Monitor AI Costs and Usage from Day One
AI API calls (especially to high-performance models) are not free and can become expensive very quickly. Without a monitoring strategy, costs will spiral out of control, you will have no way to attribute them to the correct teams or products, and you will have no data to justify the ROI.
You should implement granular cost and usage monitoring for all third-party AI services from the first day of use. Assign unique API keys or cloud-provider tags to each team, project, or feature to enable detailed cost attribution and prevent budget overruns.
Failing to monitor AI costs is a direct path to budget shock and an inability to prove value. It also contributes to pain-point-08-toolchain-sprawl by obscuring which tools are actually being used and which are providing value. A robust monitoring strategy is a core component of "FinOps for AI" and is essential for running a responsible, data-driven AI program. When an API call is made, the service (like OpenAI) returns the number of tokens used. This data must be captured. You cannot manage what you do not measure. By logging this usage data, you can build dashboards to track spend, identify high-cost users or features, and set alerts to prevent overages.
This is a foundational, non-negotiable practice. It must be implemented before giving teams access to any paid, consumption-based AI APIs.
There are several levels of maturity for AI cost tracking, all of which are effective: Good (API-Key-Level): The simplest method. Create a separate API key for each team, project, or feature. Most AI provider dashboards, including OpenAI's, allow you to view usage and costs per API key. This gives you basic project-level attribution. Better (Cloud-Provider-Level): If using AI services through a major cloud (e.g., Azure, Google Cloud), use their built-in cost management tools. Azure: Use Cost Analysis and "Group by" the Meter to see costs broken down by model series (e.g., GPT-4 vs. GPT-3.5). Google Cloud: Use the Metrics page for the Vertex AI API to view project-level usage, traffic, and errors. For all clouds: Apply tags (e.g., team: "payments", project: "doc-summarizer") to your resources. This allows you to filter cost reports by team or project. Best (Observability-Platform-Level): Integrate AI cost data into your existing observability platform (like Datadog). This allows you to create a unified dashboard that combines cost data with performance and health metrics. You can add tags, set up granular alerts for budgetary overages, and give engineers direct visibility into the cost of their services.
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Engineering Leader & AI Guardrails Leader. Creator of Engify.ai, helping teams operationalize AI through structured workflows and guardrails based on real production incidents.