Buying snapshot
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| Decision point | What Evidence states |
|---|---|
Open-source core | Evidence Core is MIT-licensed. It builds to a static site you can self-host on Vercel, Netlify, Docker or your own infrastructure, with no account required. |
Data sources | Core queries your own warehouse through direct connectors. The hosted plans list native warehouse support and an Evidence Warehouse. |
How reports are built | Markdown files with embedded SQL and components, previewed locally with the CLI ( |
Our take
Choose Evidence when your data team wants reports and dashboards to live in a Git repository as SQL and Markdown, reviewed like code. Start with Evidence Core if you can host a static site yourself; choose a hosted plan when the team needs the shared editor, the analytics agent and access control.
Evaluate Evidence
Comparisons
Where it stands out
Reports reviewed like software. Every page is a file in a repository, and page-level access is managed in a single version-controlled file. For publishing, Evidence lists push to deploy, instant rollback, branch previews and automated checks on every pull request.
An analytics agent your team can extend. On both hosted plans, the agent answers questions and builds reports, uses existing dashboards and reports as context, and takes custom context and skills kept in your repo. It works in Slack or any MCP client, including Claude Desktop and ChatGPT.
Room beyond standard charts. Data tables support sparklines, subtotals and images, maps load custom GeoJSON and can act as inputs, and custom components let you build any visual and reuse it across the team.
Product screenshot
Trade-offs to weigh
Authoring assumes SQL and Markdown. Evidence describes itself as a code-based alternative to drag-and-drop BI tools, so people who do not write SQL depend on the data team or the analytics agent to build pages.
A flat price favors wide rollouts. Team is one monthly price for unlimited users, so its cost per person depends entirely on how many people use it.
Single sign-on means an Enterprise contract. Team includes Log in with Google but not SSO or SCIM; companies that require either need the custom-priced annual Enterprise plan.
Pricing and plan conditions
Evidence Core is MIT-licensed and needs no account; the prices below are for the hosted plans. Both plans cover unlimited users and projects, and you can start with a 30-day trial. AI features use credits, with terms and usage-based billing set out on Evidence's AI Credit Pricing page.
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| Plan | Published price | What the plan includes |
|---|---|---|
Team | $2,500 per month, flat monthly billing | Unlimited users and projects; analytics agent, Slack bot and MCP; page-level access control, user groups and table-level access controls; scheduled emails, data and PDF export; usage analytics; Log in with Google; private Slack or Teams support; 20,000 AI credits a month, then $0.01 per credit. |
Enterprise | Custom, annual contract | Everything in Team, plus SSO (OIDC and SAML), SCIM, row-level access rules, external customers and viewers, embedding and white-labeling, your own encryption keys, cloud and model API key, SIEM audit logging, SOC 2 report and DPA, multi-region support, GitHub Enterprise Cloud, custom AI credits, and dedicated training. |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
141 GitHub commits 90d7.1k GitHub stars0 vulnerabilities across 1 package
See all signals from 5 sources
npm · @evidence-dev/evidence@40.1.8
Alternatives to Evidence
The reviewed substitutes for Evidence among the BI platforms, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Metabase
- Metabase and Evidence are open-source ways to share dashboards and reports built on company data. Metabase gives people a point-and-click question builder and a SQL editor in a web app. Evidence is code-first, with reports written in SQL and markdown and kept under version control.Applies to: Choosing between these two for the open source bi decision.
- Lightdash
- Lightdash and Evidence are open-source BI tools for data teams that already model data in SQL. Lightdash reads metrics straight from a dbt project and gives business users self-service exploration. Evidence has authors write reports in SQL and markdown, so the output is a curated set of pages rather than open exploration.Applies to: Choosing between these two for the open source bi decision.
- Redash
- Evidence and Redash are open-source tools that turn SQL queries into charts and reports a company can share. Redash is a query-and-dashboard web app that connects to many data sources and is driven from its query editor. Evidence keeps reports as SQL and markdown in code and builds them into polished pages, which suits teams that work in version control.Applies to: Choosing between these two for the open source bi decision.
- Apache Superset
- Apache Superset and Evidence are open-source projects for publishing charts and dashboards from SQL data. Superset is a web platform with a no-code chart builder and SQL Lab, hosted and run by the team. Evidence turns SQL and markdown files into report pages, so reports live in code like the rest of a data project.Applies to: Choosing between these two for the open source bi decision.
- Looker
- Looker and Evidence both publish dashboards and reports built on a company's warehouse data. Looker is a commercial Google Cloud platform with a LookML model and self-service explores for business users. Evidence is open source and code-based: authors write reports in SQL and markdown instead of using a drag-and-drop interface.Applies to: Choosing the tool that publishes dashboards and reports on warehouse data.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Spotfire
- Evidence replaces Spotfire for code-authored SQL and markdown reporting workloads. **Evidence** is an open-source, code-based BI platform for building polished data products with SQL and markdown instead of drag-and-drop dashboards.
See detailed alternatives analysis
If you are evaluating Evidence alternatives, you are likely looking for a business intelligence tool that fits your team's workflow, technical comfort level, and budget. Evidence takes a distinctive code-based approach to BI -- using SQL and markdown to build reports and dashboards -- but that paradigm is not the right fit for every organization. Below, we break down the leading alternatives across architecture, pricing, and use-case fit to help you make an informed decision.
Top Alternatives Overview
Evidence competes in the Business Intelligence (BI) category alongside both open-source and commercial platforms. Here are the most notable alternatives worth evaluating:
Metabase is an open-source BI tool focused on making data exploration accessible to non-technical users. It offers a visual query builder that lets team members ask questions about data without writing SQL, while still providing a full SQL editor for power users. Metabase has a sizable open-source community with over 48,000 GitHub stars and connects to over 20 data sources. It is built in Clojure and provides features like drill-through navigation, scheduled reports, Metabot AI for natural-language queries, and embedded analytics via a React SDK. Metabase holds an 8.4/10 user rating across 66 reviews.
Lightdash is an open-source, AI-native BI platform built specifically for dbt users. It provides a semantic layer that connects directly to dbt projects, enabling teams to define metrics once and reuse them consistently. Lightdash emphasizes a "BI-as-code" workflow with version control, preview environments, and CI/CD integration. It has accumulated over 5,700 GitHub stars and is built in TypeScript. Lightdash supports agentic BI workflows where AI agents can build dashboards, answer questions via Slack, and generate reports through a governed semantic layer.
KNIME takes a visual, node-based workflow approach to data analytics. Rather than writing code or using drag-and-drop dashboards, users connect nodes together to create data processing pipelines. KNIME Analytics Platform is free and open source and supports over 300 data connectors. It spans data blending, machine learning, and deployment -- making it a broader data science platform rather than a pure BI tool. KNIME is built in Java with over 770 GitHub stars.
Redash is a lightweight, open-source data visualization tool that focuses on connecting to data sources, writing SQL queries, and building shareable dashboards. Originally an independent project, Redash was acquired by Databricks in 2020. It is self-hosted under a BSD-2-Clause license, has over 28,500 GitHub stars, and supports a wide range of databases including PostgreSQL, MySQL, BigQuery, and MongoDB. Redash holds an 8.1/10 user rating across 17 reviews.
Sigma Computing is a cloud-native BI platform that combines the familiar interface of a spreadsheet with the scale of a data warehouse. It enables business users to explore data without SQL knowledge, while maintaining governance and security controls. Sigma holds an 8.2/10 user rating across 297 reviews and offers a free tier for up to 5 users.
Power BI is Microsoft's BI and data visualization platform, tightly integrated with Microsoft 365 and Azure. It offers a free tier for individual users and is a natural fit for organizations already invested in the Microsoft ecosystem. Power BI Pro is available at $14/user/month, paid yearly.
Architecture and Approach Comparison
The fundamental architectural distinction among these tools lies in how users interact with data and build reports.
Code-based vs. visual builders: Evidence and Lightdash both embrace a code-first philosophy. Evidence uses SQL and markdown files, versioned in Git, to produce publication-quality reports and dashboards. It is built on Svelte and JavaScript, released under the MIT license, and leverages DuckDB for client-side query execution with WebAssembly. Lightdash similarly operates as code but is deeply tied to dbt, using dbt models and metrics as its semantic foundation. Both tools appeal to data teams comfortable with developer workflows -- version control, pull requests, and CI/CD pipelines.
In contrast, Metabase, Sigma Computing, and Power BI prioritize visual, no-code or low-code interfaces. Metabase's visual query builder lets non-technical users explore data through point-and-click interactions, with an escape hatch into SQL when needed. Sigma Computing reimagines the spreadsheet as an analytics interface, letting business users perform complex analyses using familiar spreadsheet paradigms but against live warehouse data. Power BI provides a desktop application and web service with drag-and-drop report building.
KNIME occupies a unique position with its node-based visual workflow editor. Users build data pipelines by connecting nodes that represent discrete operations -- data reading, transformation, modeling, and visualization. This makes it particularly strong for data science workflows that extend beyond traditional BI dashboards into machine learning and advanced analytics.
Query engine and performance: Evidence Query Engine, providing columnar storage and vectorized execution for sub-second queries across large datasets. It also features multi-level caching with automatic query optimization and memory management. Metabase, Redash, and Lightdash function as visualization layers that sit on top of your existing database -- they send queries to your data warehouse and render the results. Metabase does offer result and model caching to keep dashboards responsive. Sigma Computing queries your cloud data warehouse directly without data extraction.
Deployment model: Evidence, Metabase, Lightdash, Redash, and KNIME all offer self-hosted open-source editions, giving teams full control over their infrastructure. Evidence, Metabase, Lightdash, and Sigma Computing also provide managed cloud offerings. Power BI is primarily a SaaS product with deep Azure integration. Amazon QuickSight is fully managed within the AWS ecosystem.
Security and governance: Evidence provides row-level security through built-in RLS policies and is SOC 2 Type II certified. It also supports SSO, directory sync via SCIM, SIEM logging, and customer-managed encryption keys. Metabase offers row-level, column-level, and collection-level permissions, along with SSO integration (SAML, LDAP, JWT, Google). Lightdash provides SOC 2 compliance with HIPAA and BAA support, plus custom roles and SCIM 2.0 for enterprise deployments.
Pricing Comparison
Pricing structures vary significantly across these tools, reflecting different go-to-market strategies.
Evidence charges one flat price rather than per seat: Team is $2,500 per month for unlimited users, billed monthly, and Enterprise is quote-only. A 30-day trial is offered, and Evidence also has a free open-source edition that can be self-hosted.
Metabase offers a fully free open-source self-hosted edition. Its cloud plans include a Starter tier at $100/month, a Pro tier at $575/month, and an Enterprise tier priced custom from $20,000 per year (contact sales for details). The Starter and Pro tiers do not charge per seat.
Lightdash provides a free self-hosted open-source edition. Its Cloud Pro plan is $3,000/month with no per-seat pricing and unlimited users. Enterprise pricing is available by contacting sales. Embedding is available as an add-on, with pay-as-you-go pricing at $0.05 per load after the first 1,000 free loads, or a predictable plan at $790/month for sizable volumes.
KNIME Analytics Platform is free and open source. Paid KNIME Hub plans cost €19 a month (Pro) and from €99 a month (Team, 3 seats); Business Hub is priced on request.
Redash is entirely free and open source under a BSD-2-Clause license for self-hosted deployments. There is no commercial cloud offering since the Databricks acquisition.
Sigma Computing offers a free tier for up to 5 users, with Pro plans starting at $25/user/month. Enterprise pricing requires contacting sales.
Power BI starts with a free tier for one user, with Pro at $14/user/month and Premium Per User at $24/user/month.
Amazon QuickSight publishes no free tier. It prices users by role, from $3 per month for a Reader to $24 for an Author ($20 and $40 for the Pro roles), and also sells Reader sessions in bulk, from $250 per month for 500 sessions.
When to Consider Switching
Choosing to move away from Evidence -- or choosing it over alternatives -- depends on your team's composition, technical maturity, and specific requirements.
Consider switching from Evidence if your team includes many non-technical stakeholders who need to explore data independently. Evidence's code-based workflow requires comfort with SQL, markdown, and Git. If your business users need self-service analytics with a visual query builder, Metabase or Sigma Computing may be more appropriate. Metabase's point-and-click interface and natural-language Metabot AI feature specifically target this use case.
Consider switching if you need a mature embedded analytics solution for your SaaS product. While Evidence supports embedding, Metabase offers a dedicated React SDK and extensive white-labeling capabilities designed for in-product analytics at scale, with options from iframe embedding to full SDK customization.
Consider switching if your organization is heavily invested in dbt and wants your BI layer tightly coupled with your data transformation layer. Lightdash is built specifically for dbt users, with native integration that keeps your models and metrics in sync through a governed semantic layer. Evidence connects to dbt-powered warehouses but does not share the same deep dbt-native semantic layer design.
Consider switching if your needs extend beyond BI into data science workflows -- model training, advanced machine learning, or complex data transformation pipelines. KNIME's visual workflow platform with its node-based approach and Alteryx's data automation capabilities serve these broader analytical needs better than a BI-focused tool.
Stick with Evidence if your team values a developer-centric workflow, wants reports and dashboards version-controlled in Git, and appreciates the simplicity of SQL plus markdown. Evidence's approach eliminates the complexity of drag-and-drop builders in favor of clean, reproducible, code-driven analytics. Its built-in query engine and managed infrastructure also appeal to teams that want high performance without managing separate data infrastructure. The AI-enhanced development experience, with Build your analytics agent with custom context and skills., further accelerates the code-first workflow.
Migration Considerations
Moving between BI tools involves more than just switching software. Here are the key factors to evaluate before migrating.
Content portability: stored in Git, which makes them relatively portable -- the SQL queries can be reused in any tool that supports SQL. However, Evidence-specific markdown components, Svelte customizations, and visualization syntax will need to be recreated in the target tool's format. Moving from a visual tool to Evidence similarly requires translating point-and-click configurations into SQL and markdown.
Data source connections: Most modern BI tools support the same major data warehouses (Snowflake, BigQuery, PostgreSQL, ClickHouse, etc.), so database connectivity is rarely a blocker. Verify that your target tool supports all your specific data sources and any advanced features you rely on, such as row-level security, scheduled data syncs, or live query capabilities.
User training and adoption: The biggest migration cost is often human. Moving from a code-based tool like Evidence to a visual tool (or vice versa) requires your team to learn entirely new workflows. Budget time for training and expect a temporary productivity dip during the transition. If migrating to Lightdash from Evidence, the learning curve is gentler since both tools share a code-oriented philosophy and Git-based workflows.
Permissions and governance: Evaluate how each tool handles data access control. Evidence provides row-level security through built-in RLS policies and supports SOC 2 Type II compliance. Metabase offers row-level, column-level, and collection-level permissions with SSO integration. Sigma Computing and Power BI provide enterprise-grade governance features. Lightdash supports SOC 2 compliance with HIPAA support and custom roles. Ensure your target tool can replicate your existing access control requirements.
Cost of transition: Factor in not just the subscription cost difference but also engineering time for migration, potential downtime in reporting, and the cost of maintaining parallel systems during the transition period. Organizations using Evidence's managed cloud offering should also consider data residency requirements (Evidence supports multi-region deployment) and compliance needs when switching providers. Running both systems in parallel during the transition period helps prevent reporting gaps for stakeholders.