An AWS cost review should produce decisions that a workload owner can act on. A lower bill is useful only if the application still meets its recovery, performance, and availability requirements. Start with those requirements, then decide which usage to remove, which architecture to change, and which remaining usage to discount.
This guide applies the Cost Optimization design principles to one workload at a time. For the broader operating process, start with cloud cost management. For the framework's other concerns, see the AWS Well-Architected pillars.
Turn the five design principles into review questions
The AWS Cost Optimization design principles give the review its structure:
- Implement cloud financial management: who can approve a cost change, and who checks its outcome?
- Adopt a consumption model: which resources need to run continuously, and which can follow demand?
- Measure overall efficiency: what does it cost to deliver a useful business unit, such as a completed order?
- Stop spending money on undifferentiated heavy lifting: would a managed service remove maintenance work at an acceptable total cost?
- Analyze and attribute expenditure: can each material charge be assigned to a workload or a shared-service owner?
Do not treat a managed service as automatically cheaper. Compare its service charges, licensing, operational work, recovery needs, and integration constraints with the existing option. Cost can break a tie between designs that meet the same requirements; it cannot make an unsuitable design acceptable.
1. Practice cloud financial management
Bring finance and engineering to the same review. Finance defines the reporting period and treatment of credits, taxes, and commitments. Engineering explains the workload, demand pattern, and operating constraints. Name one person to maintain the improvement backlog and one owner for each proposed change.
Collect a representative billing period, including a known peak if possible. Record the cost basis, account scope, regions, business output, and known exceptions. An annual commitment paid upfront can distort a cash-only monthly comparison, so agree how commitment costs will be allocated over the term.
Require a cost estimate when a new environment or architecture is approved. Give experiments an owner and expiry review. Review new AWS service options and software licence terms when they could materially change the workload's economics, rather than replacing components merely because a new service exists.
2. Establish expenditure and usage awareness
Use account boundaries and resource metadata together. Accounts can separate environments or business units; tags describe applications, owners, and cost centres within them. AWS cost allocation tags must be activated for billing analysis. Applying a tag alone does not make it available in cost reports.
Keep an explicit unallocated category. Shared networking, support, and platform services need a documented allocation rule, such as measured traffic or an agreed fixed split. Show that rule beside the report so an apparent saving is not just a cost moved to another team.
Compare service and account trends in AWS Cost Explorer, then investigate the resources behind a material change. Billing data can lag usage. Pair cost reports with workload telemetry when a runaway job or unexpected scaling event needs immediate investigation.
Set budget notifications and route them to someone who can act. An AWS Budgets alert can arrive after additional charges accrue; it is not an immediate spending cap. Access policies, service quotas, and reviewed automation are separate controls. Do not stop production resources automatically just because a cost threshold is crossed.
3. Select cost-effective resources
For each large cost driver, collect evidence before changing resource size or count. Check memory, CPU, throughput, latency, storage growth, and peak demand. Low average CPU alone does not prove a database can be downsized. Validate a candidate change against the same performance and recovery requirements used for the baseline.
- Resource lifecycle: find abandoned environments, unattached storage, and obsolete backups. Confirm ownership, retention requirements, and dependencies before deletion.
- Service choice: compare managed and self-managed options, including licences and the engineering work required to patch, back up, and recover them.
- Pricing: remove waste and establish a steady usage baseline before buying commitments. Use the AWS pricing-model decision guide for On-Demand, Savings Plans, Reserved Instances, and Spot.
- Location and data movement: map region, Availability Zone, internet, and cross-provider traffic. Check applicable transfer, processing, and request charges for the actual path. The hidden cloud cost checklist helps identify costs beyond compute.
Before buying a commitment, check existing organisation-wide coverage and sharing settings so a local purchase does not duplicate an existing discount. A lower advertised rate can still increase total spend if committed capacity goes unused.
4. Match supply to workload demand
Separate predictable baseline traffic from bursts and interruptible background work. Use scheduling for non-production environments that can be stopped safely, scaling for variable demand, and queues or throttling where the application can tolerate delayed work. Each choice needs a limit and a way to recover when demand exceeds the plan.
For example, a hypothetical development environment used for 10 hours on each of five weekdays needs 50 running hours instead of 168 in a full week. That removes 118 running hours, about 70%, from the schedulable compute component. It is not a forecast of total-bill savings: storage, backups, commitments, and resources left running still cost money. Check startup tasks and overnight jobs before applying a schedule.
5. Optimize over time with an evidence backlog
Use one entry per proposed change. Record the resource or workload, current cost basis, owner, expected benefit, implementation effort, service risk, validation check, and rollback. Rank changes by net value and delivery risk, not by the largest percentage on a recommendation screen.
An entry might read: “Development application A has no scheduled users overnight. Owner confirms batch jobs are separate. Test an automatic stop/start schedule, check morning readiness, and compare running hours over matching working weeks. Remove the schedule if morning readiness fails.” “Reduce EC2 spend” names no owner, test, or rollback.
After a change, compare cost and business output over equivalent periods. A bill that rises with customer demand may still show better unit economics. Revisit the backlog after releases, usage changes, and commitment renewals. If the review exposes a move between platforms, include these assumptions in the AWS migration assessment and cutover plan.