PerUnit VS Spanlens

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.

Spanlens's answer:

The entire product is MIT licensed, including the dashboard, evals, and prompt A/B testing. There is no separate enterprise edition. Everything ships in one repo you can run with a single Docker Compose file. Integration is one line: you change the baseURL on your OpenAI, Anthropic, or Gemini client, and every call gets logged with its full body, token counts, cost, and latency. A few things that are usually paid add-ons come built in, like agent traces with a critical path view, A/B tests that use Welch's t-test to tell you whether a difference is real, and a recommender that flags cheaper models based on the traffic you actually send.

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.

Spanlens's answer:

Mostly because of where the market went. Helicone was acquired, LangSmith is closed source, and self-hosting Langfuse takes real setup work. Spanlens fills the gap those tools left: setup in about five minutes, one Docker Compose file if you want the data on your own servers, and no feature gating between free and paid tiers. To be fair, if you need SOC 2 reports and enterprise support today, the bigger platforms are ahead. If you want request logs, costs, and traces without adopting a heavy platform, that is what Spanlens was built for.

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.

Spanlens's answer:

Developers who ship LLM features in production apps. The typical user is a solo developer or a small team that added OpenAI or Anthropic calls to their product and now has no clear picture of what those calls cost or why some are slow. Agent builders are the second group, since multi-step workflows are hard to debug without traces. It is a developer tool through and through: if you don't touch code, you won't get much out of it.

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.

Spanlens's answer:

I was building LLM apps on the side and kept pasting token counts into a spreadsheet to figure out what each feature cost me. The tools I tried were either acquired mid-migration, closed source, or heavier to self-host than the app I was trying to monitor. So in April 2026 I started building the tool I actually wanted: change one line, see every request. It launched in June 2026, and the whole codebase went up on GitHub under MIT from day one.

PerUnit's answer

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

Spanlens's answer:

TypeScript across the stack. The dashboard is Next.js, the API and proxy run on Hono, and data is split between Supabase Postgres for accounts and relational data and ClickHouse for request logs, which grow fast. The repo is a pnpm monorepo that also holds the JavaScript and Python SDKs, a CLI, and an MCP server. Self-hosting runs on Docker Compose, and OpenTelemetry traces can be ingested over OTLP.

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.

Spanlens's answer:

We don't publish customer names yet. Spanlens launched in June 2026, and most users so far are indie developers and small AI teams.