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 containerized workloads, use Kubernetes Cluster Autoscaler:

# Karpenter for AWS EKS

kubectl scale deployment api-server --replicas=0  # Automated idle scaling

Karpenter and similar tools scale nodes based on actual pod resource requests, eliminating node-level waste.

Database Cost Optimization

Databases are often the most expensive service:

**Serverless databases**: Use Aurora Serverless, Cloud SQL auto-scaling, or Azure SQL Serverless for variable workloads.

**Read replicas**: Add replicas for read-heavy workloads instead of upscaling a single instance.

**Connection pooling**: Use PgBouncer or RDS Proxy to handle thousands of connections without provisioning for peak.

**Delete old data**: Archive historical data to object storage.

Reserved Capacity with Spot

Combine reserved capacity for baseline with spot for spikes:

# Terraform: Mix OD and Spot in ASG

resource "aws_autoscaling_group" "app" {

  mixed_instances_policy {

    launch_template {

      launch_template_specification { ... }

      override {

        instance_type = "t3.medium"

      }

    }

    instances_distribution {

      on_demand_base_capacity = 2

      on_demand_percentage_above_base_capacity = 50  # Rest from Spot

      spot_allocation_strategy = "capacity-optimized"

    }

  }

}

Monitoring and Budgets

Set up cost monitoring to catch anomalies early:

# AWS Budget with action

aws budgets create-budget \

  --budget-name "Monthly-Infra" \

  --budget-file monthly-budget.json \

  --notifications-with-subscribers file://alert-config.json



# GCP budget alert

gcloud billing budgets create \

  --billing-account=XXXXXX \

  --display-name="Monthly Budget" \

  --budget-amount=5000USD \

  --threshold-rules=percent=50,percent=90 \

  --notifications-pubsub-topic=budget-alerts

Set up alerts at 50%, 80%, and 90% of budget. Tag resources by cost center and review spending weekly.

Summary

Cloud cost optimization is an ongoing process, not a one-time cleanup. Start with right-sizing (the quickest wins), add reserved capacity for baseline workloads, use spot instances for fault-tolerant work, and configure autoscaling to match demand. Monitor storage lifecycle, minimize data egress, and optimize database tiering. The most effective approach is a 20% time investment per quarter: review spending, tag resources, and implement the top three savings opportunities. Most organizations can reduce their cloud bill by 30-50% within three months by following these practices.