Documentation Index
Fetch the complete documentation index at: https://docs.costoptix.com/llms.txt
Use this file to discover all available pages before exploring further.
How It Works
Cost Optix uses rolling Z-score and adaptive statistical algorithms to establish a baseline of expected spend per service and per account. When actual spend deviates significantly from the expected range, an anomaly is raised at the service level — not just the account total. This means Cost Optix can identify that, for example, your Azure Blob Storage costs spiked 300% while your overall Azure spend only increased 12% — pinpointing the root cause rather than just flagging a top-level number. Each detected anomaly includes:- The service name and cloud provider
- The anomaly date
- Actual spend vs expected spend
- Deviation percentage
- Severity rating
- Detection method used
Anomaly Workflow
Anomalies can be managed directly in the dashboard:| Status | Description |
|---|---|
new | Freshly detected, unreviewed |
acknowledged | Team member has seen it |
investigating | Actively being looked into |
resolved | Root cause identified and addressed |
false_positive | Flagged as expected behaviour |
Alerts & Notifications
Anomaly alerts can be delivered to Slack, Microsoft Teams, Discord, or any custom webhook endpoint. Each alert includes the service name, severity, actual vs expected cost, and a direct link to the anomaly in your dashboard. See Webhooks for setup instructions.Tier Limits
| Tier | Anomaly alerts / month |
|---|---|
| Starter | 3 |
| Professional | 100 |
| Business | Unlimited |
| Enterprise | Unlimited |
The limit applies to anomalies stored and tracked per month. Detection still runs across all services — anomalies beyond the limit are surfaced in the dashboard but not retained for historical review.