🔷 Introduction

Cloud cost is one of the biggest challenges in large-scale platforms:

• Kubernetes clusters running 24×7

• Multi-tenant workloads

• Batch simulations

• Airflow jobs

• SDV/Digital Twin environments

• CI/CD pipelines

• High-memory or GPU workloads

• Growing storage and logs

Companies often spend 30–60% more than necessary due to poor visibility and lack of structured cost governance.

This guide explains how to optimize cloud, Kubernetes, and SDV workloads using proven architectures, FinOps practices, and real-world implementation patterns used by top enterprises.

🔷 1. Why Cloud Costs Spiral Out of Control

❌ Over-provisioned workloads

Developers request:

• 4 CPU

• 8GB RAM
  When they only use:

• 500m CPU

• 1GB RAM

❌ Unused resources

• Idle pods

• Orphaned volumes

• Unused load balancers

• Old EBS/PVC

❌ CI/CD running unnecessary jobs

Dozens of pipelines triggered by every commit.

❌ Wrong storage tiers

Premium SSD vs Standard HDD where not needed.

❌ Logs consuming 2TB+

Raw logs stored with no TTL policies.

❌ GPU nodes always running

Even when no one uses them.

This is where cost optimization becomes vital.

🔷 2. Reference Cost-Optimized Cloud Architecture

A real-world, cost-aware architecture:

Every layer is optimized for resource efficiency.

🔷 3. Step-by-Step Implementation Guide

⭐ STEP 1 — Enable Kubernetes Autoscaling the Right Way

Use:

• HPA (Horizontal Pod Autoscaler)

• VPA (Vertical Pod Autoscaler)

• CA (Cluster Autoscaler)

Example:

Best Practices:

• Avoid static pod counts

• Set requests based on actual usage

• Allow autoscaler to scale app + nodes

⭐ STEP 2 — Implement Spot Nodes for Non-Critical Workloads

Spot nodes can reduce compute cost by 70–90%.

Use spot nodepools for:

• Simulations

• Batch jobs

• CI runners

• Non-critical APIs

Best Architecture:

⭐ STEP 3 — Right-Size Pods Using Metrics

Find actual usage via:

• Kube-state-metrics

• Prometheus

• Metrics-server

• vpa-recommender

Example:

If actual usage =
CPU: 120m
Memory: 200Mi

Set requests:
CPU: 150m
Memory: 256Mi

Avoid:

• 500m / 1Gi

• 1 CPU / 2Gi

This alone saves thousands of dollars monthly.

⭐ STEP 4 — Implement TTL Policies for PVC, Logs & Artifacts

Storage TTL:

• Delete unused PVC > 30 days

• Auto-delete completed-job PVs

• Cleanup old simulation logs

Container registry TTL:

Log TTL:

• Information logs: 7–15 days

• Error logs: 30 days

• Simulation logs: 14 days

⭐ STEP 5 — Use “Scale-to-Zero” for GPU & High-Compute Nodes

GPU nodes cost extremely high.

Implement:

• KEDA

• Event-driven architecture

• Nodepool autoscaling

A nodepool with:

Only charges when workloads exist.

⭐ STEP 6 — Use FinOps Dashboards for Visibility

Dashboards via:

• Grafana

• Azure Cost Management

• AWS Cost Explorer

• KubeCost

• Prometheus-exporters

Track:

• Cost per namespace

• Cost per workload

• Idle CPU

• Cost per tenant/team

• Storage cost

• Egress cost

FinOps makes cost visible to developers — not just DevOps.

⭐ STEP 7 — Optimize CI/CD Pipelines

CI/CD often consumes 35–45% of cloud compute.

Optimize by:

• Parallelizing only where necessary

• Cancelling old pipeline runs

• Caching dependencies

• Scaling CI runners on spot VMs

• Reusing artifacts

• Reducing pipeline triggers

Example Optimization:

Cancel previous runs if new commit arrives:

⭐ STEP 8 — Use Multi-Tenant Cost Allocation

For SDV / DevOps platforms:

• Each team gets its namespace

• Each namespace mapped to cost center

• Quotas control overuse

Example quotas:

⭐ STEP 9 — Implement Pod Disruption Budgets (PDBs)

PDB allows APIs to run on spot nodes without downtime:

⭐ STEP 10 — Optimize Network & Load Balancer Costs

LB Best Practices:

• Use internal LBs

• Use ingress controllers

• Minimize standalone LBs

• Use Azure Private Link

Egress optimization:

• Use VNET integration

• Use NAT gateways

• Optimize cross-zone traffic

🔷 4. Real-World Cost Optimization Scenario

Scenario: SDV Simulation Cluster Costs Too High

Symptoms:

• GPU nodes running idle

• Hundreds of PVCs unused

• Simulation results stored forever

• Pipeline runs unnecessary jobs

Fixes:

1. GPU nodepool → scale-to-zero

2. Storage TTL → delete > 14 days

3. Separate spot nodepool for simulations

4. Add cancellation logic in CI

5. Use vpa-recommender for right-sizing

Savings: ~45% monthly.

🔷 5. Cost Optimization Best Practices

Kubernetes

✔ Always use autoscaling
✔ Right-size everything
✔ No static replicas
✔ Delete orphaned resources

Storage

✔ Move logs to cheaper storage
✔ Enable TTL policies
✔ Compress simulation logs

CI/CD

✔ Limit triggers
✔ Use spot runners
✔ Cache everything

Governance

✔ Monthly cost review
✔ Dashboards per team
✔ Alerts for spikes

🔷 6. Common Anti-Patterns

❌ Using on-demand nodes everywhere
❌ Keeping 1000s of logs forever
❌ High CPU/memory requests
❌ No namespace-level budgeting
❌ Using GPUs for small tasks
❌ No cluster autoscaling

Fix these and cost automatically drops.

🔷 Conclusion

Cost optimization is not a one-time task — it is a continuous engineering discipline.
With the right architecture:

• Kubernetes becomes efficient

• CI/CD cost drops dramatically

• SDV simulations become predictable

• Cloud bills stabilize

• Engineering productivity increases

A mature cost strategy transforms cloud from a liability into a powerful enabler.