Interactive checklist of cost optimization best practices across all three providers.
Analyze utilization metrics and downsize over-provisioned instances to match actual workload requirements.
Leverage discounted spot or preemptible capacity for batch processing, CI/CD, and other interruptible workloads.
Commit to 1-year or 3-year terms for predictable workloads to receive significant discounts over on-demand pricing.
Configure auto-scaling groups, VM scale sets, or managed instance groups to dynamically adjust capacity with demand.
Stop dev, test, and staging environments during off-hours, weekends, and holidays to eliminate idle compute costs.
Migrate compatible workloads to ARM-based processors for better price-performance ratios.
Automatically transition aging data to cheaper storage tiers and expire objects that are no longer needed.
Find and remove orphaned EBS volumes, Azure Disks, or Persistent Disks that are no longer attached to instances.
Reduce the data footprint through compression and deduplication to lower storage and transfer costs.
Match storage class or tier to actual access patterns. Frequently accessed data uses standard tier; infrequent data uses lower tiers.
Delete unnecessary EBS snapshots, VM disk snapshots, or persistent disk snapshots that are no longer needed for recovery.
Co-locate resources in the same region and use CDNs to reduce expensive cross-region and internet egress charges.
Route traffic to cloud services through private endpoints instead of NAT gateways to avoid data processing charges.
Increase cache hit ratios by tuning TTLs, cache keys, and content organization to reduce origin fetches.
Consolidate or eliminate unnecessary NAT gateways to reduce hourly and data processing charges.
Analyze database CPU, memory, and I/O utilization to match instance type to actual workload requirements.
Switch to serverless or on-demand database pricing models for workloads with unpredictable or bursty access patterns.
Offload read traffic to read replicas and consider placing them in cheaper regions for cost savings.
Reduce unnecessary backup storage by adjusting retention periods to match actual recovery requirements.
Profile and optimize memory allocation for serverless functions to find the most cost-effective configuration.
Use provisioned concurrency judiciously for latency-sensitive functions while avoiding over-provisioning.
Consolidate unused APIs and remove orphaned endpoints to eliminate unnecessary charges.
Apply consistent tags or labels to all resources to enable accurate cost allocation by team, project, and environment.
Configure budget thresholds and alerts to proactively detect and prevent unexpected cost overruns.
Establish weekly or monthly cost review meetings with stakeholders to track spending trends and identify optimization opportunities.
Enable automated anomaly detection to catch unexpected spending spikes before they become significant budget impacts.
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}The Multi-Cloud Cost Optimization Checklist provides an interactive, categorized checklist of cost optimization best practices across AWS, Azure, and GCP. It covers compute rightsizing, reserved instances, storage tiering, networking costs, idle resource cleanup, and more. Each item includes a description and links to relevant provider tools. You can track your progress through the checklist to ensure your cloud environments follow cost optimization best practices.
Disclaimer: This tool runs entirely in your browser. No data is sent to our servers. Always verify outputs before using them in production. AWS, Azure, and GCP are trademarks of their respective owners.