Cloud gives teams speed—but without governance, it also creates silent waste. As environments grow across multiple accounts, regions, and services, costs can climb faster than usage. The biggest challenge isn’t cutting costs once; it’s building a repeatable operating model that prevents waste from returning while still supporting innovation.
A scaling organization running business-critical workloads in the cloud saw costs rise sharply over several quarters. Engineering teams were shipping features faster, but cloud spend became unpredictable and difficult to explain. Leadership needed spend control without slowing delivery.
Key challenges included:
Over-provisioned compute and storage
Many services were sized for peak demand but ran underutilized most of the time. Instances had excess CPU/memory headroom, and storage tiers were not optimized.On-demand heavy consumption
A large portion of compute ran on on-demand pricing even for steady workloads, resulting in missed savings opportunities.Idle and orphan resources
Unused volumes, snapshots, load balancers, IPs, and stale environments accumulated—especially in dev/test and sandbox accounts.Limited cost visibility and accountability
Cost allocation tags were inconsistent, and teams could not easily attribute spend to products, environments, or owners. Finance had limited ability to forecast cloud spend accurately.No FinOps rhythm
Optimization was ad-hoc and reactive. There were no recurring reviews, policy guardrails, or KPI tracking to keep spend under control over time.
The goal was to reduce spend materially while improving forecasting and building a sustainable FinOps practice.
Maayan Technologies executed a phased cloud optimization program that combined immediate savings levers with long-term governance.
1) FinOps Foundation: Visibility, Allocation, and Ownership
We established a FinOps baseline to make costs measurable and actionable:
Defined cost allocation model by product, environment, and team
Standardized tagging policies and coverage targets
Built dashboards for daily/weekly spend visibility, anomalies, and trends
Set accountability through showback/chargeback reporting
Established monthly FinOps reviews with engineering and finance
This created a shared operating language between finance and tech.
2) Rightsizing: Align Capacity to Real Utilization
We analyzed utilization and performance metrics across compute, databases, and container clusters, then executed rightsizing changes with controlled safeguards:
Compute rightsizing based on CPU/memory/IO patterns
Autoscaling tuning for variable workloads
Container resource request/limit optimization to reduce wasted cluster capacity
Database tier optimization where utilization was consistently low
Storage optimization through tiering, lifecycle policies, and deletion of stale snapshots
We implemented change windows and rollback plans to ensure reliability was maintained.
3) Reserved Capacity Strategy: Commit Where It’s Safe
After stabilizing baseline utilization, we introduced a reserved capacity approach for predictable workloads:
Identified steady-state services suitable for reservations (core APIs, always-on services, base cluster capacity, databases)
Modeled commitment levels to balance savings vs flexibility
Applied reserved instances / savings plans (depending on platform) based on usage patterns
Set renewal and coverage targets to prevent “commitment drift”
This unlocked significant savings on long-running workloads while keeping elasticity for spikes.
4) Waste Reduction and Policy Guardrails
We eliminated recurring waste by implementing:
Scheduled shutdown policies for non-prod environments
Idle resource detection and cleanup playbooks
Budget alerts and anomaly detection for unusual spikes
Standard “golden templates” for instance sizing and storage defaults
Governance workflows for new services (tagging, cost estimation, owner assignment)
These guardrails ensured savings sustained beyond the initial push.
5) KPI Tracking and Continuous Optimization
We operationalized cost optimization through measurable KPIs:
Spend by product/team/environment
Rightsizing savings and utilization improvement
Reserved capacity coverage and effective savings rate
Non-prod uptime reduction and idle cleanup impact
Forecast accuracy and month-end variance
Optimization became a recurring practice, not a one-time project.
The cloud cost program delivered both immediate and sustained value:
~25% reduction in cloud spend through rightsizing, waste removal, and reserved capacity adoption.
Improved cost visibility with tagging discipline and actionable dashboards for teams.
Better forecasting and financial control through FinOps rhythm and showback reporting.
Reduced non-production waste via scheduling, cleanup automation, and governance policies.
Higher operational maturity with repeatable optimization playbooks and KPI-driven accountability.
Sustained savings by preventing cost creep through guardrails and ongoing review cycles.
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