# Supaboard vs Lightdash > Seventeen capabilities compared, from price and connectors to AI analysts, alerts, MCP support and answer accuracy, each figure sourced to Lightdash's own pages. HTML version: https://supaboard.ai/compare/lightdash ## At a glance | Row | Supaboard | Lightdash | | --- | --- | --- | | Price | **$99/seat/mo** — Or $83 billed annually. Usage credits are in the seat | **Free (OSS) / $3,000/mo (Cloud Pro)** — self-hosted core costs nothing forever; Cloud Pro is one flat unlimited-seat fee, not per user (https://www.lightdash.com/pricing, checked 2026-09-10) | | AI native | **Yes** — The agent is the product, not a panel added to a dashboard tool | **Partial** — agents answer questions by querying metrics and dimensions already defined in a dbt semantic layer (https://docs.lightdash.com/agents, checked 2026-09-10) | | AI data analysts | **Yes** — Agents tuned on your business rules and metric definitions, with the SQL shown beside every answer | **Partial** — an agent picks the relevant dbt metrics and dimensions and runs the query, not open-ended business analysis (https://docs.lightdash.com/agents, checked 2026-09-10) | | Deep reasoning | **Yes** — Deep Dive answers why a number moved and what to do next, not only what it is | **Yes** — Deep research runs a multi-step investigation with a coordinator and up to two data workers into an evidence-backed report (https://docs.lightdash.com/agents/deep-research, checked 2026-09-10) | | Data apps, AI made | **Yes** — Live data apps on your own design system, from a prompt | **Yes** — generates an interactive React data app from a prompt in a sandboxed iframe, free during its current beta (https://docs.lightdash.com/data-apps, checked 2026-09-10) | | Dashboards, AI made | **Yes** — A live dashboard from a single prompt | **Partial** — data apps can generate a dashboard-style app from a prompt, but native dashboards are still built tile by tile (https://docs.lightdash.com/explore/dashboards, checked 2026-09-10) | | AI workflows | **Yes** — One prompt builds the whole chain: detect, analyse, export and route the result | **Partial** — an agent conversation can become a recurring scheduled delivery, but no single prompt chains detect, analyze, export and route (https://docs.lightdash.com/agents/use-ai-agents, checked 2026-09-10) | | Alerts | **Yes** — Threshold and anomaly alerts described in plain English, which analyse the change rather than only announce it | **Yes** — threshold alerts on a saved chart's first value, delivered to email or Slack, no anomaly detection (https://docs.lightdash.com/explore/create-alerts, checked 2026-09-10) | | Automated reporting | **Yes** — Scheduled reports written by an agent and delivered as PDF, PowerPoint or Excel | **Yes** — scheduled deliveries send a chart or dashboard as image, CSV, XLSX or PDF to email, Slack, Teams or Google Chat (https://docs.lightdash.com/explore/create-scheduled-deliveries, checked 2026-09-10) | | Cross-source data query | **Yes** — One question spanning every connected source | **No** — each project queries the one warehouse its dbt project compiles against, no cross-warehouse query in a single question | | Python scripts | **Yes** — SQL and Python in one editor, over every connected source | **No** — no in-app Python execution; the SQL runner is SQL-only, Python access is external via a Postgres-wire API or SDK (https://docs.lightdash.com/integrations/metrics-sql-api, checked 2026-09-10) | | Slack, Teams, Claude, ChatGPT | **Yes** — Slack, Teams, Claude, ChatGPT and Cursor | **Slack** — native chat lives in Slack; Claude, ChatGPT and Codex reach Lightdash only as MCP query clients (https://docs.lightdash.com/agents/lightdash-mcp, checked 2026-09-10) | | MCP support | **Yes** — In both directions: we expose one, and we read yours | **Yes, both directions** — ships an MCP server for Claude, ChatGPT and Codex, and agents can call external MCP servers like Notion and Linear (https://docs.lightdash.com/agents/lightdash-mcp, checked 2026-09-10) | | Query bench | **Yes** — SQL and Python with schema-aware autocomplete, and AI edits proposed as a diff you approve | **Yes** — SQL runner with a warehouse table browser and schema panel; no documented autocomplete or AI-diff editing (https://docs.lightdash.com/explore/sql-runner, checked 2026-09-10) | | Context and memory management | **Yes** — Rulesets, knowledge, dropped files and MCP sources such as Notion | **dbt-defined semantic layer** — metrics, dimensions and tables defined once in version-controlled YAML that every chart, agent and API call reads from (https://docs.lightdash.com/semantic-layer, checked 2026-09-10) | | Connectors | **700+** — Connectable, 124 of them without talking to us | **9 warehouses** — BigQuery, Snowflake, Redshift, Databricks, Postgres, Trino, ClickHouse, Athena and DuckDB, no operational-system connectors (https://www.lightdash.com/pricing, checked 2026-09-10) | | Answer accuracy | **97.8%** — On LegendEHR's tuned agent in production, with a confidence score on every answer | **None** — no published accuracy benchmark found on Lightdash's own pages | Last checked 2026-09-10. Lightdash pricing page: https://www.lightdash.com/pricing ## Why choose Supaboard over Lightdash Supaboard connects to the operational systems that never make it into a dbt project, so a company doesn't need a modeling effort before its agents can answer a real business question. The same AI that answers questions also builds the dashboard, the data app, and the automation that routes the result, as one system rather than a stack of separately priced add-ons. And because pricing is a flat per-seat rate with usage included, a team doesn't need to budget for AI agents, embedding, and data apps as three more line items after buying the base product. ## Supaboard vs Lightdash, in our own words Lightdash's pitch is that governed BI comes from a semantic layer you already have: define metrics, dimensions, and joins once in dbt YAML, check them into git, and let every chart, dashboard, and now AI agent query that source of truth. It ships as MIT-licensed open source you can self-host free, and as a hosted Cloud Pro plan at a flat $3,000 a month with unlimited seats, not per user. The AI layer is newer, billed mostly as a custom add-on. The real question isn't whether Lightdash has AI, it's whether your team already has, or wants to build, the dbt project everything else depends on. ### Where the cost actually lands Open source is free to run, but that means hosting the app, the database, and your own AI provider keys, and self-hosted AI agents specifically need a separate Enterprise Edition license, gated out of the MIT core by Lightdash's own license file. Cloud Pro removes the ops work for a flat $3,000 a month regardless of headcount, with AI agents and embedding billed as separate add-ons. The cost most teams underweight is upstream: Lightdash needs metrics already modeled in dbt before anyone can ask a question. ### What is actually different Lightdash's AI surfaces are real, not vaporware. Deep research runs a coordinator plus up to two isolated data workers and returns an evidence-backed report, comparable in ambition to Supaboard's Deep Dive. But every surface sits on metrics an analytics engineer already defined in dbt. Supaboard's agents tune directly on a customer's business rules against a connected source, no modeling project required, so a team goes from connecting a source to a first analysis in about five minutes rather than standing up a dbt project. The same split shows in dashboards. Lightdash's core dashboards are still assembled tile by tile from saved charts. Its newer Data apps feature (Beta, currently free) can generate a dashboard-style app from a prompt, the closer comparison, but it still only queries the one warehouse the project's dbt models compile against. Supaboard generates the same kind of live app on the customer's own design system, pulling from any of its 700+ connected sources in one app, not just the modeled warehouse. Alerts and reporting tell a similar story. Lightdash alerts are threshold conditions on a saved chart's first row, sent to email or Slack, no anomaly detection. Scheduled deliveries cover email, Slack, Teams, and Google Chat as an image, CSV, XLSX, or PDF. Supaboard's alerts add anomaly detection in plain English, and its scheduled reports are agent-written rather than a rerun of a saved chart. The closest Lightdash gets to automation is scheduling a recurring delivery from an agent conversation: a schedule, not a workflow. No single prompt chains detect, analyze, export, and route the way Supaboard's AI workflows do. Both products expose an MCP server and call out to other MCP servers, a fair match. Chat surfaces diverge: Lightdash agents have a native presence in Slack, but Claude, ChatGPT, and Codex only reach it as MCP query clients, and ChatGPT's MCP support is still "coming soon" by Lightdash's own account. Teams and Google Chat are one-way delivery targets, not conversation surfaces. Supaboard ships native chat in Slack, Teams, Claude, ChatGPT, and Cursor. Python and cross-source querying follow the same pattern. Lightdash's SQL runner is SQL only; Python access means an external script hitting Lightdash's Postgres-wire metrics API or Python SDK from outside the product. Supaboard runs SQL and Python in the same in-app editor. Because a Lightdash project is scoped to the one warehouse its dbt project compiles against, a single question can't span two warehouses the way a Supaboard question spans every connected source at once. That scope difference runs to connectors, too: Lightdash reaches nine warehouses and nothing else, with no path to operational systems that never make it into a dbt project. Supaboard's 700+ connectors cover those same warehouses plus the systems most companies never model. The honest tradeoff: a team with a mature, well-modeled dbt project that wants an analytics-engineer-first tool gets a genuinely good foundation in Lightdash's semantic layer, and its AI features are catching up fast. A team whose metrics live outside a dbt project can't reach them in Lightdash without modeling work first, and can reach them in Supaboard the same afternoon they connect the source. ### What to test if you trial both Give each product the same messy question spanning two systems, for example why support tickets spiked relative to last month's product launch cohort, where half the answer lives in a modeled warehouse and the other half in a tool nobody has piped into dbt. Watch how much setup each demands before answering, then compare the dashboard or data app for how much came from one prompt versus how much needed an analytics engineer to build the model first. ## FAQ ### How does Lightdash's pricing compare to Supaboard's? Lightdash's core is free and open source if you self-host it yourself; its hosted Cloud Pro plan is a flat $3,000/month with unlimited seats, and AI agents are a separately quoted add-on on top of either tier. Supaboard is $99/seat/month billed monthly, or $83/seat/month billed annually, with usage credits already included in the seat, no separate AI add-on to buy. ### Can Supaboard read the same data Lightdash already touches? Yes. Supaboard connects directly to the same warehouses Lightdash requires for its dbt project, including Snowflake, BigQuery, Redshift, Databricks, and Postgres, among its 700+ connectors, so running both in parallel or switching over doesn't mean re-piping your data. ### What does Supaboard produce that Lightdash doesn't? Lightdash's own Data apps feature (Beta, free for now) can generate an app from a prompt, but it's scoped to the one warehouse a Lightdash project's dbt models compile against. Supaboard generates the same kind of live data app on the customer's own design system, but pulls from any connected source in one app, including operational systems that were never modeled in dbt. ### Do I still need a dbt-fluent analytics engineer for Lightdash, but not for Supaboard? For Lightdash, yes in practice: its own docs say it "leverages your existing dbt project to build and govern your entire BI platform," and every agent, dashboard, and data app answer routes through metrics someone defined in dbt YAML first. Supaboard's agents tune directly on a customer's business rules against a connected source, with no modeling project required before the first analysis. ### Can Supaboard self-host or run in our own VPC, the way Lightdash's open-source edition does? Yes, on Supaboard's Enterprise plan. Worth noting the two aren't identical: Lightdash's core is MIT-licensed and free to self-host from day one, though AI agents specifically require a separate Enterprise Edition license even when self-hosted. Supaboard's self-hosted option is Enterprise-plan only, with no free self-hosted tier. ## Other comparisons - [Supaboard vs Microsoft Power BI](https://supaboard.ai/compare/power-bi) - [Supaboard vs Metabase](https://supaboard.ai/compare/metabase) - [Supaboard vs ThoughtSpot](https://supaboard.ai/compare/thoughtspot) - [Supaboard vs Looker](https://supaboard.ai/compare/looker) - [Supaboard vs Qlik](https://supaboard.ai/compare/qlik) - [Supaboard vs Domo](https://supaboard.ai/compare/domo) - [Supaboard vs Sigma](https://supaboard.ai/compare/sigma) - [Supaboard vs Omni](https://supaboard.ai/compare/omni) - [Supaboard vs Zenlytic](https://supaboard.ai/compare/zenlytic) - [Supaboard vs WisdomAI](https://supaboard.ai/compare/wisdom-ai) - [Supaboard vs TextQL](https://supaboard.ai/compare/textql) - [Supaboard vs Sundial](https://supaboard.ai/compare/sundial) - [Supaboard vs Wren AI](https://supaboard.ai/compare/wren-ai) - [Supaboard vs Basedash](https://supaboard.ai/compare/basedash) - [Supaboard vs Hex](https://supaboard.ai/compare/hex) - [Supaboard vs Julius AI](https://supaboard.ai/compare/julius-ai) - [Supaboard vs Bruin](https://supaboard.ai/compare/bruin) - [Supaboard vs Snowflake Cortex Analyst](https://supaboard.ai/compare/snowflake-cortex) - [Supaboard vs Databricks Genie](https://supaboard.ai/compare/databricks-genie) - [Supaboard vs Amazon QuickSight](https://supaboard.ai/compare/amazon-quicksight) - [Supaboard vs ChatGPT](https://supaboard.ai/compare/chatgpt) - [Supaboard vs Claude](https://supaboard.ai/compare/claude) --- - Site index for agents: https://supaboard.ai/llms.txt - Sitemap: https://supaboard.ai/sitemap.xml - Blog feed: https://supaboard.ai/blog/rss.xml - Product documentation: https://docs.supaboard.ai Any page on https://supaboard.ai is available as markdown: append `.md` to its path, or send `Accept: text/markdown`.