Cloud Cost Control (FinOps + AIOps)
Continuous cost engineering -- waste detection, root-cause correlation, IaC-driven optimisations, and prepared cost reviews so cloud and AI spend stays controlled as you scale.
- Cost baseline with automated tagging: infrastructure scanned, untagged resources detected, tagging corrections proposed as pull requests to establish clear ownership
- Root-cause anomaly detection: cost spikes linked to specific deploys, autoscaler changes, or traffic shifts with targeted remediations -- not just that spend increased, but what caused it and how to fix it
- Continuous savings execution: rightsizing evaluated, IaC changes drafted for committed savings, safe automations (shutdown, cleanup) executed with human approval for high-impact changes
- Prepared cost reviews: monthly agenda generated with savings achieved, anomaly summary, and ranked optimisation opportunities for human decision
Cost Baseline With Automated Ownership
Automated scanning of your infrastructure produces the cost baseline, breaking down spend across compute, storage, network, and Kubernetes workloads, attributed to teams and services. There is no manual spreadsheet phase -- resources are inventoried, mapped to owners, and gaps highlighted continuously.
Attribution is enforced through policy-enforced tagging standards. Every resource gets an owner, a cost centre, and an environment tag. Untagged or mistagged resources are detected continuously and corrections proposed as pull requests, so ownership stays accurate as infrastructure evolves.
The baseline includes month-over-month trends and top cost drivers, ready for human review. Finance and engineering start each optimisation conversation with a shared, current picture of where money goes and who is responsible.
Anomaly Detection and AI Spend Telemetry
Cost overruns are easiest to fix when you know exactly what caused them. AI agents correlate cost anomalies with root causes in real time -- not just that spend increased, but that a specific autoscaler change on service X added a specific amount per month, with a proposed remediation attached.
For AI workloads, dedicated AI spend telemetry covers token consumption, inference cost, and cost-per-workflow tracking. This matters because AI costs scale with usage in ways traditional compute does not, and a single poorly optimised prompt chain can consume a meaningful share of the monthly budget.
Alerts are routed to resource owners with full context: what changed, when it started, and which service is responsible. Each alert includes a specific remediation proposal from the agent, reducing the time from detection to resolution and ensuring no anomaly is acknowledged without a clear next step.
Continuous Savings With Safety Boundaries
Rightsizing opportunities are evaluated continuously, agents draft IaC changes for committed savings, and safe automations such as non-production shutdowns and orphaned resource cleanup run on schedule. Fry Express delivers these automations as code, integrated into your existing IaC and CI/CD workflows.
Every automation has a safety boundary. Rightsizing recommendations are validated against peak usage before being applied. Cleanup policies exclude resources with active dependencies. Scheduling rules respect maintenance windows and on-call requirements. High-impact changes require human approval; low-risk automations execute within defined blast-radius limits.
The result is reviewable, versioned, and reversible automation. Your team maintains full control over what runs and when, while automated evaluation covers the continuous assessment that manual processes cannot sustain.
Monthly Cost Reviews and Optimisation Backlog
Agents prepare the monthly cost review automatically: generating the agenda, highlighting anomalies, calculating savings achieved since the last review, and ranking the next optimisation opportunities by expected savings, effort, and risk. Humans review the findings and decide which items to pursue.
The review produces an optimisation backlog maintained automatically and owned by humans. Typical entries include commitment purchases, storage tier migrations, egress reduction, and workload consolidation. Fry Express facilitates the first reviews and provides templates so the cadence continues independently.
The result is continuous, agent-assisted cost engineering: scanning, correlating, proposing, and executing within governed boundaries. Cloud and AI spend scales with the business, not ahead of it.