Track and Govern AI Inference Costs

May, 2026 • Alex Serban, Koen van der Blom, Joost Visser

57 / 57 • Governance •

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Intent

Prevent uncontrolled AI inference spend and make cost a first-class engineering concern.

Motivation

Serving large models, especially LLM APIs or GPU-based deep learning, can generate costs that are invisible until they appear on a cloud bill. Without instrumentation and governance, teams cannot make informed trade-offs between model quality, latency, and cost.

Applicability

Cost tracking and governance should be applied to any ML system served at scale or relying on pay-per-use APIs.

Description

As machine learning systems move into production, inference cost can quickly dominate the total system cost, particularly for large language models accessed via APIs or GPU-intensive deep learning workloads.

Instrument and Attribute Costs

Measure and log the compute cost of every inference request, broken down by model, endpoint, and use case. For API-based models, record token usage per call and aggregate by feature or user segment. For self-hosted models, track GPU/CPU utilisation, memory, and infrastructure spend per deployment. Cost instrumentation should be part of the same observability stack as latency and quality metrics.

Set Budgets and Alerts

Define cost budgets per model and per team, and configure alerts when spending approaches thresholds. Treat unexpected cost spikes as incidents: investigate whether they are caused by traffic growth, model changes, or inefficient prompting patterns.

Make Cost a Design-Time Trade-Off

Before deploying a model, explicitly evaluate the cost-quality trade-off:

benchmark lighter models or quantised variants against the target quality bar; if the cheaper model is sufficient, use it,
evaluate batching, caching, and request coalescing strategies at design time rather than as afterthoughts,
document the cost assumptions for the selected model in the deployment decision record.

Govern API Usage

For third-party LLM APIs, apply rate limits and quotas at the application level to protect against runaway loops or misbehaving clients. Review and renegotiate API contracts as usage scales. Avoid hard-coding specific external model versions without a plan for cost reassessment when pricing changes.

Machine Learning Operations: A Mapping Study