The open source business intelligence and KPI tracking landscape in 2025 is mature, diverse, and actively maintained. Based on comprehensive research including GitHub statistics, community feedback, and feature analysis, here are the top recommendations:
Top Picks by Use Case:
- Best Overall for Traditional BI: Apache Superset (68.4k stars)
- Easiest for Non-Technical Users: Metabase (44.1k stars)
- Best for Monitoring & Real-Time Metrics: Grafana (70.3k stars)
- Best for SQL-Proficient Teams: Redash (27.9k stars)
- Best for dbt Users: Lightdash (5.3k stars)
- Best for Code-First Analytics: Evidence.dev (5.6k stars)
Quick Comparison Table
Tool | GitHub Stars | Best For | Complexity | License | Key Strength |
|---|---|---|---|---|---|
Grafana | 70.3k | Monitoring, time-series | Medium | AGPL-3.0 | Real-time metrics & alerting |
Apache Superset | 68.4k | Complex analytics | High | Apache 2.0 | Visualization variety |
Metabase | 44.1k | Business users | Low | Multiple | Ease of use |
Redash | 27.9k | SQL users | Medium | BSD-2 | Query flexibility |
Evidence.dev | 5.6k | Developers | Medium | MIT | Version control |
Lightdash | 5.3k | dbt teams | Medium | MIT | dbt integration |
Detailed Tool Reviews
1. Grafana (70.3k)
GitHub: https://github.com/grafana/grafana
Website: https://grafana.com
License: AGPL-3.0
Overview
Grafana is the most popular open source observability and data visualization platform. Originally focused on monitoring and metrics, it has evolved into a comprehensive analytics solution that excels at visualizing time-series data from multiple sources.
Key Features
- Multi-source support: Connects to Prometheus, InfluxDB, PostgreSQL, Elasticsearch, MySQL, and 100+ other data sources
- Real-time monitoring: Excellent for live dashboards and alerting
- Rich visualization library: Over 50 panel types including graphs, heatmaps, histograms
- Alerting system: Built-in alerting with multiple notification channels
- Plugin ecosystem: Extensive marketplace for extensions
Best Use Cases
- DevOps and infrastructure monitoring
- Application performance monitoring (APM)
- IoT and sensor data visualization
- Real-time business metrics
- Server and network monitoring
Strengths
- Largest community and ecosystem
- Exceptional for time-series data
- Powerful alerting capabilities
- Cloud and self-hosted options
- Very active development (commits within hours)
Limitations
- Less intuitive for traditional BI use cases
- Steeper learning curve for complex dashboards
- Not ideal for ad-hoc data exploration
- AGPL license may be restrictive for some commercial use
Installation Complexity
Medium – Docker installation is straightforward, but production deployment requires configuration management
2. Apache Superset (68.4k)
GitHub: https://github.com/apache/superset
Website: https://superset.apache.org
License: Apache 2.0
Overview
Apache Superset is a modern, enterprise-ready business intelligence platform that originated at Airbnb. It’s considered the most feature-rich open source BI tool, offering capabilities comparable to commercial solutions like Tableau and Looker.
Key Features
- 40+ visualization types: Including pivot tables, geospatial charts, and advanced analytics
- SQL IDE: Robust SQL editor with syntax highlighting and query history
- Semantic layer: Build reusable metrics and dimensions
- Row-level security: Granular access control for enterprise deployments
- Caching layer: Redis-based caching for performance optimization
- Custom visualizations: Plugin architecture for custom viz types
Best Use Cases
- Enterprise business intelligence
- Complex data exploration and analytics
- Multi-tenant analytics applications
- Organizations needing extensive visualization options
- Teams with both technical and non-technical users
Strengths
- Most comprehensive visualization library
- Enterprise-grade security features
- Strong Apache Foundation backing
- Excellent for complex analytics
- Active community (60% growth in adoption YoY)
Limitations
- Most complex setup and maintenance
- Steepest learning curve
- Resource-intensive (requires more compute)
- UI can be overwhelming for beginners
Installation Complexity
High – Requires Docker Compose or Kubernetes for production. Configuration and tuning needed for optimal performance.
3. Metabase (44.1k)
GitHub: https://github.com/metabase/metabase
Website: https://www.metabase.com
License: Multiple (AGPL for open source)
Overview
Metabase is the easiest-to-use open source BI tool, designed for everyone in an organization to work with data. Its intuitive visual query builder and simple setup make it ideal for teams that want to start quickly.
Key Features
- Visual query builder: Create queries without writing SQL
- Automatic insights: AI-powered X-ray feature for automatic analysis
- Embedded analytics: Easy embedding in applications
- Slack/email integration: Schedule reports and alerts
- Collections and permissions: Organize dashboards and control access
- Mobile-friendly: Responsive dashboards work on any device
Best Use Cases
- Small to medium businesses
- Teams with limited SQL knowledge
- Embedded analytics in SaaS products
- Quick dashboard creation
- Self-service analytics
Strengths
- Lowest learning curve
- Beautiful, intuitive UI
- Fast setup (running in minutes)
- Great for embedded analytics
- Strong documentation and community support
Limitations
- Less flexible than Superset for advanced analytics
- Limited customization options
- Performance issues with very large datasets
- Some features locked behind paid version
Installation Complexity
Low – Single JAR file or Docker container. Can be running in under 5 minutes.
4. Redash (27.9k)
GitHub: https://github.com/getredash/redash
Website: https://redash.io
License: BSD-2-Clause
Overview
Redash is a SQL-first data visualization and collaboration platform. It’s designed for data analysts and engineers who are comfortable with SQL and want a straightforward way to query data and share insights.
Key Features
- Multi-source queries: Connect and query 100+ data sources
- Query editor: Full-featured SQL editor with auto-complete
- Query scheduling: Automatic refresh and alerts
- API access: Programmatic access to queries and results
- Query snippets: Reusable SQL fragments
- Collaboration: Share queries and dashboards easily
Best Use Cases
- Data analyst teams
- SQL-proficient organizations
- Ad-hoc data exploration
- Collaborative query development
- Internal analytics dashboards
Strengths
- Perfect for SQL-savvy users
- Simple, focused interface
- Extensive data source support
- Easy collaboration features
- Permissive BSD license
Limitations
- Limited no-code options
- Fewer visualization types
- Less active development than competitors
- Not ideal for non-technical users
Installation Complexity
Medium – Docker-based installation is straightforward. Requires PostgreSQL and Redis.
5. Lightdash (5.3k)
GitHub: https://github.com/lightdash/lightdash
Website: https://www.lightdash.com
License: MIT
Overview
Lightdash is a modern, self-serve BI platform built specifically for teams using dbt (data build tool). It recently secured funding from Accel (October 2024) and is rapidly growing in the modern data stack ecosystem. New AI features were announced in 2024.
Key Features
- Native dbt integration: Automatic metric generation from dbt models
- AI-powered insights: Recently announced AI analyst features
- Version control: Analytics logic in Git
- Semantic layer: Built on dbt’s semantic layer
- Collaborative: Team-based exploration and sharing
- Cloud or self-hosted: Flexible deployment options
Best Use Cases
- Teams already using dbt
- Modern data stack environments
- Analytics engineering workflows
- Organizations wanting version-controlled BI
- Data teams preferring code-based definitions
Strengths
- Perfect dbt integration
- Growing rapidly with VC backing
- Modern, developer-friendly approach
- New AI features for enhanced analysis
- Active development and innovation
Limitations
- Requires dbt (not standalone)
- Smaller community (newer tool)
- Less mature than established alternatives
- Limited visualization options compared to Superset
Installation Complexity
Medium – Requires dbt setup. Docker or cloud deployment available.
6. Evidence.dev (5.6k )
GitHub: https://github.com/evidence-dev/evidence
Website: https://evidence.dev
License: MIT
Overview
Evidence is a code-first business intelligence tool that lets you build data visualizations using SQL and Markdown. Released Evidence Studio cloud version in June 2025. It’s perfect for developers who want analytics in version control.
Key Features
- Code-first approach: Write analytics in SQL and Markdown
- Version control native: Store in Git like code
- Fast rendering: Built with Svelte for performance
- Evidence Studio: Cloud-based development environment (June 2025)
- Agentic AI: AI assistance for query writing
- Interactive components: Built-in interactive visualizations
Best Use Cases
- Developer-focused teams
- Version-controlled analytics
- Internal data products
- Technical documentation with live data
- Analytics-as-code workflows
Strengths
- True version control for analytics
- Developer-friendly workflow
- New AI features with Studio release
- Fast and lightweight
- Permissive MIT license
Limitations
- Requires coding skills
- Not suitable for business users
- Smaller visualization library
- Newer tool with evolving features
Installation Complexity
Medium – Node.js based. Simple npm install but requires familiarity with code workflows.
Specialized Mention: Cube.js
GitHub: https://github.com/cube-js/cube
Website: https://cube.dev
While not a dashboard tool itself, Cube.js deserves mention as a universal semantic layer and analytics API platform. It works as a headless BI tool that sits between your data sources and any visualization layer (including Metabase, Superset, or custom frontends).
Use it when:
- Building embedded analytics in applications
- Needing a consistent metric layer across tools
- Creating custom data applications
- Requiring high-performance querying with caching
Selection Guide
Choose Grafana if you:
- Need real-time monitoring and metrics
- Work with time-series data
- Want powerful alerting
- Run DevOps or infrastructure teams
- Need the largest community and plugin ecosystem
Choose Apache Superset if you:
- Need the most comprehensive BI features
- Require enterprise-grade security
- Want maximum visualization flexibility
- Have technical resources for setup/maintenance
- Need row-level security and multi-tenancy
Choose Metabase if you:
- Want the easiest setup and use
- Have non-technical users
- Need embedded analytics
- Want to start quickly
- Prefer beautiful, simple interfaces
Choose Redash if you:
- Your team is SQL-proficient
- You want simplicity over features
- Need good collaboration tools
- Don’t need advanced visualizations
- Prefer a permissive license
Choose Lightdash if you:
- Already use dbt
- Want analytics as code
- Need Git version control
- Value modern data stack integration
- Want AI-assisted analytics
Choose Evidence.dev if you:
- Prefer code-first workflows
- Want version-controlled analytics
- Need fast, lightweight solution
- Have developer-focused team
- Want analytics in Markdown
Technical Considerations
Database Support
All tools support common databases including PostgreSQL, MySQL, SQLite, BigQuery, Redshift, and Snowflake.
Best database support: Redash and Superset (100+ connectors each)
Deployment Options
Tool | Docker | Kubernetes | Cloud Hosted | Complexity |
|---|---|---|---|---|
Grafana | Yes | Yes | Grafana Cloud | Low-Medium |
Superset | Yes | Yes | Preset.io | High |
Metabase | Yes | Yes | Metabase Cloud | Low |
Redash | Yes | Yes | Redash Hosted | Medium |
Lightdash | Yes | Yes | Lightdash Cloud | Medium |
Thoughts
The open source KPI and business intelligence ecosystem in 2025 offers robust solutions for organizations of all sizes and technical capabilities. Whether you need enterprise-grade analytics with Apache Superset, user-friendly dashboards with Metabase, real-time monitoring with Grafana, or modern code-first approaches with Evidence.dev and Lightdash, there is a mature, well-supported option available.
When selecting a tool, consider your team’s technical expertise, specific use cases, deployment preferences, and long-term maintenance capabilities. All the tools reviewed here have active communities, regular updates, and proven track records in production environments.
The continued growth and innovation in this space, including new AI-powered features and improved developer experiences, suggests that open source BI tools will remain competitive alternatives to commercial solutions for the foreseeable future.
