AI Ops Guardian: My Weekly Automated LLM Bill Audit System
I've been running AI agents at scale for a while, and one thing that quietly kills margins is LLM bill creep — token waste, prompt bloat, retry storms, and silent model downgrades that add up fast.
So I built a recurring audit system that runs every week and alerts me before the invoice surprises me.
What It Catches
- Token waste: Spikes in input/output token ratios that suggest a prompt is getting bloated
- Retry storms: When the model hits errors and your system retries 10 times instead of 1
- Prompt bloat: Gradual expansion of system prompts that inflate costs without improving output
- Anomaly detection: Spend that deviates from your established baseline
How It Works
Each week I get a structured report delivered to Slack and/or email with the anomalies, their likely cause, and what action to take.
No dashboards to check. No manual log digging. Just the signal and the fix.
Why I Built This
After watching one client's OpenAI bill go from $800/mo to $4,200/mo in three months — with zero change in output quality — I realized the gap wasn't the model, it was the observability.
Pricing
$499/month — covers one organization. Weekly email + Slack digest, anomaly root-cause notes, and a 30-minute call if something critical is found.
Top comments (0)