Catch the runaway job on day one,
not on the invoice
Every ML team has a version of the story: a stuck training job, a demo endpoint that ran for six weeks, a retry loop hammering an LLM API. MLCostIntel watches spend at the workload level and flags the anomaly within a day, with enough context that you know what to shut off.
Why ML cost spikes slip through
The invoice is weeks late
By the time month-end billing lands, a runaway workload has been burning for 20+ days. The feedback loop that catches it must run on daily usage data, not invoices.
Service-level alerts cry wolf
ML spend is naturally bursty — a big training week is normal. Generic anomaly detection either alerts on every burst or gets tuned so loose it misses real runaways.
Alerts without context don't get fixed
An alert that says "SageMaker spend up 40%" kicks off a half-day investigation to find the cause. An alert that names the endpoint, the team that owns it, and how long it's been running at peak capacity gets handled before lunch.
Token spend has no natural ceiling
A prompt change that doubles context length, or a client bug that retries every request, shows up in your LLM bill immediately. The provider dashboards will happily record it, but nothing in them is going to call you about it.
How MLCostIntel catches anomalies
Because every dollar is already attributed to a workload, team, and model, anomaly detection runs where it's meaningful — on the workload's own history, not the service total.
- ✓ Workload-level baselines — each endpoint, training pipeline, and model has its own historical spend profile
- ✓ Daily detection — anomalies surface within a day of appearing in usage data, weeks ahead of the invoice
- ✓ ML-aware context — alerts name the resource, owner team, and deviation, so triage starts with the answer
- ✓ Token anomaly detection — per-model, per-application LLM usage monitored across Bedrock, OpenAI, and Anthropic
- ✓ Idle-after-active detection — endpoints and instances that keep billing after their workload went quiet
- ✓ Custom thresholds (Scale tier) — hard limits per job, per team, or per model that trigger immediate alerts
What teams get out of it
Detection window
Anomalies surface with daily refresh instead of month-end billing
Earlier than the invoice
Typical head start on a runaway workload versus waiting for billing
False alarms
Workload-level baselines separate planned bursts from real runaways
Owner on every alert
Resource, team, and deviation included — triage starts with the answer