Is your AI feature paying for itself?
Every B2B SaaS in 2026 ships 3-8 distinct AI features. Each has wildly different unit economics — yet most founders can't answer the simplest question their CFO will ask in the next board meeting: "Is our summarise feature paying for itself?"
Edit the table below → see the per-feature P&L. The math is shown — nothing hidden.
≈ $300.0K / month
| Feature | Model | Calls/mo | $/call | % of ARR | |
|---|---|---|---|---|---|
Total monthly AI cost
$28.5K
Net monthly margin
$108.0K
Features tracked
3
Costs more than earns
1
Heads up — your summarise feature is costing about $8.5K/month more than it earns.
That works out to about $102.2K a year. Common quick wins: cache aggressive responses, switch to a cheaper model for low-stakes calls, gate it behind a paid tier, or raise its usage-share-of-ARR if customers genuinely value it.
Auto-summarise
summarise · gpt-5.2 · 6,200 calls/mo
Cost
$13.0K
Revenue
$4.5K
Margin
-$8.5K
Chat assistant
chat · claude-sonnet-4.5 · 412,000 calls/mo
Cost
$14.0K
Revenue
$54.0K
Margin
$40.0K
Semantic search
search · embedding + pinecone · 1,840,000 calls/mo
Cost
$1.5K
Revenue
$78.0K
Margin
$76.5K
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Cost = avg_cost_per_call × monthly_calls. Editable per row; nothing hidden.
Revenue share = (ARR / 12) × usage_share_of_arr_pct. Your honest estimate of how much subscription revenue this feature drives. Use heuristics (engagement %, time-spent, perceived value); refine as you learn.
Verdict: ROI ≥ 200% pays for itself, ≥ 25% profitable, ±25% break-even, otherwise costs more than it earns.
Why we built this: every AI SaaS founder we talk to has the same blind spot — they ship 4-8 AI features and have zero per-feature unit economics. Datadog has APM-level traces for this but it's an enterprise sale. We give you 80% of the answer in 5 minutes, free.