Tokenwise is a one-line LLM proxy (OpenAI-compatible baseURL) for makers and small teams. It learns from your real requests, shows exactly where you're overpaying, proven with quality checks on your own traffic, not public benchmark, and lets you apply the fix in one click while it verifies the savings in real dollars.
Hey everyone, Theo here.
I build a few small SaaS on the side of a full-time data engineering job, and at some point every one of them started leaning on LLMs. My API bills crept up every month and honestly I could never tell you why. Which feature, which prompt I'd changed last week, which model I picked without really thinking about it. I'd just top up credits and move on.
The part that really got to me was the spend I couldn't even see. Claude Code running all day while I work, plus Cursor and Codex. None of that shows up anywhere until the invoice lands, and it turned out to be the money I understood the least.
I tried the tools that already existed. One felt like it was in maintenance mode, one needed a whole observability setup just to get started, and one only worked if your stack was built around a specific framework. None of them were made for someone like me who just wanted to know where the money went and what to do about it.
So I built Tokenwise. You add one line of code, or point your coding agents at it with no production changes, and you see every call: cost, latency, tokens, and what's being wasted. Then it tells you what to cut. A cheaper model here, a cache there, a bloated prompt to trim. Every fix gets checked against your own quality bar first, so you're never trading cost for worse output.
The idea shifted a lot while I was building it. I started out thinking it was a dashboard. Then I realised nobody wants another dashboard, they want the answer: here's the $842 a month you're burning, and here's the one click to fix it. The real value was proving the savings on your own traffic, live.
It's early and I'd genuinely love your honest feedback. Tell me what's missing, what's confusing, what you'd never use. That's more useful to me right now than anything.
Thanks for taking a look.
Observe-only is probably where I’d start, especially for Claude Code spend. The scary part is the “apply” step.
Before swapping a model, does Tokenwise show exactly which traffic it will touch, and is there an easy rollback?
About Tokenwise on Product Hunt
“A smart LLM proxy that shows where you're overpaying”
Tokenwise launched on Product Hunt on June 1st, 2026 and earned 105 upvotes and 9 comments, placing #12 on the daily leaderboard. Tokenwise is a one-line LLM proxy (OpenAI-compatible baseURL) for makers and small teams. It learns from your real requests, shows exactly where you're overpaying, proven with quality checks on your own traffic, not public benchmark, and lets you apply the fix in one click while it verifies the savings in real dollars.
Tokenwise was featured in Analytics (172.1k followers), Developer Tools (513.3k followers) and Artificial Intelligence (469.8k followers) on Product Hunt. Together, these topics include over 181.9k products, making this a competitive space to launch in.
Who hunted Tokenwise?
Tokenwise was hunted by Théophile Louvart. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Théophile Louvart