AWS Cloud Optimization
Mayan.Host reviews your AWS accounts, billing data, workload architecture, and operating practices, then implements a prioritized optimization plan across compute, storage, databases, networking, commitments, security, and automation. The goal is simple: keep the AWS capabilities that create value, remove the waste that does not, and move steady workloads to private cloud only when the economics and risk profile support it.
Best Fit
Optimization works best when the cloud bill is treated as an architecture signal, not just an accounting problem.
Budget alerts and manual instance checks are not enough when the real cost drivers are architectural.
AWS is valuable for managed services, global reach, burst capacity, and fast product delivery. It is not automatically the best place for every steady workload.
Optimization succeeds when engineering, finance, and operations can agree on the tradeoffs and then actually ship the changes.
Scope
Each recommendation is ranked by savings potential, engineering risk, reliability impact, security impact, and reversibility before anything changes in production.
We normalize the billing baseline so decisions are made from tagged, accountable, workload-level cost data.
We tune compute to actual utilization and workload behavior instead of static provisioning assumptions.
We separate durable baseline usage from variable demand before recommending Savings Plans or Reserved Instances.
Storage and network costs often hide behind small line items that compound across regions, accounts, and environments.
Managed services should reduce operational burden without quietly creating oversized, idle, or poorly configured spend.
Cost changes are paired with controls so optimization does not erode the production posture.
Method
The goal is not a one-time cleanup. It is a repeatable operating loop that keeps spend, performance, reliability, and ownership visible.
We start with billing, architecture, and operations, not a generic report.
Every recommendation gets an implementation path and a risk rating.
We make changes through reviewed infrastructure and release workflows.
Optimization becomes a monthly operating practice instead of an annual scramble.
Deliverables
Outcomes
Some workloads belong on AWS. Some steady-state workloads are better on managed private cloud. We help you compare both paths using cost, latency, managed-service dependency, compliance, reliability, and team capacity.
Start the Review
Use the form to request an AWS cost review. A Mayan.Host cloud engineer will review your workload profile, current AWS pain points, and the level of optimization or migration support you need.
FAQ
We implement. The engagement can start with an assessment, but the useful work is converting the backlog into IaC changes, sizing changes, lifecycle policies, commitment decisions, monitoring updates, and operational runbooks.
Production changes are planned in waves with rollback paths. Some actions, such as deleting idle resources or changing lifecycle rules, are low risk. Others, such as database resizing or cluster changes, need testing, scheduling, and monitoring.
No. Commitments make sense only for usage that is durable enough to justify the lock-in. We model coverage, utilization, workload roadmap, migration plans, and contract terms before recommending commitments.
Yes. We identify workloads where private cloud economics and operational control are stronger, then design the migration path, networking, observability, CI/CD, backup, and rollback plan.
The minimum useful starting point is read-only billing, Cost Explorer or cost export access, utilization metrics, architecture context, and workload ownership. Implementation access is scoped separately and can be handled through pull requests to your IaC repositories.
