Cloud Cost Audit feeds cloud-spend telemetry, utilization metrics, logs, traces, and the code itself into an AI that reasons across all of them, the way a senior engineer auditing a system would, scaled. The output is a ranked roadmap of cost-savings and consolidation opportunities, each grounded in cross-signal evidence. On a recent engagement it identified 35%+ savings on a multi-tenant SaaS's legacy cloud footprint, work the single-signal tools missed entirely.
Most cost reviews start in one of three places (the billing console, the monitoring dashboards, or someone asking "what are we paying for?") and they get one answer's worth of evidence. We start by wiring all four signal streams together. Cloud-spend exports give the bill, line by line. Utilization metrics (CPU, memory, network, DB) say what each resource is doing. Logs, traces, and traffic flows show who's calling whom. Code and IaC repos say what was intended and where reality has drifted.
The connection is the deliverable in itself: every dollar maps to a workload, every workload to a service, every service to its code home. From there, the questions get specific.
This is where the cross-signal AI does its work. Each candidate lever (right-size this, consolidate that, downgrade the database tier, schedule this environment off) gets weighed against all four streams before it lands in the roadmap. Not a heuristic ("CPU < 5% = idle, decommission"); a judgment that asks if the workload is real, what depends on it, what features the license is actually using. The "looks idle" service is often a critical batch worker the traces catch; the "fine to run as-is" database is often paying for licensing on features the code doesn't use.
The output is a ranked list of levers, each with a dollar figure, a risk class, and an owner. Humans approve, defer, or reject, never a black box. Every line cites the evidence it weighed.
Implementation goes in waves, low-risk levers first, deeper architectural changes once the visibility holds. Wave 1 on a recent engagement shipped a weekends-only schedule on non-production environments, $600–800/mo, immediate. Phase 2 scoped a SQL Server licensing downgrade ($2,200–2,900/mo, the single biggest lever) and a round of MIG consolidation. Each wave's execution cites the audit findings, so any change traces back to the evidence behind it.
The cuts you don't make matter as much as the ones you do. On the same engagement, a "park-and-watch" on an environment slated for sunset surfaced 59 requests per minute of live traffic within 24 hours, health checks green, real users hitting a 404 nobody had monitored. The lever flipped from "decom" to "fix and keep."
We don't end at Wave 1. Cloud Cost Audit becomes a continuous practice: re-running the audit on a cadence, watching for new drift in the IaC, catching spend creep before it turns into the next cleanup. The same cross-signal reasoning that found the levers the first time watches them stay closed, and surfaces the next ones as the workload evolves, the team grows, and the cloud providers change pricing.
On the engagement behind this case, the ongoing pass catches roughly one new lever per month, the kind one-shot audits never see, because the system that was right last quarter isn't always right this one. Savings only matter if they keep saving.
Cloud Cost Audit doesn't depend on a vendor. Cloud-native billing exports cover the spend story on AWS, GCP, and Azure equally. Prometheus, Cloud Monitoring, CloudWatch, or any time-series store covers utilization. Loki, Cloud Logging, or any log aggregator covers operations. Terraform, or Pulumi, or CloudFormation, covers infrastructure-as-code. Git covers history. The toolchain bends to fit what the customer already runs.
The novel work is the AI synthesis on top, reasoning across four signal streams the way a senior engineer would, and refusing to recommend a cut until the data agrees on it.
30 minutes, no pitch. Mike runs the call.