Forecast AI COGS per feature, per MAU, and per pricing tier before you ship — not after gross margin shows up red on the board deck. Compare GPT-5, Claude 4.6, Gemini 3, DeepSeek, and 25+ more models against your real usage shape.
SaaS economics used to be the cleanest story in software: 80%+ gross margin, near-zero marginal cost per user, the whole company chart pointed up and to the right. Then AI features happened. Suddenly every additional active user carries a variable token bill that scales with how aggressively they engage — and the most engaged users, the ones every product team optimizes for, are also the most expensive ones to serve.
That single line on the P&L has quietly become the largest determinant of whether an AI-native SaaS is a fundable business or a heavily subsidized growth experiment. A copilot that costs $0.04 in tokens per session against a $25 seat is fine. The same copilot routed to a frontier model, called fifteen times a session, by a power user who logs in daily, can cost $11-$18 in tokens against the same $25 seat. The seat is now margin-negative, and the spreadsheet rolling up to investor relations has no idea.
This calculator exists to put the per-feature and per-tier number on the page before the pricing meeting. Enter realistic call counts, prompt size, output length, and MAU at each pricing tier, and it cross-multiplies against published rates for every major hosted model. The output is a sortable cost table that drops straight into a board appendix, a pricing experiment doc, or the COGS section of a fundraising model.
Before you green-light an AI feature in the next sprint, model the expected per-MAU AI bill at projected adoption. A new AI summary feature that fires twice per session across forty percent of weekly actives sits in a very different cost band depending on whether the underlying model is GPT-5 or DeepSeek. The calculator's adoption and frequency sliders show the spread so the feature ships with a defensible margin story rather than a hopeful one.
If you offer a free tier with any AI feature, you are quietly running a subsidy program. The calculator gives you the worst-case monthly cost per free signup, which is the number you need to know before launching a paid acquisition campaign or a viral loop. Drop it into the pricing doc and the conversation about quotas, throttles, and the move-to-paid wall gets considerably faster.
Every AI-native SaaS board deck in 2026 has a slide on AI COGS, gross margin trajectory, and the unit economics at the next ARR milestone. The calculator gives you a defensible AI COGS line in under two minutes, and the same view exports the assumptions block your CFO or finance lead needs for the audit trail. No more "we'll refine the COGS number before the next board" carried forward across three quarters.
Most SaaS products discover, sometimes painfully, that running every AI call through the flagship model costs four to fifteen times more than it needs to. The calculator lets you model a routed setup — cheap model for first-pass and high-volume work, flagship for sensitive or explicit user-facing generation — and surfaces exactly how much margin a routing layer would unlock at current MAU. The payback period on building one is usually under thirty days.
Investors evaluating AI-native SaaS in 2026 underwrite to gross margin at scale, not gross margin today. They want to see a model that holds 70-80% blended margin at 10x current ARR, with a credible story about how model costs evolve. The calculator gives you the per-call cost curve under the current pricing regime, which is the starting point for the "what happens to margin when model X gets cheaper" sensitivity that every venture diligence asks for.
Three failure modes show up over and over inside AI-native SaaS in 2026:
The calculator surfaces all three by design — it asks for context size, output length, and call frequency separately, then recomputes against every supported provider. For canonical per-token rates check OpenAI's pricing page and Anthropic's Claude pricing. For broader benchmarks on AI-SaaS unit economics, a16z and OpenView publish regular reports on gross margin and pricing patterns at AI-native software companies.
To make the numbers concrete, here is how a typical AI summary-and-copilot feature lands when run through the calculator at a small seed-stage MAU base:
| Model | Cost / MAU / mo | Total monthly COGS |
|---|---|---|
| GPT-5 | $1.94 | $4,852 |
| Claude Sonnet 4.6 | $1.31 | $3,278 |
| Gemini 3 Flash | $0.18 | $444 |
| DeepSeek V3.1 | $0.10 | $248 |
| Mixed (Flash route + Sonnet copilot) | $0.46 | $1,158 |
Numbers above are illustrative. Plug your real per-MAU shape into the live tool to get a current comparison against the latest published rates. The "mixed" row is the pattern most profitable AI-native SaaS settles into — a cheap model for the high-volume invisible work and a flagship for the explicit user-facing generation. Multiply by your projected MAU at the next ARR milestone to see how the COGS line scales, and divide by ARPU to get the gross margin impact in basis points.
The AI bill is one line on the P&L. The other lines — brand, marketing site, compliance, and acquisition — the TinyTools suite already covers most of them without adding another seat license or vendor login:
The pattern is the same across all of them: free, single-purpose, no signup, no extra seat license to expense. For broader reading on AI-SaaS pricing and unit economics, the SaaStr archive tracks pricing-model shifts at AI-native startups, and Bessemer's Atlas publishes benchmark reports on gross margin and growth efficiency.
Yes — the table is plain HTML, so it pastes cleanly into a Notion doc, a Google Slides deck, a Linear spec, or a Figma board template. Many founders paste the per-feature COGS number directly into the COGS slide and the assumptions block into the model appendix.
Yes. GPT-5 mini, Claude Haiku 4.5, Gemini 3 Flash, and DeepSeek's full lineup are all included. Mini tiers run 5-20x cheaper than the flagship and are usually the right default for embeddings, classification, intent routing, autocomplete, and any feature the user does not consciously experience as "the AI feature."
The calculator reads from a price table that we update whenever a major provider publishes a change. Expect 1-3 day lag on smaller providers, near-real-time on the top five.
The current calculator models one feature at a time, but founders typically run it three or four times — one per feature, one per pricing tier — and sum the totals into a product-wide COGS line. A multi-feature dashboard is on the roadmap for the next release.
Self-hosted GPU pricing is too workload-dependent to model precisely, but we cover hosted serverless rates (Together, Fireworks, Groq, Bedrock) for Llama, Mistral, Qwen, and DeepSeek — those are a reasonable upper bound for what a self-hosted deployment saves once ops overhead and reliability engineering are factored in. Worth modeling once you cross roughly 50k MAU on a heavy feature.