Datost vs Numerous AI. Spreadsheet AI vs the warehouse analyst.
- The data already lives in a spreadsheet and the unit of work is a row.
- You are enriching, classifying, or summarizing cells in Sheets or Excel.
- You do not have a data warehouse and may never need one.
- You want a low-friction add-in for a power user, not an org rollout.
- The real data lives in a warehouse, not a spreadsheet — and you need answers from it the team can trust.
- Questions need to join the warehouse with CRM, billing, product analytics, and uploaded docs.
- You want answers in Slack with the SQL attached so the analyst can audit and the next person can build on them.
- You want proactive monitoring — Datost watches your top metrics and pings the channel when one breaks.
- Benchmark-verified accuracy on real schemas matters (75.2% on BIRD-Interact for Datost).
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 | Numerous |
|---|---|---|
Lives in Slack, in-thread for the whole team Ask in any channel. The team sees the sourced answer at the same time. | ||
Runs SQL against your warehouse Snowflake, BigQuery, Redshift, Postgres — on live tables. | ||
Joins warehouse + CRM + billing + product + docs in one query The shape of question a real data analyst handles every day. | ||
Proactive metric monitoring Datost watches your top metrics and posts in the channel when one breaks. | ||
Returns the SQL with every answer Auditable replies for the analyst, reusable for the next person. | ||
Grounds answers in your metric definitions Upload your "what counts as activation" doc. Datost retrieves and cites it. | ||
AI =formula inside Google Sheets / Excel Per-cell AI on spreadsheet data. Numerous’s core feature. | ||
Per-row enrichment / classification at spreadsheet scale AI-categorize 10,000 rows, summarize a column. Numerous is built for this. | ||
Audited on a public text-to-SQL benchmark BIRD-Interact (ICLR 2026). See accuracy section below. |
Datost scores 75% on warehouse text-to-SQL. Numerous lands around 36%.
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. Numerous AI lands around 36% on warehouse-style questions — close to the bare-model baseline, because warehouse text-to-SQL is not what Numerous is built for. Datost scores 75.2% on top of the same model. The gap is grounding: schema retrieval, metric definitions, and clarification before generating SQL. Numerous is excellent at the job it is built for — per-row spreadsheet enrichment — so this comparison is really about which tool you need.
Read the benchmark write-upNumerous is AI inside a spreadsheet. Datost is AI on top of your warehouse.
Numerous is the right tool when the data already lives in a Sheet and the unit of work is a row. Datost is the right tool when the data lives in a warehouse joined with the rest of the business stack, and the unit of work is a sourced answer the whole team can see in Slack. Different problems, different shapes — and Datost is the one to pick when you need a real data analyst.
Open a Google Sheet, write =NUM("classify this support ticket") in a cell, drag it down 1,000 rows. The AI runs per-row and fills in the column. Useful for enrichment and tagging at spreadsheet scale.
Ask in #revenue-ops "which support tickets last week mention churn risk, joined with their Salesforce account ARR?" Datost runs the join, returns the sourced answer in the thread with the SQL attached. The team acts. Separately, Datost is already watching your top revenue metrics; if churn risk spikes, the channel sees it before anyone asks.
Questions buyers ask us about Numerous AI.
Is Datost a replacement for Numerous AI?
No — they solve different problems. Numerous is AI inside the spreadsheet (per-row enrichment, classification, summarization in Sheets and Excel). Datost is AI on top of the warehouse (cross-source sourced answers in Slack, with the SQL attached). Some teams use both: Numerous for tactical spreadsheet work, Datost as the data analyst the whole team relies on.
Can Datost enrich rows in a Google Sheet for me?
No — that is exactly the spreadsheet-AI job Numerous is built for. Datost is optimized for live warehouse queries joined with business systems and uploaded docs. Per-row AI enrichment in a Sheet is a different surface and a different shape of work.
We do not have a data warehouse. Should we still consider Datost?
If your data is purely in spreadsheets, Numerous or similar tools will be a better fit right now. Datost gets most of its value from grounding in warehouse schema + business systems. If you have or are setting up Snowflake / BigQuery / Postgres, that is when Datost makes sense.
What about price?
Numerous is priced per individual user (around $10/month at last check, with a 7-day trial); check numerous.ai for current pricing. Datost is sales-led, priced for org-wide team access since the whole team is the user.
30 minutes. Bring a real question your team has been waiting on.