After 7 years in FinOps and working with hundreds of companies, I’ve seen a familiar pattern emerge around the cost of Generative AI. There’s a lot of buzz — and a little bit of fear -about skyrocketing bills and unpredictable expenses.

But I’m here to tell you something simple: Don’t panic.

Managing AI costs isn’t some dark art. In fact, if you’re familiar with FinOps, you already have the playbook. The principles that guide us in managing cloud costs work just as well here. We’ve been through this before.

Don’t Fear the AI Bill: How to Apply Proven FinOps Playbooks to Generative AI Costs

We’ve Seen This Movie Before: The Kubernetes Analogy

Remember the early days of Kubernetes? Shared clusters meant a single, confusing bill, and standard tags fell short. It was a cost allocation nightmare. Then came namespaces and labels, which brought the granular visibility we needed to allocate costs effectively. It was a game-changer.

Application Profiles are the “Namespaces for AI”

The challenge with managing costs for services like AWS Bedrock has felt very similar. By default, all your AI model usage gets lumped together. But just like with Kubernetes, the tools have evolved. AWS recently introduced Application Inference Profiles.

Think of these profiles as the namespaces and labels for your AI workloads.

Instead of just tagging an instance, you create a profile for a specific context — like app:chatbot, team:marketing, or tenant:customer-42—and attach it directly to your API call. That context now flows through the entire system, allowing you to filter your costs in Cost Explorer by the exact tags you defined. The black box is open.

Cost is Only Half the Story. What About Business Value?

Knowing that the marketing-team profile cost $5,000 is the first step. But to truly optimize and demonstrate value, you need to connect cost data with usage data from CloudWatch.

With Application Profiles, your CloudWatch metrics — like InputTokenCount and InvocationLatency—are also tagged. Now you can answer the crucial business questions leadership cares about:

  • What is the Return on Investment (ROI) for our chatbot feature?
  • Can we provide transparent cost reports to management for each AI initiative?
  • Is the expensive Claude 3 Opus model for customer-42 actually delivering better latency that justifies its cost, or can we reduce expenses by 80% with a cheaper model without impacting the user?

The FinOps Loop is Unbroken

The core FinOps loop remains your guide:

  1. Inform: Use Application Profiles to get granular cost visibility. Combine it with tagged CloudWatch metrics to understand usage and calculate unit economics.
  2. Optimize: Use your insights to make data-driven decisions. Right-size your models, introduce caching, or even use simple code instead of an AI call for basic tasks.
  3. Operate: Turn these insights into action. Set budgets per profile, create alerts for anomalous usage, and build automation that takes action when thresholds are breached.

The fear around AI costs is understandable, but it’s based on an outdated view of the tooling. The practices we’ve honed over years of managing cloud costs are perfectly suited for this new challenge. It’s not a new game; it’s just a new level.

If you’re ready to move from fear to control and see this in action, let’s connect. I’ll be happy to walk you through setting up your first AI Application Profile and building an automation strategy that works.