Datost vs Mode. The analyst your team can actually use.
- Your data team writes SQL + Python every day and wants a polished IDE for it.
- Curated, published reports the team subscribes to are your durable artifact.
- You have the analyst headcount to build and maintain those reports.
- ThoughtSpot’s broader BI suite is part of the buying story.
- Business teams should be able to ask without filing a ticket.
- Answers should land in Slack with the SQL attached, not as a report URL.
- You want to join the warehouse with CRM, billing, product analytics, and uploaded docs in one query.
- You want proactive monitoring — Datost watches your top metrics and pings the channel the moment one breaks.
- Benchmark-verified accuracy on real schemas matters: 75.2% on BIRD-Interact for Datost vs Mode AI around 43%.
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 | Mode |
|---|---|---|
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 + Snowflake + a PRD definition, joined at query time. | ||
Proactive metric monitoring Datost watches your top metrics and posts in the channel when one breaks. | ||
Built for non-technical users Marketing, finance, ops, sales can ask and get sourced answers themselves. | ||
Returns the SQL with every answer Every reply auditable; the analyst can verify and the next person can build on it. | ||
SQL + Python + R notebook for analysts Mode’s core surface — a polished IDE for analyst-team workflows. | ||
Published, curated reports the team subscribes to Build once, share a URL the team trusts and revisits. | ||
Audited on a public text-to-SQL benchmark BIRD-Interact (ICLR 2026). See accuracy section below. |
Datost scores 75% on BIRD-Interact. Mode AI lands around 43%.
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. Mode’s AI assist lands around 43% — 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-upMode is the IDE. Datost is the analyst.
Mode shines when there is an analyst writing SQL and Python daily, publishing curated reports the org subscribes to. The reason Datost wins for most buyers: the bottleneck is rarely "we need a better SQL IDE." It is "the business has questions, the analyst team is the bottleneck, and we want answers in Slack with the SQL attached so we can move." That is the job Datost is built for.
An analyst opens a notebook, writes SQL, runs Python on the result, charts it, publishes as a report. The team subscribes or views the URL. The notebook is durable; the analyst maintains it.
Someone in #revenue-ops asks "which expansion accounts need exec attention today?" Datost joins Salesforce with the warehouse, posts a sourced answer with the SQL attached. The team takes action immediately. Separately, Datost is already watching your top metrics; if any move overnight, the channel sees it before anyone has to ask.
Questions buyers ask us about Mode.
Is Datost a replacement for Mode?
For most teams, the day-to-day "we have a question and need an answer" job belongs to Datost. Mode keeps doing its job for curated published reports the analyst team owns. Teams that standardize on Datost find the long tail of one-off Mode reports stops getting built, but the recurring ones still belong in Mode.
Why Datost over Mode AI for plain-English questions?
Accuracy: Datost scores 75.2% on BIRD-Interact, Mode AI lands around 43%. Surface: Datost answers in the Slack thread where the question was asked, not inside the BI tool. Monitoring: Datost watches metrics proactively and pings when something breaks — Mode 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 subscription reports. For long-lived parameterized reports your org subscribes to, Mode is purpose-built.
What about price?
Mode (now part of ThoughtSpot) is sales-led; check mode.com or thoughtspot.com for current pricing. 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.