CostGuard

I’ve been building a product called CostGuard over the last few months after noticing something I think is going to become a much bigger problem as more companies adopt AI: AI costs drift quietly. Most teams don’t realize something is wrong until the invoice shows up. An agent gets stuck in a loop, an automation quietly keeps running, a new model rollout increases spend, or one provider slowly starts consuming the majority of usage. Right now, most people only have dashboards after the fact — not actual monitoring or protection. So I built CostGuard to function more like an AI cost monitoring and alerting layer than just another analytics dashboard. The goal is simple: Catch unusual AI spend behavior before it becomes expensive. Current features include: * AI spend monitoring across providers * anomaly detection for unusual usage spikes * provider concentration alerts * forecasted monthly spend tracking * alert feed + issue detection * email alerts and notifications * demo/sample workspace for onboarding * support for OpenAI, Anthropic, Gemini, Bedrock, and more One thing I intentionally moved away from was over-focusing on token analytics and charts. There are already a lot of dashboards being built. What felt more important was operational visibility and early warning signals. A few examples of the types of things CostGuard can detect: * sudden spikes in AI costs * one provider unexpectedly consuming most spend * abnormal usage patterns * forecasted budget overruns * services that suddenly become active again after being quiet Still early, but I’d genuinely love feedback from: * teams using AI APIs heavily * people building AI agents/workflows * founders managing growing AI infrastructure * agencies running lots of automations I think “AI operational oversight” is going to become a much bigger category over the next few years, especially as autonomous workflows become more common. Would love thoughts, criticism, or feature ideas from people dealing with this already.