AWS Cost Anomaly Detection | Overiview.

An Overview of AWS Cost Anomaly Detection.

Focus:

•        Tailored for DevOps / DevSecOps / Cloud / FinOps Engineers.

Breakdown:

•        Intro,
•        Key Features,
•        The Concept: Cost Anomaly Detection,
•        How the Detection Model Works,
•        Monitor Types (Critical to Get Right),
•        Alerting Mechanics,
•        Root Cause Breakdown (This Is Where Value Is),
•        Sensitivity Tuning (Avoid Alert Fatigue),
•        What Cost Anomaly Detection Is Good At,
•        What It Is NOT Good At,
•        Cost Anomaly Detection vs Other Tools,
•        Production-Grade Setup (Recommended),
•        Advanced Tips,
•        Final thoughts.

Intro:

•        AWS Cost Anomaly Detection is a free, machine learning-driven feature of the AWS Cost Management suite that automatically identifies and alerts users to unusual spikes or deviations in their AWS spending patterns.
•        AWS Cost Anomaly Detection helps prevent unexpected billing surprises by providing proactive notifications and root cause analysis for identified anomalies.

Key Features

Machine Learning (ML) Models:

•          The service uses ML to analyze historical data, calculate an expected daily spend baseline, and identify when actual spending exceeds the normal limits.

Monitors: 

•         Users can create monitors to track costs across various dimensions, including specific AWS services, linked accounts, cost allocation tags (e.g., team), or cost categories.
•         AWS managed monitors can automatically adapt to organizational growth without manual reconfiguration.

Alerting Thresholds:

•          Users can set alert preferences using both fixed-dollar amounts and/or percentage-based thresholds to determine when notifications are sent.

Notifications:

•          Alerts can be sent via email or Amazon Simple Notification Service (Amazon SNS) topics, which can then be integrated with chat applications like Slack or Amazon Chime.

Root Cause Analysis:

•          When an anomaly is detected, the service provides a detailed root cause analysis, breaking down the impact by dimensions such as AWS service, account, region, or usage type to help quickly pinpoint the source of the cost increase.

Integration:

•          It is integrated with AWS Cost Explorer for detailed visualization and analysis and with Amazon EventBridge for creating automated reactions to events.

Getting Started

•        To use AWS Cost Anomaly Detection, twtech must first enable AWS Cost Explorer, as the service relies on its data.
•        For new Cost Explorer users (enabled on or after March 27, 2023), a default anomaly detection configuration is automatically enabled. 
•        twtech can configure and manage the service through the AWS Cost Management console. 

1.     Navigate to the Cost Anomaly Detection section in the console.
2.     Create a monitor to define what twtech wants to track (e.g., all linked accounts or a specific tag).
3.     Configure alert subscriptions with twtech preferred notification frequency and thresholds. 
After setup, the service begins monitoring within 24 hours as it builds a baseline from historical data

1. The Concept: Cost Anomaly Detection

• AWS Cost Anomaly Detection (CAD) uses machine learning to detect unusual cost patterns compared to twtech historical spend baseline.

Key point:

• It detects unexpected cost changes, not necessarily “high cost.”

Example:

•         $10 → $40 overnight = anomaly
•         $10,000 → $10,200 = probably not an anomaly

2. How the Detection Model Works

AWS builds a dynamic baseline using:

•         Historical cost trends
•         Day-of-week patterns
•         Seasonality
•         Recent behavior weighting

Then it looks for:

•         Sudden cost spikes
•         Persistent upward drift
•         Unusual service/account/region behavior

Important:

•         Models are independent per monitor
•         No fixed thresholds (unlike Budgets)

3. Monitor Types (Critical to Get Right)

A. AWS Services Monitor (Recommended Start)

Scope:

•         All AWS services
•         All linked accounts
•         All regions

Best for:

•         Org-wide anomaly visibility
•         New FinOps programs

Limitation:

•         Alerts can be noisy without filtering

B. Linked Account Monitor

Scope:

•         One or more AWS accounts

Best for:

•         Multi-account orgs
•         Platform vs product account separation
•         Chargeback / showback

C. Cost Category Monitor (Most Powerful)

Scope:

•         Based on Cost Categories

Examples:

•         Environment = Prod
•         Team = Payments
•         Workload = Data Platform

Best for:

•         Ownership-based alerts
•         Reducing alert fatigue
•         Mature FinOps teams

 Best practice:

• Create Cost Categories first, then build monitors on top.

4. Alerting Mechanics

Alert Triggers

Alerts fire when:

•         Actual cost exceeds expected cost by more than your configured threshold

twtech configure:

•         Absolute threshold (e.g., $100)
•         Percentage threshold (e.g., 30%)

AWS evaluates:

•         Daily (not real-time)
•        With ~24-hour delay

Alert Destinations

Supported:

•         Email
•         SNS (for Slack, PagerDuty, Opsgenie, Lambda, Jira)

 DevOps tip:
Route alerts to:

•         Slack for awareness
•         PagerDuty only for large anomalies

5. Root Cause Breakdown (This Is Where Value Is)

When an anomaly is detected, AWS automatically analyzes:

•        Service
•        Linked account
•         Region
•         Usage type
•         Operation

Example:

EC2 → us-east-2 → DataTransfer-Out-Bytes → +$1,200

• This saves hours of manual Cost Explorer digging.

6. Sensitivity Tuning (Avoid Alert Fatigue)

Too Sensitive?

Symptoms:

•         Alerts every day
•         Small dollar amounts

Fix:

•         Increase absolute threshold
•         Add cost category filters
•         Split monitors by environment

Not Sensitive Enough?

Symptoms:

•         twtech finds spikes manually
•         Alerts arrive too late

Fix:

•         Lower percentage threshold
•         Create service-specific monitors
•         Separate prod vs non-prod

7. What Cost Anomaly Detection Is Good At

✅    Sudden misconfigurations

•         NAT Gateway left open
•         EC2 instance type changes
•         Accidental region usage

✅    Security-related cost events

•         Crypto mining
•         Data exfiltration
•         Compromised credentials

✅     Slow-burning cost leaks

•         Gradual scaling drift
•         Forgotten test workloads

8. What It Is NOT Good At

❌     Planned increases

•         Launches
•         Migrations
•         Traffic campaigns

❌     Usage-based but expected growth

•         Seasonal scaling
•         Auto Scaling behaving correctly

❌     Real-time detection

•         It’s daily, not instant

NB:

 Always pair with engineering context.

9. Cost Anomaly Detection vs Other Tools

Tool	Purpose	Best Use
Cost Anomaly Detection	Detect unexpected spend	Incident-style response
Budgets	Enforce limits	Governance
Cost Explorer Forecast	Predict growth	Planning
CUR + Athena	Deep analysis	FinOps analytics

10. Production-Grade Setup (Recommended)

Step 1: Create Cost Categories

•         Team
•         Environment
•         Product

Step 2: Create Monitors

•         Org-wide services monitor
•         Per-prod cost category monitor
•         High-risk service monitors (EC2, NAT, Data Transfer)

Step 3: Alert Routing

•         Low severity → Slack
•         High severity → PagerDuty

Step 4: Runbooks

For each alert:

•         Owner
•         Expected causes
•         Investigation steps
•        Rollback options

11. Advanced Tips

 Combine with CloudWatch

Cost spike + resource metric spike = confidence

 Security Signal

Treat unexplained cost anomalies as security events until proven otherwise.

 Continuous Improvement

•         Review false positives monthly
•         Adjust thresholds
•         Refactor monitors as architecture evolves

12. Final thoughts

•         ML-based detection of unexpected cost behavior
•         Best used with Cost Categories
•         Not real-time, but highly effective
•         Critical for catching:
•    Misconfigurations
•    Security breaches
•    Cost leaks
•         Works best as part of a FinOps operating model

AWS Cost Anomaly Detection Architecture

AWS Anomaly Detection

AWS Cost Anomaly Detection Suite (Tool) and integration.