PerUnit VS MarginWard

PerUnit's answer

PerUnit is built for AI unit economics, not just cost tracking. We break down AI spend by:

Customer — see which accounts drive costs Feature — see which AI features burn through tokens Pricing tier — free vs paid user breakdown We connect AI costs to revenue via Stripe. Provider dashboards show totals; observability tools show tokens. PerUnit shows who's driving costs and whether they're profitable. No data warehouse or pipelines — direct API sync to OpenAI, Anthropic, and Google.

MarginWard's answer:

MarginWard is the only tool that joins your LLM costs with your Stripe revenue to show gross margin per customer. Cost-tracking tools show spend; revenue analytics show MRR, neither tells you which customers cost more than they pay. MarginWard does, and alerts you the moment one turns unprofitable.

PerUnit's answer

PerUnit is built for product and pricing decisions, not observability. Competitors focus on tokens, latency, and traces. We focus on:

Cost per customer Cost per feature Cost per tier — connected to revenue If you need to know who to charge more, what to gate, or where to cut, PerUnit gives you that. No pipelines, no data engineering, no data warehouse.

MarginWard's answer:

Observability tools track your LLM spend but don't know your revenue; SaaS analytics track revenue but ignore token cost. MarginWard is built for the intersection, gross margin per customer, with alerts on unprofitable accounts. Read-only Stripe key, plugs into Langfuse/OpenRouter or a simple ingest API, set up in ~15 minutes. Flat pricing, never a percentage of your spend. There's also a free calculator, no signup.

PerUnit's answer

Founders, product managers, and CFOs at companies building AI-powered products. Anyone who needs to answer:

Which customers cost the most? Are free users profitable? Should we gate AI behind paid plans? Teams that care about unit economics and margins, not just total spend.

MarginWard's answer:

Founders and teams building AI SaaS on flat or subscription pricing, products where LLM tokens are a real per-customer cost. From solo founders to small product teams who need their true unit economics, not just their MRR.

PerUnit's answer

We've shipped products with AI and watched margins disappear without knowing why. The AI bill was one number — we couldn't see which customers or features were driving it. Every product and pricing decision was a guess.

We built PerUnit so product teams can have the same clarity on AI costs that they have on revenue and headcount.

MarginWard's answer:

MarginWard was built by a solo founder running his own AI SaaS. One month he realised his biggest customers were also his least profitable, burning more in LLM tokens than they paid, and Stripe never told him. So he built the tool he wished existed: the real margin of an AI product, customer by customer.

PerUnit's answer

Frontend: Next.js, React, TypeScript, Tailwind CSS Integrations: OpenAI, Anthropic, Google APIs, Stripe Deployment: Vercel

MarginWard's answer:

Next.js, TypeScript, Supabase (PostgreSQL), Stripe, Vercel, Tailwind CSS, Resend, with Langfuse and OpenRouter integrations for LLM cost data.

PerUnit's answer

PerUnit is in early access. We're onboarding a small group of teams who need clarity on their AI unit economics. Our early users are founders and PMs at companies building AI features — from early-stage to growth — who want cost attribution before scaling further.