Clawgate solves a problem that's becoming universal for teams on Claude Code or Codex - AI spend that creeps up silently until the invoice lands. What makes it stand out is real enforcement, not just visibility: hard budget caps by token, session, or dollar amount that actually stop a run, paired with real-time spend breakdowns by user and project so you know exactly where the money's going. Add in model access controls to keep expensive frontier models from being the default, prompt compression that cuts token usage on heavy runs without hurting accuracy, and abuse detection for leaked keys or spikes, and it feels less like a dashboard and more like a governance layer. All of that through a one-line endpoint swap with zero retraining is the real win - genuinely useful infra for any team trying to keep AI's speed without losing control of the bill.
Hey Product Hunt 👋
I'm CK, Engineering Lead at Virstack LLC. Opviera (Formerly Clawgate) started as an internal tool we built to solve our own problem.
Here's what happened. Like a lot of teams, we gave our engineers Claude Code and Codex, and the productivity jump was real and immediate. But a few weeks in, our AI bill had climbed in a way none of us could explain. We couldn't see who was spending what, which projects were burning the most, or why a documentation task had quietly run on a frontier model all weekend.
Turns out we weren't alone. Uber burned through its entire annual AI budget in four months. Meta's engineers ran up 73.7 trillion tokens in a single month racing an internal usage leaderboard. If it can happen to them, it can happen to a 30-person team. It happened to us.
So we built Clawgate. It's an API gateway and dashboard that sits between your developers and the AI providers.
What it does:
🔹 Track spend by user and by project, in real time
🔹 Set hard budget caps (daily/weekly, in sessions, tokens, or dollars) that actually stop the bill
🔹 Get usage alarms and email notifications when spend crosses a threshold you set (fully configurable)
🔹 Control which models each team can use & keep the expensive ones for who truly needs them
🔹 Mix and match models across providers, so Claude Code and Codex run on whatever's cheapest for the job
🔹 Compress bulky context before it hits the model (up to 92% fewer tokens, accuracy held)
🔹 Catch abuse: leaked keys, spikes, shared credentials without ever reading your code
Setup takes minutes. It's a one-line endpoint change in Claude Code or Codex, and your team never has to learn a new tool or change how they work.
The whole idea: keep the speed AI gave you, without the surprise bill.
We'd genuinely love your feedback! What would make this a no-brainer for your team? And if you're running Claude Code or Codex across a team right now, I'm especially curious how you're handling cost today. Happy to answer anything in the comments 🙏
honestly the prompt compression claim is what caught my eye, 92% on heavy agent workloads is a wild number and basically the reason I'd even consider switching tools. nice execution on something that's been a real pain point
@serhatkulaber That's the one we're proudest of, and also the one we knew people would rightly be skeptical of, so it's measured on real agent workloads. The heavier the run, the more stale context there is to compress, which is why the number lands where it does. Give it a try and tell us what you get. 🙏
finally something that stops me from getting blindsided by a runaway agent run at 3am. the per-project visibility is super clear and the prompt compression actually moved the needle on my heaviest workflows
@devran1236463 The 3am runaway is exactly the nightmare we built the hard caps for, so glad you're covered now. And the compression moving the needle on your heaviest workflows is what we were after, not just a number on a slide. Appreciate your comment. 🙏
Finally something that tackles the runaway token bill problem head on. The prompt compression on agent workloads is what got me, shaved a meaningful chunk off our Codex spend in the first week.