PerUnit VS Diffyn

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.

Diffyn's answer:

Addresses workflow and change management on LLM prompts, provide teams with traceability and visualization of tests across multiple models, provide deeper understading into efficiency of these prompts.

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.

Diffyn's answer:

Diffyn is the platform that specializes on both change management and multi-model analysis.

PerUnit's answer

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

Diffyn's answer:

React, Next.js, POSTGRESQL

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.

Diffyn's answer:

Professionals incorporating LLMs or AI tools in their workflow and wants to keep track of changes and test their prompts.

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.

Diffyn's answer:

I started working on Diffyn when I notice that prompting has become an essential part of work across many industries. While there are version control platofrms like github, they are not designed for just prompt management are can be overkill such applications, it is also not integrated natively with various LLMs and relevant tools for users to validate ideas and visualise results properly.

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.