Control runaway AI spend before it becomes a billing incident

Adzly detects abnormal model calls, traces cost root causes, and recommends governance actions before AI bills spike.

Check my AI cost risk See how Adzly works

3 minutes. No bill upload. No API key required.

When calls run away, bills run away

Unexpected calls can rapidly multiply token spend. The real risk is not expensive models; it is abnormal usage that goes unnoticed.

AI agent loop causing repeated model calls and runaway token cost

Agents can burn budget on their own

Task chains fail to terminate, and model calls keep expanding.

Failure pattern: An agent enters a task loop that never reaches a stop condition and keeps calling models automatically.
Incident signal: Calls jump 20x in 15 minutes, rapidly amplifying token cost.

Retry storm causing failed model calls and rising token cost

Failed calls still cost money

API failures trigger repeated retries, raising errors and token spend together.

Failure pattern: Automatic retries stack after API failures, causing failed requests to compound.
Incident signal: Error rate, latency, and token cost rise at the same time.

Test environment AI calls consuming production token budget

Test environments consume production budget

Premium models are accidentally used for testing, debugging, and non-production workloads.

Failure pattern: High-cost models are used in test, debug, or non-production environments without guardrails.
Incident signal: Non-production traffic steadily consumes production budget, making month-end bills hard to explain.

Employee AI usage anomaly and token cost attribution

Employee usage should not be a black box

Track AI usage by person, team, and time window.

Failure pattern: Employees call models directly without budget boundaries or behavioral audit trails.
Incident signal: Night spikes, abnormal context lengths, or non-business model usage.

Not sure which risk applies to your team?Find your top AI cost risk in 3 minutes.

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Detect, trace, govern

Detect

Find anomalies

Identify token spikes, looping calls, repeated retries, night usage, and test waste.

Trace

Locate the root cause

Trace issues to agents, APIs, models, environments, projects, employees, teams, and workflows.

Govern

Recommend actions

Generate recommendations for rate limits, downgrades, circuit breakers, isolation, audits, and budget thresholds.

See how Adzly detects, traces, and governs abnormal token spend.

See the governance workflow

See abnormal spend at a glance

Trace anomaly cost back to agents, APIs, environments, employees, and workflows.

AI cost $12.5k +18% MoM

Anomaly risk $58k Needs action

High-risk objects 4 Open

Detection window 15m Real-time alert

Object Anomaly cost Root cause Recommended action

Report Agent $9.8k Loop 20x Set call caps

Search API $4.6k Retry storm Break / downgrade

Test env $3.1k Premium model misuse Isolate environment

Internal usage $2.4k Night spike Audit members

Where the money went

Attribute token spend by agent, environment, employee, team, feature, and workflow.

Where the problem started

Trace abnormal spend to the call chain, task, model, and responsible object.

See anomalies immediately

Detect token spikes, looping calls, retry storms, and test waste.

Know what to do next

Recommend rate limits, circuit breakers, downgrades, isolation, and audits.

Find the problem and the fix

Every anomaly maps to a concrete governance action.

Agent loopsStop repeated model calls

Retry stormsContain failed retries

Test environment wasteSeparate test budgets

Employee anomaliesClarify employee usage

Model mismatchUse the right model

Make AI cost understandable for every team

Spot cost black holes

Find the agents, product features, and automation flows that burn the most budget, then prioritize the biggest risks.

Anomaly cost exposure
Budget burn trends
High-risk agent ranking
Governance priorities

Explain every bill

Turn model vendor bills into explainable, allocatable, alertable, and accountable cost ledgers.

Budget alerts
Cost allocation
Anomaly explanation
Responsible object tracing

Trace call root causes

Locate call-chain root causes before agent loops, retry storms, and test waste become billing incidents.

Call-chain root cause tracing
Anomaly threshold alerts
Model cost controls
Rate limit / downgrade / breaker advice

The more AI you use, the more you need it

Once model calls operate at scale, cost governance cannot wait for the month-end bill.

Connected to multiple model APIs Multiple model API connections

Using agents or automated workflows AI agents and automated workflow usage

Monthly model spend above $1,500 Monthly model spend above the governance threshold

Bills are hard to explain AI model bill that is difficult to explain

Worried about abnormal calls burning budget Abnormal model calls creating AI cost risk

More than call volume

Adzly focuses on anomaly cost, risk objects, and governance actions.

Basic API monitoringAdzly

Tracks request volumeTracks cost anomalies

Tracks error rateTracks risk amount

Tracks endpoint statusTracks responsible objects

Requires manual investigationAutomatically locates root causes

Only sends alertsRecommends governance actions

Stop loss first, then measure ROI

Adzly first helps companies detect and govern abnormal AI costs. It then maps token consumption to customers, features, teams, agents, and business output to build an ROI system for the AI era.