atSpark vs Mode
Mode is the BI tool data teams reach for when SQL is the lingua franca and notebooks are the deliverable. atSpark is the AI analyst finance and RevOps teams reach for when they want answers in plain English. They live at different ends of the same stack — here’s how they compare.
Side-by-side
| atSpark | Mode | |
|---|---|---|
| Primary audience | ✓ Finance, RevOps, growth — non-technical | ~ Data teams, analysts |
| Interface | ✓ Plain-English chat (AI Assist) + ready-made reports | ~ SQL editor + notebook + dashboards |
| Connectors | ✓ Stripe / HubSpot / QuickBooks / Zoho + 100 more, one click each | ~ Bring your own warehouse |
| Pre-built SaaS metrics | ✓ 150+ on day one | × You build them in SQL |
| Custom SQL when you need it | ~ Available, not the focus | ✓ Core strength |
| Notebook-style analysis | × Not the focus | ✓ Native |
| Time-to-first-dashboard | ✓ ~14 minutes | ~ Weeks (warehouse + modeling + SQL) |
| Embeddable | ✓ AI Assist + dashboards via iframe + JWT | ✓ Mode embeds |
| Row-level security | ✓ Per-org, per-tenant, per-user | ~ Possible but you configure it |
Where each one wins
atSpark wins for…
- Finance / RevOps / growth teams without dedicated data engineers
- Plain-English self-serve so every teammate gets answers without filing a ticket
- SaaS-shaped questions out of the box (MRR, ARR, cohorts, NRR, retention)
- Onboarding in minutes, not a quarter
- Embedded analytics inside your own product (with Embed Portal)
Mode wins for…
- Data teams whose primary deliverable is a SQL notebook
- Deep ad-hoc analytics where you control every join
- R / Python notebook integration in the same workspace
- Custom visualizations beyond standard SaaS charts
- Organizations that already standardize on dbt + warehouse + SQL
When to pick which
Pick atSpark if the people asking the questions aren't the people writing the SQL. The most common pattern: a finance lead, a CFO, a head of RevOps, a CEO — all of whom want answers, none of whom want a notebook.
Pick Mode if you have a data team and the deliverable is "an analyst will write up a report." Mode is also a great fit for product analytics that lives next to engineering and is owned by data.
Pick both if you have both audiences. They share a warehouse, so the same source-of-truth feeds both. atSpark covers self-serve revenue analytics; Mode covers deep custom analysis.
Common questions
Can atSpark write SQL?
AI Assist translates plain-English questions into SQL under the hood and runs it against your warehouse. You see the answer; the SQL is available if you want to inspect or copy it.
Do I need a data team to use atSpark?
No. atSpark connects directly to your billing, CRM, ledger, and subscription tools and ships pre-built models. A finance lead can stand it up alone.
What if I already use Mode?
Keep it. atSpark is for the audience that doesn't fit Mode — finance, RevOps, growth, leadership. Many teams run both on the same warehouse.
The shorter version
Mode is great for SQL people. atSpark is great for everyone else. If your team is finance, RevOps, or growth, you'll get answers faster with atSpark. If your team is data engineers and notebook authors, Mode is the better tool.
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atSpark · The AI analyst for SaaS revenue & finance
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