Supaboard vs Sundial

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 has a free plan, then per-seat prices of $19 to $249 per editor a month ($16 to $208 billed annually), so an evaluator can price out ten or fifty seats without a call. Each paid seat includes a weekly AI allowance. When it runs out, new AI requests pause until the reset, or you can add credits. Extra storage is billed separately. The tradeoff isn't "cheap vs. expensive," it's "published 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 12 databases and warehouses directly and reads 22 apps live through their MCP servers, and one question can combine 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 servers 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: Both products offer ways to evaluate answers: Supaboard rates its confidence in each data answer and runs evals against each agent. Supaboard advertises a 97.8% accuracy result on the BI Bench; Sundial publishes none. Neither result is a substitute for running your own question set and judging what it means for your data. Confidence ratings 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.