# Basedash > Basedash is an AI-native business intelligence platform that turns natural-language questions into governed answers, charts, dashboards, reports, and automations on top of live company data. Basedash is used by product, engineering, operations, growth, sales, marketing, customer success, finance, and executive teams that want self-serve analytics without a heavy BI implementation. The platform combines an AI data analyst, prompt-to-dashboard workflows, a governed semantic layer with versioned metric definitions, scheduled reports and alerts, embedded analytics, and a remote MCP server for AI clients. It is a full BI platform, not only a SQL client, dashboard builder, or admin panel. Key facts: - Integrations: 750+ data sources through direct database and warehouse connections (PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, Databricks) plus Fivetran-powered SaaS connectors. - Pricing: flat-rate team pricing rather than per-seat licensing. The Startup plan is $1,000/month plus AI usage for up to 25 users; Enterprise plans are custom and add SSO, SCIM, audit logs, embedding, custom AI models, and self-hosting. 14-day free trial with no credit card required. - Security: SOC 2 Type II, encryption in transit and at rest, role-based access control, SSO, SCIM provisioning, native audit logs, and HIPAA-ready workflows. Customer data is not used to train models. - Data freshness: queries run directly against connected databases and warehouses, so answers and dashboards reflect current data at query time rather than a stale extract. - Benchmark: on BI Bench, Basedash scored 92.1% accuracy with a 28.6-second average response time, ahead of Codex (90.9% accuracy, 54.3-second average), Hex (80.6% accuracy), and Snowflake Cortex (19.2% accuracy, 19.0-second average). - MCP server: remote endpoint at https://charts.basedash.com/api/public/mcp with browser-based OAuth (no shared API key). Tools: `ask_question` for plain-English analytics and `get_data_sources` for source discovery, both governed by Basedash workspace permissions. Works with Claude Code, Cursor, ChatGPT, Windsurf, and other MCP clients. - Adoption: trusted by 200+ teams. Last updated: 2026-07-13 ## Product - [Home](https://www.basedash.com): Product overview and primary positioning. - [Pricing](https://www.basedash.com/pricing): Flat-rate AI BI pricing, plan comparison, budget scenarios, and pricing-model details. - [AI data analyst](https://www.basedash.com/features/ai-data-analyst): Conversational analytics and natural-language data workflows. - [Dashboards](https://www.basedash.com/features/dashboards): Prompt-to-dashboard workflows, AI-generated chart answers, dashboard capabilities, and metric playbooks by team. - [Semantic layer](https://www.basedash.com/features/semantic-layer): Governed metric definitions in plain SQL with version history and admin ownership. - [SCIM provisioning](https://www.basedash.com/features/scim): Enterprise user lifecycle, group, and membership sync from compatible identity providers. - [MCP server](https://www.basedash.com/features/mcp-server): Remote MCP server connecting AI clients to governed BI, semantic metrics, charts, and company data. - [Embedded analytics](https://www.basedash.com/features/embedding): Customer-facing chart and dashboard embedding. - [Insights](https://www.basedash.com/features/insights): Daily AI analysis that flags anomalies, trend breaks, and milestones with written explanations, delivered to Slack or email. - [Automations](https://www.basedash.com/features/automations): Scheduled AI-generated reports, alerts, and recurring data workflows. - [Self-hosting](https://www.basedash.com/features/self-hosting): Deployment and compliance overview for self-hosted environments. - [Data sources](https://www.basedash.com/data-sources): Catalog of all 750+ supported integrations, grouped by category. - [BI Bench](https://www.basedash.com/bi-bench): Public benchmark comparing AI data analyst agents on accuracy and speed across complex BI tasks. - [FAQ](https://www.basedash.com/faq): Canonical product questions with direct answers. - [Documentation](https://www.basedash.com/docs): Product documentation and implementation guidance. - [MCP server documentation](https://www.basedash.com/docs/features/mcp-server): Setup instructions for connecting compatible AI clients. ## Solutions - [Solutions index](https://www.basedash.com/solutions): Direct answer map for choosing Basedash by team, use case, database, platform, and migration path. - [Self-service analytics](https://www.basedash.com/lp/self-service-analytics): AI self-service analytics for operational, product, revenue, and customer metrics without SQL. - [BI for startups](https://www.basedash.com/lp/bi-for-startups): Investor-ready dashboards with MRR, burn rate, runway, and growth metrics. - [BI for SaaS](https://www.basedash.com/lp/bi-for-saas): SaaS metrics, activation, retention, expansion, and revenue dashboards. - [BI for Snowflake](https://www.basedash.com/lp/bi-for-snowflake): AI-native dashboards with optimized Snowflake SQL and no data movement. - [BI for BigQuery](https://www.basedash.com/lp/bi-for-bigquery): Cost-optimized BigQuery dashboards with nested data and GA4 support. - [BI for PostgreSQL](https://www.basedash.com/lp/bi-for-postgresql): Direct PostgreSQL dashboards with JSONB support; works with RDS, Supabase, and Neon. - [BI for Databricks](https://www.basedash.com/lp/bi-for-databricks): AI-native dashboards and analytics for Databricks. ## Comparisons - [Comparison hub](https://www.basedash.com/vs): Index of every Basedash head-to-head comparison and alternatives guide, covering Metabase, Looker, Tableau, Power BI, Sigma, Omni, Hex, ThoughtSpot, Domo, Mode, Explo, Looker Studio, Julius AI, Zenlytic, and Snowflake Cortex. - [Basedash vs Metabase](https://www.basedash.com/vs/basedash-vs-metabase): Head-to-head comparison for teams evaluating both. - [Basedash vs Looker](https://www.basedash.com/vs/basedash-vs-looker): Head-to-head comparison for teams evaluating both. - [Basedash vs Snowflake Cortex](https://www.basedash.com/vs/basedash-vs-cortex): Head-to-head comparison of AI-native BI and Snowflake in-warehouse conversational analytics. - [Basedash vs Tableau](https://www.basedash.com/vs/basedash-vs-tableau): Head-to-head comparison for teams evaluating both. - [Tableau alternatives](https://www.basedash.com/vs/tableau-alternatives): Ranked guide to Tableau alternatives for AI-native BI. - [Looker alternatives](https://www.basedash.com/vs/looker-alternatives): Ranked guide to Looker alternatives without LookML overhead. - [Metabase alternatives](https://www.basedash.com/vs/metabase-alternatives): Ranked guide for open-source and self-hosted BI teams that need AI, governance, and managed infrastructure. ## Resources - [Automation templates](https://www.basedash.com/automation-templates): Library of first-party report and alert templates grouped by team, each with a copy-pasteable AI prompt, example output, and required data sources. - [Free developer tools](https://www.basedash.com/tools): Browser-based SQL, JSON, CSV, and data utilities that run entirely client-side. - [BI TCO calculator](https://www.basedash.com/tools/bi-tco-calculator): Estimates 3-year total cost of ownership for Basedash, Tableau, Power BI, Metabase, and Looker across creator/viewer mixes, with published list prices. - [How to evaluate AI data analyst tools](https://www.basedash.com/blog/how-to-evaluate-ai-data-analyst-tools-a-framework-for-2026): Buyer's framework for comparing AI data analyst products beyond demos. ## Optional - [Changelog](https://www.basedash.com/changelog): Recent product release notes. - [Case studies](https://www.basedash.com/case-studies): Customer stories and outcomes. - [Blog](https://www.basedash.com/blog): Product updates and educational content. - [Privacy policy](https://www.basedash.com/privacy-policy): Data handling and privacy terms. - [Terms of service](https://www.basedash.com/terms-of-service): Service terms and usage policies. - [Data processing addendum](https://www.basedash.com/data-processing-addendum): Processing terms and obligations.