Client

A SaaS company running high-traffic applications across AWS/GCP, facing rising cloud bills and inefficient resource usage.

Monthly cloud spending had grown beyond expectation due to:

• Over-provisioned instances

• Idle resources

• Unoptimized storage tiers

• Poor autoscaling configuration

• No visibility into service-level costs

The goal was to reduce cloud costs without reducing performance.

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Project Overview

We executed a detailed FinOps-driven cloud cost optimization initiative involving:

• Rightsizing virtual machines / compute instances

• Optimizing storage tiers and lifecycle policies

• Configuring efficient autoscaling

• Using AWS Cost Explorer / GCP FinOps dashboards

• Eliminating unused and idle resources

• Improving observability and cost tracking

The focus:
Cut cost, maintain performance, improve efficiency.

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Key Challenges

1. Over-Provisioned Compute Instances

Many services used:

• Large EC2 / GCE instance types

• Under 20–30% CPU usage

• No autoscaling

2. Unoptimized Storage

• Large S3 / GCS buckets with mixed cold/hot data

• EBS disks unused or attached to terminated VMs

• Logs consuming expensive storage

3. Missing Autoscaling & Scheduling

Workloads were running 24/7 even when traffic dropped drastically at night/weekends.

4. Lack of Visibility

Team had no clear breakdown of:

• Application-level cost

• Team-wise cost

• Historical cost trends

• Idle resource wastage

Our Solution

1. Rightsizing Compute Instances (AWS EC2 / GCP GCE)

We analyzed usage via:

• AWS Cost Explorer

• AWS Compute Optimizer

• GCP Recommender

• CloudWatch & Stackdriver metrics

Based on CPU, RAM, and I/O usage, we:

• Reduced instance sizes

• Switched from general-purpose to compute/memory optimized

• Migrated to ARM-based Graviton2/3 (AWS) or Tau T2D (GCP) where applicable

• Consolidated workloads on fewer but appropriately sized machines

Result:
30–40% savings on compute without any performance drop.

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2. Autoscaling Enhancements

We implemented/optimized:

• Horizontal Pod Autoscaling (Kubernetes)

• EC2 Auto Scaling Groups

• Instance cooldown periods

• Predictive scaling rules

• Scheduled scaling (reducing nodes at night)

This ensured:

• Scale up during peak

• Scale down during low-traffic hours

Result:
Over 25% savings on variable workloads.

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3. Storage Optimization

We optimized all major storage services:

AWS

• Moved large S3 data to Glacier & Infrequent Access

• Enabled lifecycle rules

• Deleted unused EBS volumes

• Converted gp2 → gp3 for cost reduction

GCP

• Migrated data to Nearline/Coldline

• Cleaned old logs

• Reduced SSD dependency where not needed

Result:
20–35% reduction in storage spending.

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4. Eliminating Unused Resources

We removed or decommissioned:

• Old snapshots

• Orphaned IPs

• Idle VMs

• Unused load balancers

• Legacy autoscaling configs

• Dead Kubernetes namespaces

This provided immediate, no-risk cost savings.

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5. Using FinOps Tools (AWS Cost Explorer / GCP Cost Management)

We built dashboards to track:

• Per-service cost

• Environment-wise cost

• Month-over-month trends

• Wastage metrics

• Team-level spending

Configured:

• Alerts for unusual usage spikes

• Forecasting for budgeting

• Reports for management

Provided full visibility into where cloud money goes.

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6. Introducing Cost Governance & Best Practices

We established:

• Tagging standards

• CI/CD cost checks

• Developer training

• Rightsizing policy

• Scheduled reviews

Created a culture of cost awareness, not just technical fixes.

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Architecture Diagram (Text Version)

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Results & Impact

💰 Total Cloud Cost Saving: 35–55%

Achieved without compromising reliability or performance.

⚡ Optimized Performance

Better autoscaling → more stable workloads under load.

🧠 Improved Visibility

Teams now understand where cost originates.

🧹 Zero Wastage

Unused instances, volumes, and IPs were fully cleaned up.

🧱 Future-Proof Cost Governance

Clear policies ensure costs stay low over time.

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Conclusion

Through a structured cloud cost optimization strategy including rightsizing, autoscaling, storage tuning, and FinOps tooling — we enabled the client to cut costs dramatically without sacrificing performance.

The platform is now:

• More efficient

• More predictable

• Easier to scale

• Much cheaper to operate

A long-term FinOps foundation is now in place for continued savings.