-your-own-cloud choices, including Azure for VPC deployments.

Who it's for

Cube is a strong candidate for data teams that need governed metrics shared across business intelligence, embedded analytics and AI agents, especially when API access, row-level security, dimensions and joins are part of the desired model. It is less compelling for teams seeking a generous free tier for sustained use: Free caps workbooks and daily requests, and even Starter retains only one day of query history.

Pros and cons

• Pro: One governed semantic model can serve dashboards, embedded apps and agents, reducing the need to maintain separate definitions for each surface.
• Pro: The Free plan includes workbooks, dashboard publishing, agents and analytics chat, giving teams a broad set of tools to begin with.
• Pro: Managed, VPC and BYOC deployments give organizations options for where Cube Cloud runs.
• Con: Free is limited to five workbooks and 1,000 daily requests, so a wider rollout may require a paid plan.
• Con: Free and Starter retain query history for only one day; longer retention requires Premium or Enterprise.
• Con: Premium's Explorer and Viewer roles cost extra, complicating budgets for teams that need broad user access.

Alternatives

For a broader survey of products in the category, see Semantic Layer Software and Data Transformation Tools.

• Starburst Enterprise Context Layer is worth considering if a free plan capped at three clusters and standard ad hoc query execution better fits the intended scope; its Pro plan charges price on request.
• Tableau may suit teams choosing between Creator at $75 a month billed annually and Explorer at $42 a month billed annually, with each new deployment requiring a Creator license.
• AtScale is an alternative for teams looking for semantic models and BI-tool connectivity in its Standard plan, or broader integrations, enterprise governance and large-scale semantic capabilities in Enterprise; both plans have custom pricing.
• dbt Semantic Layer may suit teams focused on dbt's model-oriented workflow: its Developer plan is free for one developer seat and 3,000 successful models per month, while Starter is $100 a month.
• Sema offers a Sandbox plan with one connector, one user and 100 chat queries per month, alongside limits on saved reports and delivery features.
• Strata may be a fit for a small team seeking a free plan with one developer, 25 users and two data sources per project.
• Definite is another freemium web-based option.
• Verdonz is another freemium web-based option.

Verdict

Choose Cube if your team needs one governed analytics model to serve internal BI, embedded use and AI agents, and values deployment flexibility. Its broad free toolkit makes it approachable, but daily request caps and one-day history on the lower tiers are real constraints; look elsewhere if those limits, or the added cost of Premium access roles, do not fit your planned use.