# Supaboard vs Sundial > Seventeen capabilities compared, from price and connectors to AI analysts, alerts, MCP support and answer accuracy, each figure sourced to Sundial's own pages. HTML version: https://supaboard.ai/compare/sundial ## At a glance | Row | Supaboard | Sundial | | --- | --- | --- | | Price | **$99/seat/mo** — Or $83 billed annually. Usage credits are in the seat | **Quote-only** — usage-based, priced on data volume and queries, tailored per customer with no public number (https://www.sundial.ai/pricing, checked 2026-09-10) | | AI native | **Yes** — The agent is the product, not a panel added to a dashboard tool | **Yes** — built as an agentic analytics platform from the outset, not a BI tool with AI added on (https://www.sundial.ai/, checked 2026-09-10) | | AI data analysts | **Yes** — Agents tuned on your business rules and metric definitions, with the SQL shown beside every answer | **Yes** — an Analysis Agent answers business questions end to end, from lookups to multi-step investigations (https://www.sundial.ai/analysis-agent, checked 2026-09-10) | | Deep reasoning | **Yes** — Deep Dive answers why a number moved and what to do next, not only what it is | **Partial** — root-cause investigation is documented; no forecasting capability is described (https://www.sundial.ai/analysis-agent, checked 2026-09-10) | | Data apps, AI made | **Yes** — Live data apps on your own design system, from a prompt | **Yes** — the Apps surface builds interactive views with KPI cards, charts, tables and filters from a prompt (https://docs.sundial.ai/analyze-visualize/apps, checked 2026-09-10) | | Dashboards, AI made | **Yes** — A live dashboard from a single prompt | **Yes** — same Apps feature; ask the Analysis Agent to build a dashboard and it stays always live (https://docs.sundial.ai/analyze-visualize/apps, checked 2026-09-10) | | AI workflows | **Yes** — One prompt builds the whole chain: detect, analyse, export and route the result | **Partial** — Slack supports threshold alerts and scheduled prompts with CSV/PDF export, no documented single chained automation (https://www.sundial.ai/sundial-for-slack, 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 and scheduled anomaly digests are delivered through Slack (https://www.sundial.ai/sundial-for-slack, checked 2026-09-10) | | Automated reporting | **Yes** — Scheduled reports written by an agent and delivered as PDF, PowerPoint or Excel | **Partial** — Slack delivers scheduled digests, rich PDFs and CSV exports; no PowerPoint or Excel export documented (https://www.sundial.ai/sundial-for-slack, checked 2026-09-10) | | Cross-source data query | **Yes** — One question spanning every connected source | **No** — not documented; only Snowflake and BigQuery are listed as connectors, with no stated multi-source query claim | | Python scripts | **Yes** — SQL and Python in one editor, over every connected source | **Yes** — the Analysis Agent can use SQL or Python directly for deeper investigations (https://www.sundial.ai/analysis-agent, checked 2026-09-10) | | Slack, Teams, Claude, ChatGPT | **Yes** — Slack, Teams, Claude, ChatGPT and Cursor | **Partial** — Slack is native; Teams and Codex are named on the homepage; Claude and Cursor connect only as MCP clients (https://www.sundial.ai/, checked 2026-09-10) | | MCP support | **Yes** — In both directions: we expose one, and we read yours | **Yes** — Sundial runs its own MCP server for external clients and reads Notion, Slack, Linear and GitHub as context (https://docs.sundial.ai/integrations-apis, checked 2026-09-10) | | Query bench | **Yes** — SQL and Python with schema-aware autocomplete, and AI edits proposed as a diff you approve | **No** — no SQL/code editor with autocomplete is documented; interaction is through chat and generated Apps/Artifacts | | Context and memory management | **Yes** — Rulesets, knowledge, dropped files and MCP sources such as Notion | **Yes** — a Context Engine holds the semantic layer, playbooks, AI context and warehouse metadata, fed by Notion/Slack/Linear/GitHub (https://docs.sundial.ai/for-data-teams, checked 2026-09-10) | | Connectors | **700+** — Connectable, 124 of them without talking to us | **2 (Snowflake, BigQuery)** — only two warehouse connectors documented, plus dbt/Astro metadata and four context connectors (https://docs.sundial.ai/connectors, checked 2026-09-10) | | Answer accuracy | **97.8%** — On LegendEHR's tuned agent in production, with a confidence score on every answer | **None** — Evals page describes methodology for testing the agent but publishes no accuracy or correctness percentage (https://www.sundial.ai/evals, checked 2026-09-10) | Last checked 2026-09-10. Sundial pricing page: https://www.sundial.ai/pricing ## Why choose Supaboard over Sundial Sundial connects to two warehouses (Snowflake and BigQuery) and pairs its analyst with a separate observability product to keep it honest; Supaboard connects to 700+ sources, spans all of them in one question, and folds the same confidence-scoring and evals into the system that also builds the dashboards, apps, and automations. Supaboard is newer than the legacy BI incumbents too, but between the two AI-native platforms, its bet is breadth-plus-trust in one product rather than trust sold as an add-on layer. ## Supaboard vs Sundial, in our own words Sundial's pitch is "Opinionated Intelligence for data teams": an agentic analyst that plans and investigates, plus an Observability and Evals layer so the data team can watch how its own agent is being used and tighten it over time. Pricing is usage-based and quote-only — priced on data volume and queries answered, tailored per customer, with no public number. That combination raises the real question for a buyer: is an in-house AI analyst with its own observability and evals layer, running on two documented warehouse connectors, the right starting point, or does a team want the analyst to already come with the breadth — connectors, workflows, chat surfaces — built in? ### Where the cost actually lands Sundial doesn't publish a number, so the real cost is a sales conversation plus a usage meter that scales with data volume and queries — a team can't budget it from the pricing page alone. Supaboard is a flat $99/seat/mo ($83 annually), with usage credits already inside the seat, so an evaluator can price out ten or fifty seats without a call. The tradeoff isn't "cheap vs. expensive," it's "predictable vs. negotiated." ### What is actually different On raw AI depth, Sundial and Supaboard land closer than the category suggests. Sundial's Analysis Agent does real root-cause work — plans an investigation, tests likely drivers, and shows its evidence — and drops into SQL or Python when a lookup isn't enough, which is a genuinely deep feature for a smaller company to have shipped. Supaboard's Deep Dive mode covers the same "why did this move" question, and keeps SQL and Python in the same editor over every connected source, so the difference here is scope more than capability. That scope gap shows up in concrete places. Sundial's dashboards and apps are the same feature — ask the Analysis Agent to build one and it stays always live — a real, working AI-generated dashboard, not a mockup; Supaboard does the same from a prompt, but also generates full interactive data apps on the customer's own design system, a distinct surface Sundial doesn't document. Workflow automation is thinner on Sundial's side: Slack gives threshold alerts and scheduled recurring prompts with CSV or PDF export, covering detect-and-notify, but there's no documented single prompt that chains detect, analyze, export, and route the way Supaboard's AI workflows do. Reporting follows the same pattern — Sundial's exports live inside Slack (PDF, CSV); Supaboard adds PowerPoint and Excel, from a scheduled agent rather than a manual export. Connectivity is the widest gap. Sundial's own docs list exactly two warehouse connectors — Snowflake and BigQuery — plus dbt and Astro for metadata, and four context connectors (Notion, Slack, Linear, GitHub) feeding its Context Engine. That's a deliberately narrow, modern-stack focus, and Sundial is upfront about it. Supaboard connects to 700+ sources, with 124 connectable without contacting Supaboard, and one question can span every connected source in a single answer — a claim Sundial's pages don't make for its two-connector setup. On chat surfaces, Sundial ships Slack natively and names Teams and Codex on its homepage, while Claude and Cursor reach it only as external MCP clients; Supaboard ships native presence in Slack, Teams, Claude, ChatGPT, and Cursor. Both run MCP in both directions — Sundial's server lets an external agent query its analyst, and it reads Notion/Slack/Linear/GitHub as context; Supaboard exposes a server and reads other MCP sources the same way. Where Sundial has a genuinely distinct idea is Observability and Evals: a dashboard, built by the data team rather than the AI, for reviewing how its own agent is actually being used and where its context is thin, backed by Evals that catch regressions before they ship. That's real and worth taking seriously if a team's biggest fear is an ungoverned AI analyst. But it's worth being honest in both directions: Supaboard invests in the same problem, with a confidence score on every answer and evals on the Business plan, and neither company publishes an accuracy number for its own product. Confidence scores and evals reduce wrong answers; they don't eliminate them, on Sundial's platform or on Supaboard's. The real distinction is less "one has evaluation, one doesn't" and more that Sundial sells evaluation as a separate, adjacent product, while Supaboard folds it into the same system that also connects, queries, and automates. ### What to test if you trial both Ask both to build a dashboard from the same prompt, then ask each to build an automation running from detection to a delivered report, across two of your actual data sources at once. Sundial should be strong on single-source investigation and on showing you how its own agent is performing internally. Test Supaboard specifically on cross-source questions, Python-alongside-SQL work, and whether the scheduled PDF/PPT/Excel report lands the way your team needs — then decide whether you want a separate observability product to trust the answer, or the evaluation built into the system doing the work. ## FAQ ### How does Sundial's pricing compare to Supaboard's? Sundial doesn't publish a price — it's usage-based, tied to data volume and queries answered, and quoted per customer (sundial.ai/pricing, checked 2026-09-10). Supaboard is a flat $99/seat/mo, or $83/seat/mo billed annually, with usage credits already included in the seat, so there's no separate AI meter to negotiate. ### If we're already on Sundial with Snowflake or BigQuery, can Supaboard read the same data? Yes. Sundial's own connector docs list Snowflake and BigQuery as its two supported warehouses (docs.sundial.ai/connectors, checked 2026-09-10); both are in Supaboard's 700+ connector catalogue, so switching or running both side by side doesn't require re-platforming your warehouse. ### What does Supaboard actually produce, beyond a chat answer? A prompt can produce a live dashboard, a full interactive data app on your own design system, a scheduled PDF/PowerPoint/Excel report, or an end-to-end automation that detects, analyzes, exports, and routes — all with the SQL shown beside the answer. ### How does Supaboard handle evaluation and accuracy, given that's Sundial's whole pitch? Supaboard attaches a confidence score to every answer and offers evals on the Business plan, the same problem Sundial's Observability and Evals product is built around. Neither company publishes an accuracy percentage for its own product generally; Supaboard's one published figure, 97.8%, comes from a specific customer's (LegendEHR) production-tuned agent, not a general benchmark. ### Can Supaboard run inside our own VPC the way Sundial can self-host? Yes, on the Enterprise plan — single-tenant containers, your own model provider keys, and no data egress, matching the kind of self-hosted deployment Sundial documents for regulated teams. ## 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 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 Lightdash](https://supaboard.ai/compare/lightdash) - [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`.