Cube, Evidence, and Grafana are all open-source, API-driven, self-hostable data platforms, but they solve different problems. Cube is a headless semantic…
Best for: Data teams that want a single governed semantic layer exposing consistent metrics via SQL, REST, and GraphQL to multiple BI tools and AI agents.
Open Source / Freemium · From Free (self-hosted); Evidence Cloud from $15/user/month
Best for: Data teams comfortable with SQL and Markdown who want reports and dashboards version-controlled in Git rather than built in a drag-and-drop tool.
Freemium (Open Source plus Usage-based Cloud) · From Free (self-hosted OSS or Grafana Cloud free tier)
Best for: Engineering and DevOps teams that need to unify metrics, logs, and traces from many operational data sources into real-time dashboards and alerting.
At a Glance
Cube
Evidence
Grafana
Primary category
Business Intelligence
Business Intelligence
Business Intelligence
Rating
Not documented
Not documented
Not documented
Pricing model
Freemium
Open Source / Freemium
Freemium (Open Source plus Usage-based Cloud)
Starting price
Free
Free (self-hosted); Evidence Cloud from $15/user/month
Free (self-hosted OSS or Grafana Cloud free tier)
Free plan
Yes
Yes
Yes
Free trial
Not documented
Not documented
Not documented
Platforms
Not documented
Web
Web
Team collaboration
Not documented
Not documented
Not documented
AI features
Yes
Yes
Yes
Public API
Yes
Yes
Yes
Standout Differences
Headless Semantic Layer for BI and AI Agents
Cube defines metrics, dimensions, and access rules once and exposes them via REST, GraphQL, and a Postgres-compatible SQL API, letting AI copilots query governed numbers instead of raw tables.
Cube
Reports as Code, Versioned in Git
Evidence reports are written as Markdown files with embedded SQL, so they can be committed, diffed, and reviewed through pull requests like any other codebase.
Evidence
150-Plus Connectors for Observability
Grafana natively connects to Prometheus, Loki, Tempo, Elasticsearch, CloudWatch, and more than 150 other data sources, unifying metrics, logs, and traces in one dashboard.
Grafana
All Three Have a Free Open-Source Core
Cube, Evidence, and Grafana are each free to self-host, with usage-based or per-user pricing reserved for their managed cloud tiers.
Cube, Evidence, Grafana
Feature-by-Feature
Deployment and Pricing
Feature
Cube
Evidence
Grafana
Free open-source self-hosted core
Available
Available
Available
Managed cloud free tier
Available
Unavailable
Available
Data Modeling and Access
Feature
Cube
Evidence
Grafana
Multi-protocol API access (REST, GraphQL, or SQL)
Available
Not documented
Available
Row- or page-level access control
Available
Available
Limited
Connects to existing BI tools as a data source
Available
Not documented
Not documented
AI and Automation
Feature
Cube
Evidence
Grafana
AI-assisted features
Available
Available
Available
Pricing Compared
Starting price reflects the lowest paid tier, not the full cost for every team size or usage level.
Enterprise — Custom (from around $25,000/year) Annual
Pros & Cons
Cube
Pros
Open-source core with a large, active GitHub community
Headless architecture avoids vendor lock-in to a single BI front end
Single governed metric definitions reduce inconsistent numbers across tools
Increasingly positioned for AI/agentic use cases, not just traditional dashboards
Cons
Usage-based Cube Compute Unit billing can make costs less predictable than flat per-seat pricing
Requires engineering effort to define and maintain the semantic data model
Premium tier requires a $10,000 annual consumption commitment
Enterprise features like BYOC and custom LLM support require a custom quote
Evidence
Pros
Reports are version-controlled as code, enabling Git-based review, diffing, and deployment workflows familiar to engineering teams
Free and open source to self-host with no artificial feature limits on the core framework
Templated pages eliminate manual duplication when building many similar reports across regions, products, or customers
Designed for embedding analytics into customer-facing products, not just internal dashboards
Cons
Requires comfort writing SQL and Markdown; not accessible to non-technical business users who want pure drag-and-drop reporting
Smaller ecosystem and community than established BI incumbents like Tableau, Looker, or Power BI
Evidence Cloud pricing is per-user, which can add up for larger analytics teams
AI features are metered via credits, adding a variable cost dimension beyond the base subscription
Grafana
Pros
Free and open source core with no vendor lock-in to a single data store
Connects to virtually any metrics, logs or trace data source in one interface
Highly customizable dashboards backed by a large community plugin ecosystem
Usage-based Cloud pricing can be more cost-effective at scale than closed observability suites
Cons
Usage-based Cloud pricing can become unpredictable for high-cardinality metrics
Self-hosting the full LGTM stack requires meaningful operational expertise
Some advanced features like SSO and premium connectors require Enterprise licensing
PromQL, LogQL and TraceQL have a learning curve for newcomers
Use Cases
Choose Cube: Data teams that want a single governed semantic layer exposing consistent metrics via SQL, REST, and GraphQL to multiple BI tools and AI agents.
Choose Evidence: Data teams comfortable with SQL and Markdown who want reports and dashboards version-controlled in Git rather than built in a drag-and-drop tool.
Choose Grafana: Engineering and DevOps teams that need to unify metrics, logs, and traces from many operational data sources into real-time dashboards and alerting.
Cube
Embedded analytics in SaaS products — Software companies use Cube to power in-app dashboards and reporting for their own customers without duplicating metric logic.
Consistent enterprise metrics — Data platform teams centralize metric definitions in Cube so Tableau, Power BI, and internal tools all report the same numbers.
AI agent data access — Organizations connect AI copilots and chat assistants to Cube's semantic layer so they answer questions using governed, trustworthy metrics.
Evidence
Internal analytics for lean data teams — A small analytics engineering team builds and maintains a full internal reporting suite using the same Git workflow they already use for data pipeline code.
Customer-facing embedded analytics — A SaaS company embeds Evidence-built reports directly into its product to give customers self-service analytics without building a custom dashboard from scratch.
Automated multi-entity reporting — An organization uses templated pages to automatically generate a separate performance report for every region, store, or account from a single report definition.
Grafana
Infrastructure and Kubernetes Monitoring — Visualize cluster health, resource usage and application metrics collected by Prometheus and other exporters.
Business and Executive Reporting Dashboards — Combine data from SQL databases, APIs and cloud services into shareable business dashboards.
IoT and Sensor Data Visualization — Monitor time-series data streaming from industrial sensors, devices and edge deployments.
Frequently Asked Questions
Are Cube, Evidence, and Grafana interchangeable tools?
Not really. Cube is a headless semantic layer for governed business metrics, Evidence is a code-first framework for Git-versioned reports, and Grafana is primarily built for observability dashboards across metrics, logs, and traces. Teams sometimes use more than one together.
Can Grafana connect to a data warehouse the way Cube or Evidence can?
Yes, Grafana has more than 150 data source connectors and can query many databases directly, but it is designed and optimized for observability and operational monitoring rather than business-metric governance.
Do all three have a free, self-hosted option?
Yes. Cube Core, Evidence's open-source framework, and Grafana's open-source edition are all free to self-host.
Which of these is best for letting AI agents query governed business metrics?
Cube is explicitly positioned for this use case, with a governed semantic layer that AI copilots and LLM-based agents can query directly to reduce the risk of hallucinated numbers.
Can Evidence reports be embedded in a customer-facing product?
Yes. Evidence supports embeddable reports, so they can be used inside customer-facing products rather than only for internal dashboards.