Datost vs Sigma. The answer your team needs.
Sigma waits for the question. Datost already answered it.
Every other tool on this page is reactive: someone has to know a question is worth asking, then go ask it. Datost watches the metrics and accounts that matter to each person and posts the issue, the fix, and the opportunity before anyone thinks to look.
A Sigma workbook only answers the question it was built to answer. When something moves that no dashboard anticipated, you’re waiting on an analyst to notice and go model it.
Datost watches the metrics behind your workbooks and posts the moment one breaks. You get the cross-source root cause in Slack with the SQL attached, so no one has to be staring at the dashboard.
- You are standing up enterprise BI for hundreds or thousands of dashboard consumers.
- Spreadsheet-style workbooks are how your analysts model and present data.
- Governed self-serve dashboards at scale matter more than answer latency on one-off questions.
- You have the analyst capacity to build and maintain the workbooks and models.
- You want an analyst watching your top metrics around the clock that pings the channel the moment one breaks, instead of a workbook that only answers what it was built for.
- The long tail of one-off questions (the ones no dashboard anticipates) is what actually slows the team down.
- You want the answer in Slack with the SQL attached, not a workbook to navigate.
- Cross-source joins (warehouse + CRM + billing + docs) matter more than perfect dashboards.
- Benchmark-verified accuracy on real schemas matters: 75.2% on BIRD-Interact for Datost vs Sigma AI around 39%.
What each tool actually does, side by side.
Green check = first-class feature. Orange dash = partial / possible with effort. Gray X = not the job this tool is built for.
| Feature | Datost | Sigma |
|---|---|---|
Lives in Slack, in-thread Ask in any channel. The team sees the sourced answer at the same time. | ||
Joins warehouse + CRM + billing + docs in one query Salesforce pipeline + Snowflake usage + a metric-definition doc, joined at query time. | ||
Plain-English question, sourced answer back No workbook, no formulas. The team gets the number and the SQL behind it. | ||
Proactive metric monitoring Datost watches your top metrics and posts in the channel when one breaks. | ||
Returns the SQL with every answer Auditable replies; the analyst can verify and the next person can build on it. | ||
Spreadsheet-style workbooks on the warehouse Cells, formulas, and pivot tables: Sigma’s core surface for analyst-built workbooks. | ||
Governed self-serve dashboards at enterprise scale Hundreds-to-thousands of consumers, row-level security, lineage. Sigma is built for this. | ||
Audited on a public text-to-SQL benchmark BIRD-Interact (ICLR 2026). See accuracy section below. |
Datost scores 75% on BIRD-Interact. Sigma AI lands around 39%.
BIRD-Interact is the University of Hong Kong + Google Cloud benchmark of 600 deliberately ambiguous business questions against 22 ugly real-world Postgres schemas. Claude Opus 4.6, the underlying frontier model, scores 33% on its own. Sigma AI lands around 39% on text-to-SQL questions, a few points above the bare model, which is what wrapping a frontier LLM in a thin retrieval layer typically buys you. Datost scores 75.2% on top of the same model. The gap is grounding: schema retrieval, metric definitions, and clarification before generating SQL.
Read the benchmark write-upSigma is the BI tool. Datost is the analyst.
Sigma is a strong, mature BI platform for analysts building governed workbooks at scale. The reason Datost wins for most buyers: the bottleneck on real analytics teams is rarely "we need a better dashboard." It is "we need to answer the long-tail cross-source questions that never justify a workbook, and we need someone watching our metrics so we are not the last to know when something breaks." That is the job Datost is built for.
An analyst opens a workbook, models tables, parameterizes filters, publishes to a workspace. The team self-serves through the workbook for the recurring questions it was built to answer.
Someone in #finance asks "what was our gross margin by plan last week, and is the dip in Pro net new or churn?" Datost joins billing with the warehouse, posts a sourced answer with the SQL attached. Separately, Datost is already monitoring your top metrics; if margin slips again, the channel sees it before anyone asks.
Questions buyers ask us about Sigma.
Is Datost a replacement for Sigma?
For most teams, they are complementary, but Datost is the clearer pick for the things slowing your team down day-to-day. Sigma is where the data team builds maintained workbooks and governed dashboards. Datost is where the business team asks the questions a dashboard would not anticipate, and where the monitoring on your top metrics lives. Teams that standardize on Datost find a lot of one-off Sigma workbooks stop getting built.
Why Datost over Sigma AI for plain-English questions?
Three reasons. (1) Accuracy: Datost scores 75.2% on BIRD-Interact, Sigma AI lands around 39%. (2) Surface: Datost answers in the Slack thread where the question was asked, not inside the BI tool. (3) Proactive monitoring: Datost watches metrics and pings the channel when something breaks. Sigma AI does not.
Does Datost build dashboards?
Datost generates live dashboards from a single Slack message, but they live in the thread and refresh on demand rather than as standalone parameterized data apps for hundreds of self-serve users. For internal enterprise dashboarding at that scale, Sigma is purpose-built.
What about price?
Both are sales-led. Sigma has a free "Sigma Public" tier and a free trial; the paid tiers are not published. Datost is sales-led, priced for broader org access since the whole team is the user.
30 minutes. Bring a real question your team has been waiting on, and watch Datost surface three you hadn’t thought to ask.