Cloud Cost Optimization Code Audit
Your cloud bill grows every month and nobody knows why. We find the waste and show you exactly how to cut it.
At Variant Systems, we pair the right technology with the right approach to ship products that work.
Why this combination
- Over-provisioned instances waste money every hour they run
- Unused resources from old projects accumulate silently on the bill
- Missing tagging makes it impossible to attribute costs to services or teams
- Default configurations almost always cost more than optimized ones
Common Cloud Cost Findings
The most consistent finding: over-provisioned compute. Instances sized for peak load that occurs 2% of the time run 24/7 at that capacity. A t3.xlarge at 5% average CPU utilization should be a t3.small. Multiply this across databases, application servers, and cache instances, and the waste is 40-60% of the compute bill.
Unused resources are the second finding. EBS volumes detached from terminated instances. Load balancers routing no traffic. Elastic IPs not attached to anything. S3 buckets from decommissioned projects. Each costs only dollars per month, but dozens of them add up to hundreds.
Data transfer costs surprise everyone. NAT gateway charges for traffic that could use VPC endpoints. Cross-region data transfer for services that could be co-located. Internet egress for traffic that could be served from a CDN cache. Data transfer is often 15-25% of the total bill and the hardest line item to understand.
Our Cost Audit Approach
We analyze utilization data across all resources. CloudWatch metrics for AWS, built-in monitoring for other providers. CPU, memory, network, and storage utilization over 30+ days reveals actual usage patterns. We compare actual utilization against provisioned capacity to identify right-sizing opportunities.
Resource inventory identifies everything running in the account. We flag resources with zero or minimal utilization. Each flagged resource gets a recommendation: decommission, downsize, or justify. We never recommend deleting something without confirming it’s genuinely unused.
Pricing model analysis compares current on-demand spending against reserved capacity and savings plans. For stable workloads, committed pricing saves 30-60%. We model different commitment levels and terms to find the optimal balance of savings and flexibility.
Storage costs receive dedicated attention because they grow silently. S3 lifecycle policies are often missing entirely, so every log file and temporary upload accumulates in standard storage tier indefinitely. We configure intelligent tiering rules that move infrequently accessed objects to Glacier or Infrequent Access classes automatically. RDS storage that was provisioned generously at launch but never right-sized gets reviewed against actual IOPS and capacity utilization. EBS snapshots from long-deleted volumes are identified and cleaned up. Database backups retained well beyond compliance requirements are trimmed to policy-appropriate windows. Storage optimization alone typically recovers 10-20% of a mid-size company’s monthly cloud spend.
What Changes After the Audit
Cloud bills drop 30-50% in the first month after implementing recommendations. The savings are ongoing - lower resource costs every month, not a one-time reduction. Tagging provides visibility so future cost growth is understood and intentional.
The team gains a framework for cost decisions. Right-sizing becomes a regular practice. New resources are provisioned based on actual needs, not guesses. Reserved capacity is reviewed quarterly. Cost becomes a design consideration alongside performance and reliability.
What you get
Ideal for
- Companies whose cloud bill has grown faster than their traffic
- Teams that don't know which services cost the most
- Organizations with cloud bills over $2,000/month that haven't been optimized
- Startups that want to extend runway by reducing infrastructure costs