Anomaly detection
Catch cost spikes in hours, not at month-end.
A misconfigured autoscaler or runaway AI job can burn six figures over a weekend. CarnotCloud learns the normal rhythm of every account, service and team, flags anything unusual within hours — and tells the right owner what caused it.
- Actual cost
- Expected range (ML baseline)
Root cause found in 4 minutes
Auto Scaling group checkout-api min capacity changed 12 → 48 by deploy #8812. Owner: @payments-platform notified in Slack.
<4 hrs
median time from spike to alert, with hourly granularity
90%
fewer false positives than static threshold alerts
Root cause
resource, change and owner attached to every alert
Capabilities
What you get with Anomaly Detection
Seasonality-aware ML
Models learn daily, weekly and monthly patterns per service, account and team — so a Monday spike isn’t an emergency.
Automatic root cause
Drill from a spike to the account, service, resource, usage type and deployment that caused it, automatically.
Alerts where you work
Slack, Microsoft Teams, email, PagerDuty and webhooks — routed to the owning team, not a shared inbox.
Tunable sensitivity
Set thresholds in dollars or percent by scope, so critical environments get tighter monitoring.
Triage workflow
Acknowledge, assign, comment and resolve anomalies with a full audit trail and time-to-resolution reporting.
AI & SaaS coverage
The same detection for LLM APIs, GPUs, Snowflake, Databricks and Datadog — the line items that spike fastest.
Signal, not noise
Alerts your engineers won’t mute
Static budget alerts fire too late or too often. CarnotCloud’s models adapt to each workload’s natural rhythm and surface only the deviations that matter, ranked by dollar impact.
- Hourly detection on supported providers
- Dollar-impact ranking and projected monthly cost
- Grouping of related anomalies into one incident
- Feedback loop that improves accuracy over time
- Actual cost
- Expected range (ML baseline)
Root cause found in 4 minutes
Auto Scaling group checkout-api min capacity changed 12 → 48 by deploy #8812. Owner: @payments-platform notified in Slack.
From alert to resolution
Every anomaly arrives with an owner and an answer
CarnotCloud uses your allocation model to route each alert to the team that owns the spend — with the root cause and Carnot AI’s plain-English explanation attached.
- Ownership-based routing via your business hierarchy
- Plain-English explanations from Carnot AI
- Linked deploys and configuration changes
- Time-to-detect and time-to-resolve reporting
Why did our AWS bill jump last week, and who owns it?
Spend rose $48.2K (+11.4%) week over week. Three drivers explain 92% of it:
- EC2 in prod-us-east-1 +$27.1K — ASG
checkout-apimin capacity raised in deploy #8812. Owner: Payments Platform. - NAT Gateway data transfer +$12.6K — new cross-AZ traffic from
search-indexer. - Bedrock +$4.7K — doc-summarizer moved to a larger model on Tuesday.
Create 3 Jira tickets Share to #finops Open report
$186K spike stopped in 3 hours
“CarnotCloud flagged a runaway data pipeline at 7 a.m. on a Saturday, with the root cause and the owner already attached. That single alert paid for the platform for the year.”
FAQ
Anomaly Detection: common questions
How quickly are anomalies detected?
Can we control who gets alerted?
Does it work for AI and SaaS spend?
Keep exploring
Works even better together
AI & GPU Cost Management
Track OpenAI, Anthropic, Bedrock, Vertex AI and GPU spend by model, feature, team and customer.
Learn moreWorkflows & Governance
Tagging policies, guardrails, approval-gated remediation, Jira/ServiceNow/Slack workflows and cost estimates in every pull request.
Learn moreCarnot AI
Ask your cloud bill anything in plain English.
Learn moreSee Anomaly Detection on your own data.
Book a 30-minute demo tailored to your stack, or start with a free savings assessment — read-only, no agents, no obligation.
- Read-only, agentless setup
- First insights in 15 minutes
- SOC 2 Type II & ISO 27001