Cloud Costs Grow Faster Than Expected

Cloud computing enables rapid scaling, but without active cost management, cloud bills grow quickly. Common patterns include over-provisioned instances, unused resources, unoptimized data transfer, and missing cost allocation tags. At Nexis Limited, we manage cloud infrastructure for our SaaS products and client workloads, and cost optimization is a continuous practice.

Right-Sizing Instances

The most common source of cloud waste is over-provisioned instances. Applications running on instances with 8 cores and 32 GB RAM that actually use 1 core and 4 GB RAM are wasting 75% of their compute budget. Right-sizing workflow:

  • Monitor CPU, memory, and I/O utilization over 2-4 weeks.
  • Identify instances with consistently low utilization (under 40% average).
  • Resize to the smallest instance type that meets peak demand with 20% headroom.
  • Repeat quarterly as workload patterns change.

Reserved Instances and Savings Plans

For stable, predictable workloads, reserved instances (RIs) or savings plans offer 30-60% savings compared to on-demand pricing. Best practices:

  • Commit only to the baseline — the minimum capacity you always need.
  • Use on-demand or spot instances for variable workloads above the baseline.
  • Prefer flexible reservations (Convertible RIs, Compute Savings Plans) over rigid ones.
  • Review and adjust commitments annually based on actual usage.

Spot Instances for Fault-Tolerant Workloads

Spot instances (AWS) and preemptible VMs (GCP) offer 60-90% savings but can be terminated with short notice. Use them for:

  • CI/CD build agents — builds restart automatically on new instances.
  • Batch processing jobs — checkpoint progress and resume on new instances.
  • Development and testing environments — non-critical workloads.
  • Kubernetes node pools — mixed on-demand and spot instances for resilience.

Storage Optimization

  • Lifecycle policies: Automatically transition infrequently accessed data to cheaper storage tiers (S3 Infrequent Access, Glacier, GCS Nearline/Coldline).
  • Snapshot cleanup: Delete old EBS snapshots and disk images that are no longer needed.
  • Data compression: Compress stored objects and database backups.
  • Deduplication: Avoid storing multiple copies of the same data.

Architectural Cost Optimization

  • Serverless for variable workloads: Lambda/Cloud Functions scale to zero when idle — no cost when no traffic.
  • CDN for static content: Serve static assets from edge locations, reducing origin server load and data transfer costs.
  • Database optimization: Right-size database instances, use read replicas instead of scaling up the primary, and implement caching to reduce database query volume.
  • Container density: Run multiple services on shared Kubernetes nodes to improve resource utilization.

Cost Visibility and Governance

You cannot optimize what you cannot see. Implement cost allocation tags on all resources, set up budget alerts, and review cost reports weekly. Assign cost ownership to teams — when teams are responsible for their cloud costs, they optimize naturally.

Conclusion

Cloud cost optimization is not a project — it is a practice. Implement right-sizing, use reserved capacity for stable workloads, leverage spot instances for fault-tolerant jobs, and architect for cost efficiency. The typical result is a 30-50% reduction in cloud spend without any impact on performance or reliability.

Need help reducing your cloud bill? Our DevOps team can audit your cloud infrastructure.