Catalog
concept#Cloud#Reliability#Architecture#Observability

Rightsizing

Adjusting IT resources to actual demand to reduce cost and secure performance, especially in cloud environments.

Rightsizing is the practice of adjusting resources and capacity to actual workloads to optimize cost, performance and utilization.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

Prometheus / GrafanaCloud provider metrics (AWS, GCP, Azure)Infrastructure-as-Code (Terraform)

Principles & goals

Measure before changingIterative approach instead of one-off changesBalance between cost and reliability
Iterate
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Underprovisioning leads to SLA breaches
  • Misinterpreting historical data can produce wrong recommendations
  • Automated adjustments without review can cause side effects
  • Use 30-day metrics as decision basis
  • Combine automated recommendations with human review
  • Explicitly define safety and SLA buffers

I/O & resources

  • Monitoring data (Prometheus, cloud metrics)
  • Inventory of resources (instances, services)
  • Business requirements and SLAs
  • Concrete rightsizing recommendations
  • Implementation plan with prioritization
  • Metrics to track achieved savings

Description

Rightsizing is the practice of adjusting resources and capacity to actual workloads to optimize cost, performance and utilization. Especially in cloud environments (VMs, containers, managed services) rightsizing reduces overprovisioning and improves reliability. It relies on monitoring data, historical metrics and iterative adjustments.

✔

  • Reduced cloud costs by avoiding overprovisioning
  • Improved resource utilization and efficiency
  • Better capacity and budget planning

✖

  • Dependence on quality monitoring data
  • Short-term savings can impair long-term resilience
  • Not suitable for unpredictable load spikes

  • Average CPU utilization (30 days)

    Mean CPU usage over a representative period to assess utilization.

  • 95th percentile memory usage

    Helps detect peak consumption without being dominated by outliers.

  • Cost per workload / month

    Monetary metric to evaluate the saving effect of rightsizing measures.

E-commerce: right-sized web server fleet

An online shop reduced instance sizes outside sales peaks after traffic analysis and lowered costs while maintaining availability.

SaaS: multi-tenant databases

By monitoring tenants, DB instances were grouped by load class and provisioned accordingly, improving performance and cost.

Data pipeline: optimized batch windows

Batch clusters were scheduled and temporarily scaled up instead of permanently allocating large capacity.

1

Collect and validate monitoring data

2

Classify workloads by load profile

3

Create policies for max and min resources

4

Generate automated recommendations and review

5

Implement in stages and measure effects

⚠️ Technical debt & bottlenecks

  • Legacy monoliths without metric integration
  • Hard-coded resource limits in IaC
  • Insufficient test environments for scaling tests
CPU-boundMemory-boundI/O-bound
  • Downsizing all instances by one class without tests
  • Automatically removing buffers before peak tests
  • Neglecting memory or I/O needs in favor of CPU optimization
  • Skewed data due to short-term anomalies
  • Missing tagging leads to wrong mappings
  • Excessive automation without rollback plan
Knowledge of cloud resources and cost modelsAnalysis of monitoring and performance metricsAbility to assess risks and prioritize
Cost optimizationAvailability and SLA complianceMeasurability and observability
  • • SLA requirements with minimum capacity
  • • Granularity and latency of monitoring data
  • • Compliance and security constraints