We’re getting hit by AI sticker shock. How are you guys catching and stopping this stuff?

We’re dealing with some pretty painful AI cost issues right now, and I’m curious how other teams are handling this. A few things happened on our side: First, we had one bad loop that kept calling the model. Nothing fancy, just bad engineering control. Our normal monthly AI bill was around $10k, but that one issue pushed a single day over $5k before we noticed. Second, someone used a production Gemini key in an AI coding tool / CLI tool. No real limits, no separate dev key, no clear boundary. From the provider side it just looked like valid usage, but internally it was definitely not where we expected that key to be used. Third, we found some code using API keys directly that were not managed in our normal key system. So when the cost went up, we could see the money being burned, but it was hard to quickly figure out where it came from, who owned it, or whether it was safe to shut off. So yeah, we’re kind of getting hit by AI sticker shock. For teams using AI APIs internally, have you run into similar problems? I’m mostly curious about the practical side: How did you first notice something was wrong? Was it a billing alert? Someone from finance? A dashboard? Logs? A traffic spike? Or just someone manually checking the usage page? And once you noticed it, how did you actually stop it? Did you kill the key, shut down a job, roll back code, add quotas, rotate credentials, block certain tools from using prod keys, or build some kind of internal AI gateway? Also, how long did it usually take you to figure out the source? Would love to hear what actually worked for you guys.