The problem
Inference, GPU, and cloud spend are climbing faster than the value they produce, and no one can attribute the cost to a feature, a team, or a decision.
The approach
A measured teardown of where the money goes (model choice, token economics, GPU utilization, egress, idle capacity) and a remediation plan with the savings quantified before any change ships.
Engagement
Priced on a share of verified savings, so the engagement pays for itself or it does not bill.
What's delivered
- Spend attribution by feature/team/workload
- Token-economics and model-selection review (right model for the job, not the biggest)
- Infrastructure right-sizing and utilization fixes
- Budgets, alarms, and per-feature cost visibility that stay after I leave
The outcome
A materially lower, fully attributable spend curve, and the controls to keep it that way.
In practice
What this looks like.
AI & Cloud Cost Teardown — Spend Drivers
SampleThe method a teardown follows to trace AI and cloud spend to its drivers before a single change is proposed.
- Model selection Whether each call routes to the smallest model that clears the eval bar, and where a premium model is doing work a cheaper one passes.
- Token economics Prompt and context bloat, repeated system preambles, retry storms, and whether caching and truncation are applied where they hold quality.
- GPU utilization Accelerator occupancy against what is reserved: batching, concurrency, and whether provisioned capacity tracks real throughput.
- Data egress Cross-AZ, cross-region, and vendor-bound traffic on the inference and retrieval paths, and which transfers colocation makes avoidable.
- Idle capacity Always-on endpoints, over-provisioned node groups, and non-prod environments that bill around the clock for daytime use.
- Cost attribution Whether tagging and telemetry can tie spend to a feature, team, and request, or whether the bill arrives as one undifferentiated line.
Situation. A fintech runs LLM-backed support triage and fraud-summary features. Inference and GPU spend climb faster than request volume, and finance cannot tie the bill to either product line.
Path
- 01 Instrument first: tag inference, GPU, and egress to feature, team, and request before touching any configuration.
- 02 Trace each driver (model routing, context and token size, accelerator occupancy, idle endpoints, egress paths) against the existing eval bar.
- 03 Quantify the saving behind each candidate change and rank by effort, risk, and quality exposure.
- 04 Ship approved changes through the production-safety gate, with quality evals gating every model or routing swap.
- 05 Wire budgets, alarms, and per-feature dashboards so the corrected spend curve holds after handoff.
Shape of outcome. Spend becomes attributable per feature and per request, the cost curve bends below the usage curve, and the controls to hold it stay in place after handoff.
Representative: illustrates the method, not a specific client.
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