Cloud Cost Audit for a Mid-Stage SaaS Platform

A mid-stage B2B SaaS platform serving enterprise customers across North America and Europe. Engineering team of approximately twenty. AWS-native infrastructure, multi-region deployment, accumulating roughly four years of architectural decisions made by team members who had since rotated off the project.

The platform's AWS spend had grown materially faster than its revenue base over three consecutive quarters. The finance team was asking the CTO why. The CTO was asking the engineering team. The engineering team's honest answer was that nobody had clear visibility into which workloads accounted for which costs. The platform had grown by accretion — each team adding what they needed, no centralized cost ownership, tagging discipline that had degraded over time.

The platform wasn't in financial distress. The cost trajectory was the problem. Without intervention, cloud spend would consume a meaningfully larger share of revenue within twelve months than the business model supported.

AlgoCoder was engaged for a four-week cost audit and remediation engagement covering the full AWS footprint. The work was structured in three phases.

Phase one — visibility — A comprehensive cost attribution exercise covering every account, region, and service. Tags were normalized across the organization where they existed and added where they didn't. Cost allocation reports were rebuilt to surface spend by team, by environment, and by service category. The output was the first complete cost-attribution view the platform had produced.

Phase two — remediation — With visibility in place, three categories of remediation were executed:

• ◇Idle and over-provisioned compute — EC2 instances and EBS volumes running below meaningful utilization were rightsized or decommissioned. Test environments were placed on scheduled shutdown policies. Reserved Instance and Savings Plan coverage was extended to predictable workloads.
• ◇Storage tiering — S3 buckets carrying historical data were moved to appropriate tiers (Standard-IA, Glacier, Deep Archive) with retention policies reviewed and updated. EBS snapshot retention was rationalized.
• ◇Network egress — Inter-region traffic patterns were analyzed; several services that had been routing through suboptimal paths were reconfigured to reduce data transfer costs.

Phase three — operational handoff — The remediation was paired with operational discipline that would prevent recurrence: tagging requirements built into the deployment pipeline, monthly cost reviews scoped per team, and a cost dashboard that surfaced anomalies as they appeared rather than after they accumulated.

The platform exited the engagement with a materially reduced monthly run rate against an unchanged workload, a complete cost-attribution view that didn't exist before, and operational practices that prevented the previous accretion pattern from recurring. The finance team had answers when they asked questions. The engineering team had visibility when they made architecture decisions.

The cost reductions came from operational hygiene more than from architectural rework. Most of the savings were in compute that should have been decommissioned and storage that should have been tiered. The architectural work was secondary. That's the pattern in most overrun-affected accounts.