Nirsoft Simple Program Debugger VS Tokenwise

Tokenwise's answer:

Most LLM cost tools stop at a dashboard: they show you aggregate spend and leave the fixing to you. Tokenwise is an optimizing gateway, not just observability. It shows cost per prompt, lets you act on it (route to cheaper models, cache, cap budgets) from one line of setup, then verifies the savings on your own traffic with a built-in quality check, so cutting cost can't silently hurt output quality. That closed loop, see then act then prove quality held, is the part nobody else does well.

Tokenwise's answer:

Three reasons. Setup is one line: you point your existing OpenAI or Anthropic SDK at our base URL, with no rewrite and no framework lock-in. It's actionable: where other tools give you charts and a generic "use a cheaper model" hint, Tokenwise applies the change and proves the dollar savings on your real traffic, with quality measured so you don't trade output for cost. And it's built and priced for solo makers and small teams, not enterprise. Most alternatives are heavier to set up, tied to one framework, or stop at showing you the bill.

Tokenwise's answer:

Solo AI makers and small teams shipping real products on the OpenAI and Anthropic APIs, usually spending $50 to $2,000 a month, often building with tools like Cursor, Claude Code, the Vercel AI SDK, Lovable, or Bolt. People who feel their LLM bill creeping up but don't have a platform team to instrument it. Increasingly also developers running agentic and multi-call workloads, where cost and quality are hard to attribute to a single call.

Tokenwise's answer:

I kept hitting the same wall building LLM products: the bill grows faster than the usage, and you can't easily say which feature or prompt is driving it. The tools I tried mostly showed aggregate spend, or were too heavy to set up, and when they suggested a cheaper model they compared against public benchmarks, which tell you nothing about whether quality holds on your actual prompts. So I built the thing I wanted: a gateway you drop in with one line that shows cost per prompt, lets you cut it, and proves the savings on your own traffic with quality measured rather than assumed. Tokenwise is that, opened up for other makers.

Tokenwise's answer:

TypeScript end to end. The app is a Next.js 16 monorepo (Turborepo) running on a Hetzner VPS with Docker, and the proxy runs on Cloudflare Workers at the edge for sub-50ms overhead. Data lives in Postgres with the TimescaleDB extension, accessed via Drizzle ORM. Auth is Better-Auth, payments run through Polar, email through Resend, and analytics through PostHog.

Tokenwise's answer:

I'm deliberately not inventing names here.