AI Cost Anomaly Monitoring & Governance Platform
Stop Runaway Tokens Before They Drain Your AI Budget.
From agent loops and retry storms to test environment waste and suspicious internal usage, TokenPilot helps teams detect, locate, and govern AI cost anomalies in real time.
- $12.5k
- Monthly AI cost
- $58k
- Anomaly risk
- 3
- Open incidents
As agents automate more work, retries become automatic, and employees call models directly, token spend can shift from normal usage into compounding budget exposure.
TokenPilot turns every AI call into a traceable, explainable, and governable cost anomaly signal.
Product Console
Detect the anomaly, locate the source, and trigger governance actions
Report Agent$9.8kLoop 20x
Search API$4.6kRetry storm
Test env$3.1kPremium model misuse
Internal usage$2.4kNight spike
Cost Flow Map
Attribute AI spend to agents, environments, teams, employees, features, and workflows.
Root Cause Trace
Locate loops, retries, long context, model misuse, and suspicious usage sources.
Anomaly Watch
Detect spikes, loops, retry storms, test waste, and suspicious internal calls in real time.
Governance Advice
Recommend thresholds, rate limits, model switches, environment isolation, and audits.
Four Answers
The answers teams need to govern AI cost anomalies
Which tokens are running away?
Identify cost spikes, persistent calls, abnormal retries, and budget overruns.
Where did the anomaly happen?
Trace incidents to agents, APIs, environments, teams, employees, features, and workflows.
Why did it happen?
Explain loops, retries, model misuse, long context, and suspicious internal usage.
How should it be governed?
Generate rate limits, thresholds, model downgrades, permission audits, and isolation steps.
Anomaly Scenarios
Four anomaly patterns that drain AI budgets fastest
The more automated and distributed your model usage becomes, the more you need real-time cost anomaly monitoring.
Agent loops
Task chains fail to terminate and multi-step calls keep expanding token spend.
20x spend spike in 15 minutes
Retry storms
Failed API calls trigger automatic retries, raising cost, latency, and error rates together.
Failed calls trigger repeated retries
Test and internal waste
Test environments use premium models, or employees create suspicious high-frequency usage.
Trace by environment, user, and projectFor Operating Teams
CEO, finance, and CTO teams see the same anomaly truth
CEO
See which AI costs are running away and which product or automation flows are draining budget.
- Cost anomaly exposure
- Budget burn trends
- Agent governance priorities
Finance
Turn model vendor bills into explainable, alertable, and allocatable cost anomaly ledgers.
- Budget alerting
- Cost allocation
- Anomaly explanation
CTO
Catch loops, retry storms, test waste, and suspicious calls before they become billing incidents.
- Anomaly thresholds
- Call-chain root cause
- Model cost controls
First Step
Generate an executive-readable AI cost anomaly report first
Before implementing a complex system, see anomaly cost, source, impact, and governance advice.
AI Cost Anomaly Report · Example
Needs action
42%Anomaly cost in 3 workflows
68%From test and retry waste
20xAgent night spike
Findings
Report Agent shows looped calls, Search API has retry storms, and test environments are using premium models.
Actions
Set agent call caps, add anomaly thresholds, isolate test environments, and audit suspicious employee usage.
Early Access
Share bills or call logs to generate your first AI cost anomaly report
You will receive anomaly cost totals, source distribution, high-risk objects, anomaly events, suspected root causes, and the first governance recommendation.