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Cloud Cost Optimization Tips

Cloud Cost Optimization Tips

Cloud Cost Optimization Tips

Cloud Cost Optimization Tips

Cloud costs are often the second-largest expense after payroll for SaaS companies. Without active management, spending grows faster than revenue. This guide covers practical cost optimization strategies that reduce bills by 30-50% without sacrificing performance.

Right-Sizing Instances

The most common waste is over-provisioned resources. Use cloud provider tools to analyze utilization:

• AWS Compute Optimizer: Analyzes CPU, memory, and network utilization to recommend instance types.

• GCP Rightsizing Recommendations: Built into the Compute Engine console.

• Azure Advisor: Provides cost recommendations across all services.

Target utilization rules of thumb:

| Resource | Target Utilization |

|----------|-------------------|

| CPU | 40-70% average |

| Memory | 60-80% average |

| Disk IOPS | Below 80% of provisioned |

Downsize instances that consistently run below 20% utilization. For variable workloads, consider scaling horizontally rather than vertically.

Reserved Instances and Savings Plans

Commit to usage in exchange for discounts:

| Option | Discount | Commitment |

|--------|----------|------------|

| AWS Reserved Instances | 40-60% | 1 or 3 years |

| AWS Savings Plans | 40-60% | 1 or 3 years ($/hour) |

| GCP Committed Use | 40-57% | 1 or 3 years |

| Azure Reserved | 40-60% | 1 or 3 years |

Start with 1-year commitments for baseline workloads (30-50% of your total compute). Use 3-year commitments for stable, predictable workloads. Combine Savings Plans with Spot Instances for maximum flexibility.

Spot and Preemptible Instances

Use spot instances (AWS), preemptible VMs (GCP), or low-priority VMs (Azure) for fault-tolerant workloads:

AWS: Request spot instances in Auto Scaling

aws autoscaling create-auto-scaling-group \

\\--mixed-instances-policy file://spot-policy.json

GCP: Create preemptible VM

gcloud compute instances create worker \

\\--preemptible

Ideal workloads: batch processing, CI/CD runners, stateless web workers, data analytics, rendering.

Savings: 60-90% compared to on-demand pricing. Combine with Spot Instance interruption handling (checkpointing, graceful shutdown).

Storage Optimization

Storage costs accumulate silently. Audit your storage regularly:

• Delete unused volumes: Snapshots of deleted volumes and unattached EBS volumes.

• Use lifecycle policies: Move infrequently accessed data to colder tiers.

• Object storage tiers:

| Tier | Cost/GB/Month | Use Case |

|------|--------------|----------|

| S3 Standard | $0.023 | Active data |

| S3 Infrequent Access | $0.0125 | Accessed monthly |

| S3 Glacier | $0.0036 | Archived data |

| S3 Deep Archive | $0.001 | Regulatory retention |

Set S3 Lifecycle rules to transition objects automatically:

{

"Rules": [

{

"Id": "MoveToIA",

"Filter": {"Prefix": "logs/"},

"Status": "Enabled",

"Transitions": [

{"Days": 30, "StorageClass": "STANDARD_IA"},

{"Days": 90, "StorageClass": "GLACIER"}

],

"Expiration": {"Days": 365}

}

]

}

Network Egress Costs

Data transfer out of cloud providers is expensive. Minimize egress:

• Use the same region: Keep services that communicate frequently in the same region. Cross-region traffic is billed.

• CloudFront/CDN: Serve static assets through a CDN. CloudFront data transfer to the internet is cheaper than S3 direct access.

• Leverage direct connect: For large data transfers, use AWS Direct Connect or equivalent.

• NAT Gateway costs: Use NAT instances instead of NAT Gateway for high-volume traffic (cost savings of 70-80%).

Autoscaling

Scale resources to match demand:

AWS Auto Scaling with target tracking

autoscaling:

target_tracking:

\\- predefined_metric_specification:

predefined_metric_type: ASGAverageCPUUtilization

target_value: 60

For

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