Preset
Preset is the commercial managed version of Apache Superset, an open-source BI tool. Superset has SQL-based exploration, drag-and-drop dashboards, and a rich visualization library. Preset adds hosting, SSO, permissioning, and support. It is a good option for teams that want open-source BI with enterprise features, but the user base is smaller than Tableau or Looker.
Official site ↗Why it ranks here
- Open-source foundation (Apache Superset) means no vendor lock-in; you can self-host if needed.
- Rich visualization library with 40+ chart types, including geospatial and time-series.
- Managed hosting removes the burden of infrastructure and upgrades.
Where it sits
- More feature-rich than Metabase; Superset has better visualization options and SQL support.
- Behind Tableau or Looker in user experience and enterprise adoption; Superset is more technical.
- Similar to Lightdash in open-source positioning; Preset is less opinionated (no dbt requirement).
How GTM Operations teams use it
- Data teams at open-source-first companies use Preset for internal dashboards and stakeholder reporting.
- RevOps builds pipeline dashboards on warehouse data, using SQL Lab for ad-hoc queries and dashboards for reporting.
- Marketing analysts create attribution dashboards with custom visualizations not available in other BI tools.
- Engineering teams use Preset for operational dashboards (app metrics, usage, logs) alongside GTM analytics.
In-depth notes
- Pricing: Preset Cloud starts at low per-user monthly pricing for small teams; Enterprise is custom pricing with SSO and advanced features.
- Self-hosting Superset is free, but you manage infrastructure, upgrades, and security. Preset Cloud removes that burden.
- SQL Lab is a full SQL IDE within Preset: write queries, visualize results, and save them as charts or dashboards.
- Visualization library is stronger than Tableau or Looker for custom chart types (e.g., Sankey diagrams, geospatial maps).
- Smaller ecosystem than enterprise BI tools: fewer pre-built connectors, less community content, smaller talent pool.
Best for
Open-source-first teams that want managed BI without vendor lock-in, or orgs that need rich visualizations not available in other tools.
Avoid if
You want a polished user experience for non-technical users (use Tableau or Sigma), or you need a larger vendor ecosystem (use Looker or Power BI).
The rest of BI & analytics
Tableau Leader Visual analytics platform with drag-and-drop interface; Salesforce-owned market leader for interactive dashboards. #2 
Looker Leader Semantic layer (LookML) on live warehouse data; Google Cloud-owned platform for governed analytics with code-based modeling. #3 
Power BI Leader Microsoft's BI platform with deep Office and Teams integration; cost-effective for Microsoft shops with enterprise agreements. #4 
Omni Strong Modern warehouse-native BI with spreadsheet, SQL, and semantic model workflows; live queries with no extracts or cubes. #5 
Sigma Strong Warehouse-native BI with a spreadsheet interface; live queries on Snowflake, BigQuery, or Databricks with familiar Excel-like UX. #6 
Mode Strong SQL-first analytics platform with notebooks for analysis and reporting; popular with technical RevOps and data teams. #7 
ThoughtSpot Strong Search-driven analytics with natural language queries; AI-powered insights for business users who do not want to build dashboards. #8 
Hex Situational Notebooks for Python and SQL hybrid analysis; build interactive apps from notebooks with code and no-code cells. #9 
Domo Situational All-in-one platform with ETL, cloud data warehouse, and BI; higher total cost but unified vendor for orgs without a data stack. #10 
Qlik Situational Associative analytics engine for exploring complex data relationships; in-memory architecture with a steeper learning curve. #11 
Metabase Situational Open-source BI with simple setup and no-code query builder; good for small teams that need dashboards without enterprise complexity. #12 
Lightdash Niche dbt-native BI tool; metrics and dimensions defined in dbt YAML, dashboards built on transformed warehouse tables. #14 
Redash Niche Open-source SQL query and visualization tool; simple dashboards for technical teams with minimal setup. Learning guide
Setup
Sign up for Preset Cloud, connect to your warehouse, and use SQL Lab to write queries and create datasets. Build dashboards by adding charts from the visualization library.
First thing to build
Write a SQL query in SQL Lab to aggregate opportunities by stage, save it as a dataset, then create a bar chart and add it to a dashboard. Share the dashboard with stakeholders.
What actually matters
- Use SQL Lab for ad-hoc queries and exploration before building charts
- Define virtual datasets with custom SQL to pre-aggregate complex queries
- Set row-level security via SQL filters based on user attributes
- Enable caching to reduce warehouse query load
Watch out
Superset has a steeper learning curve than Tableau or Power BI, so budget time for training and onboarding.