Looker vs Redash
Looker and Redash serve fundamentally different segments of the BI market. Looker is the right choice for enterprises that need a governed semantic layer, embedded analytics, and tight Google Cloud integration. Redash is ideal for data teams that want a free, open-source tool for SQL-based querying and quick dashboard creation without the overhead of a managed platform.
Looker4.4Redash4.3
Business Intelligence
Quick Comparison
| Feature | Looker | Redash |
|---|---|---|
| Pricing Model | Standard $99/mo, Premium $299/mo, Enterprise custom | Self-hosted free (BSD-2-Clause license) |
| Best For | Enterprise teams needing governed semantic modeling and embedded analytics | Data teams wanting a lightweight, SQL-first query and visualization tool |
| Data Modeling | LookML semantic layer with version-controlled, reusable data models | No built-in semantic layer; relies on direct SQL queries |
| Deployment | Cloud-hosted SaaS on Google Cloud Platform | Self-hosted (Docker) or community-managed instances |
| User Rating | 8.4/10 based on 457 reviews | 8.1/10 based on 17 reviews |
| Learning Curve | Steeper learning curve due to LookML; powerful once mastered | Low barrier to entry for SQL-proficient users |
Looker
- Pricing Model:
- Standard $99/mo, Premium $299/mo, Enterprise custom
- Best For:
- Enterprise teams needing governed semantic modeling and embedded analytics
- Data Modeling:
- LookML semantic layer with version-controlled, reusable data models
- Deployment:
- Cloud-hosted SaaS on Google Cloud Platform
- User Rating:
- 8.4/10 based on 457 reviews
- Learning Curve:
- Steeper learning curve due to LookML; powerful once mastered
Redash
- Pricing Model:
- Self-hosted free (BSD-2-Clause license)
- Best For:
- Data teams wanting a lightweight, SQL-first query and visualization tool
- Data Modeling:
- No built-in semantic layer; relies on direct SQL queries
- Deployment:
- Self-hosted (Docker) or community-managed instances
- User Rating:
- 8.1/10 based on 17 reviews
- Learning Curve:
- Low barrier to entry for SQL-proficient users
Community & Adoption Signals
| Metric | Looker | Redash |
|---|---|---|
| GitHub stars | — | 28.6k |
| TrustRadius rating | 8.4/10 (457 reviews) | 8.1/10 (17 reviews) |
| PyPI weekly downloads | 4.5M | — |
| Docker Hub pulls | — | 89.6M |
| Search interest | 12 | 0 |
| Product Hunt votes | 73 | 9 |
As of 2026-05-04 — updated weekly.
Interface Preview
Looker
Redash
Feature Comparison
| Feature | Looker | Redash |
|---|---|---|
| Data Connectivity | ||
| SQL Database Support | Connects to major warehouses including BigQuery, Redshift, Snowflake, and others | Supports PostgreSQL, MySQL, Redshift, BigQuery, Snowflake, and 40+ SQL sources |
| NoSQL & API Sources | Primarily focused on SQL-based warehouses; limited native NoSQL support | Supports MongoDB, DynamoDB, Elasticsearch, and REST API data sources |
| Live Query Execution | Queries warehouses directly with no intermediate data storage for always-fresh results | Runs queries directly against connected databases with cached results for performance |
| Data Modeling & Governance | ||
| Semantic Modeling Layer | LookML defines reusable metrics, joins, permissions, and derived tables centrally | No semantic layer; users write and manage SQL queries individually |
| Version Control | Built-in Git integration for LookML model version control | No native version control; queries managed in the application database |
| Row-Level Security | Row-level and column-level security with enterprise audit features | Basic user management and access control; no row-level security |
| Visualization & Dashboards | ||
| Dashboard Builder | Enterprise dashboards with real-time data, drill-down capabilities, and governed metrics | Drag-and-drop dashboard builder with resizable visualizations and scheduled refreshes |
| Chart Types | Wide range of visualizations plus Looker Studio for ad hoc reporting and 1,000+ connectors | Line, bar, area, pie, scatter, boxplot, cohort, sunburst, word cloud, sankey, map, funnel, pivot table |
| Scheduled Refreshes | Supports scheduled data deliveries and alerts through the platform | Built-in query scheduling and automatic dashboard refresh from data sources |
| Collaboration & Sharing | ||
| Dashboard Sharing | Share within organization with role-based access; embed in external applications | Share dashboards via secret URLs with peers, clients, or the public |
| Embedded Analytics | Robust embedding and white-labeling options with API support for SaaS products | Basic iframe embedding; limited white-labeling capabilities |
| API Access | Comprehensive REST APIs, SDKs, and integrations for automation and embedding workflows | REST API for querying, creating queries, and managing data sources programmatically |
| Platform & Ecosystem | ||
| AI & Advanced Analytics | Conversational Analytics powered by Gemini; Vertex AI integration for custom AI workflows | No native AI features; focused on SQL querying and visualization |
| Marketplace & Extensions | Looker Marketplace with pre-built blocks, applications, and custom visualizations | Open-source community with plugins and custom visualizations via contributions |
| Alerts & Notifications | Supports alerts and data delivery through scheduled sends and integrations like Slack | Built-in alert system that triggers notifications when query results meet defined conditions |
Data Connectivity
SQL Database Support
LookerConnects to major warehouses including BigQuery, Redshift, Snowflake, and others
RedashSupports PostgreSQL, MySQL, Redshift, BigQuery, Snowflake, and 40+ SQL sources
NoSQL & API Sources
LookerPrimarily focused on SQL-based warehouses; limited native NoSQL support
RedashSupports MongoDB, DynamoDB, Elasticsearch, and REST API data sources
Live Query Execution
LookerQueries warehouses directly with no intermediate data storage for always-fresh results
RedashRuns queries directly against connected databases with cached results for performance
Data Modeling & Governance
Semantic Modeling Layer
LookerLookML defines reusable metrics, joins, permissions, and derived tables centrally
RedashNo semantic layer; users write and manage SQL queries individually
Version Control
LookerBuilt-in Git integration for LookML model version control
RedashNo native version control; queries managed in the application database
Row-Level Security
LookerRow-level and column-level security with enterprise audit features
RedashBasic user management and access control; no row-level security
Visualization & Dashboards
Dashboard Builder
LookerEnterprise dashboards with real-time data, drill-down capabilities, and governed metrics
RedashDrag-and-drop dashboard builder with resizable visualizations and scheduled refreshes
Chart Types
LookerWide range of visualizations plus Looker Studio for ad hoc reporting and 1,000+ connectors
RedashLine, bar, area, pie, scatter, boxplot, cohort, sunburst, word cloud, sankey, map, funnel, pivot table
Scheduled Refreshes
LookerSupports scheduled data deliveries and alerts through the platform
RedashBuilt-in query scheduling and automatic dashboard refresh from data sources
Collaboration & Sharing
Dashboard Sharing
LookerShare within organization with role-based access; embed in external applications
RedashShare dashboards via secret URLs with peers, clients, or the public
Embedded Analytics
LookerRobust embedding and white-labeling options with API support for SaaS products
RedashBasic iframe embedding; limited white-labeling capabilities
API Access
LookerComprehensive REST APIs, SDKs, and integrations for automation and embedding workflows
RedashREST API for querying, creating queries, and managing data sources programmatically
Platform & Ecosystem
AI & Advanced Analytics
LookerConversational Analytics powered by Gemini; Vertex AI integration for custom AI workflows
RedashNo native AI features; focused on SQL querying and visualization
Marketplace & Extensions
LookerLooker Marketplace with pre-built blocks, applications, and custom visualizations
RedashOpen-source community with plugins and custom visualizations via contributions
Alerts & Notifications
LookerSupports alerts and data delivery through scheduled sends and integrations like Slack
RedashBuilt-in alert system that triggers notifications when query results meet defined conditions
Our Verdict
Looker and Redash serve fundamentally different segments of the BI market. Looker is the right choice for enterprises that need a governed semantic layer, embedded analytics, and tight Google Cloud integration. Redash is ideal for data teams that want a free, open-source tool for SQL-based querying and quick dashboard creation without the overhead of a managed platform.
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Frequently Asked Questions
Is Redash still actively maintained after the Databricks acquisition?
Yes, Redash remains actively maintained as an open-source project. Databricks acquired Redash in June 2020, and the project continues to receive updates, with the latest release being v26.3.0 in March 2026. The GitHub repository has over 28,500 stars and ongoing community contributions.
Can Looker connect to data sources outside Google Cloud?
Yes, Looker connects to a wide range of SQL-based data warehouses beyond Google BigQuery, including Amazon Redshift, Snowflake, PostgreSQL, MySQL, and others. Looker queries these warehouses directly without storing data locally, ensuring results are always fresh regardless of which cloud provider hosts the data.
What is the main technical difference between Looker and Redash?
The core technical difference is Looker's LookML semantic modeling layer versus Redash's direct SQL approach. Looker requires teams to define reusable data models, metrics, and relationships in LookML, creating a governed layer that ensures consistent definitions across the organization. Redash lets analysts write SQL queries directly against connected databases with no intermediate modeling layer.
How do Looker and Redash compare on cost?
Redash is open source under the BSD-2-Clause license and free to self-host, making it the clear winner on direct software cost. Looker uses an annual commitment pricing model that requires contacting sales for a quote, with pricing signals indicating per-seat and usage-based components. The total cost of ownership for Redash includes infrastructure and maintenance for self-hosting, while Looker is a fully managed SaaS platform.