LLM cost attribution: Tracking and optimizing spend for GenAI apps

As companies ramp up their use of large language models (LLMs), cost management has become a critical concern. Whether it’s a customer support assistant or a content generation engine, the cost of inference quickly adds up when LLMs are used across teams and products. Without clear visibility into who is using what and for which purpose, optimizing spend becomes nearly impossible.

Why LLM cost attribution matters

Unlike traditional cloud infrastructure, where cost attribution is pretty much a solved problem (you tag resources, group them by department, and track accordingly), the LLM world is still catching up. When you're working with LLMs, your usage patterns tend to be more fluid, teams access APIs differently, prompts vary considerably, and multiple departments might hit the same endpoints but for entirely different purposes.

Without attribution:

• Finance teams struggle to tie spend back to business value
• Engineering teams can’t optimize usage
• Leadership has no clarity on ROI

As your organization leans more heavily into LLMs, this attribution gap quickly becomes more than just an accounting headache—it becomes a genuine roadblock to scaling your AI initiatives effectively.

Challenges in attributing LLM costs

First, there's a real lack of standard metadata in most LLM requests.Without this structured information, connecting costs to specific initiatives becomes a manual process.

Then there's the fragmentation problem. Your organization likely has multiple tools and services making independent calls to various LLMs. Your data science team might use a notebook environment, while your product team accesses models through a custom application, and your marketing folks use a no-code platform. Each of these creates its own isolated stream of usage.

The lack of unified logging compounds this issue. With costs spread across providers like OpenAI, Anthropic, and your open-source deployments, you're looking at multiple dashboards and billing systems with no automatic way to bring that data together.

Unlike static infrastructure, where usage patterns tend to be predictable, LLM prompts are dynamic and evolving. The prompts that worked well last week might be replaced by more efficient ones today, making historical cost analysis less useful for forecasting future spend.

Setting up smart LLM cost tracking

Let's look at some practical approaches to get better visibility into your LLM spending. Based on what we've seen work for teams managing significant LLM deployments, here are some effective practices:

First, bring everything into one place. Instead of jumping between OpenAI's dashboard, Anthropic's billing page, and your internal metrics for open-source models, set up a centralized observability platform. This gives you a single source of truth for all LLM usage logs, making analysis significantly easier.

Make tagging non-negotiable. For every single request going to an LLM, include metadata tags for things like team, use_case, environment, and when appropriate, user_id. This might seem tedious at first, but it's the foundation that makes all subsequent analysis possible.

Organize by what matters to your business. Once you have the data, create views that align with how your organization thinks about costs. Set up budgets and alerts. Define spending thresholds for each team or use case, and set up alerts when usage approaches these limits. This gives teams time to optimize costs before they become problematic, and helps avoid those uncomfortable conversations about unexpected bills.

Optimizing LLM spend through attribution

Now that you've got visibility into where your LLM budget is actually going, you can make data-driven decisions to optimize that spending.

• Spot high-cost, low-value use cases and tune or remove them
• Consolidate model usage to negotiate better rates
• Route low-priority traffic to cheaper or open-source models
• Cache frequent requests to cut down on repeated calls
• Identify opportunities to batch requests or compress prompts

The role of LLMOps in cost attribution

When we talk about LLMOps, proper attribution becomes one of those foundational elements that enables several critical capabilities.

First, it transforms your planning and forecasting from guesswork into data-driven decisions. When you know exactly how much each use case costs and how those costs trend over time, you can project future spending with much higher confidence.

Perhaps most importantly, attribution data feeds directly into your model selection and routing strategies. Understanding the cost-performance tradeoffs for specific use cases helps you make smarter decisions about which models to deploy where, ensuring you're not using expensive models for tasks where cheaper alternatives would work just as well.

Read more: What is LLMOps?

The right LLMops platform for cost attribution

If you're looking to implement these attribution practices without building everything from scratch, Portkey is the right LLMops platform that offers a ready-to-use solution that handles many of the challenges we've discussed.

With Portkey, you can immediately start tagging requests with metadata for teams, environments, and specific use cases. The platform handles detailed logging of all your LLM interactions and presents this data through real-time analytics dashboards.

This means you can attribute every dollar spent to the appropriate team or project, track your costs down to the token level across different providers (whether that's OpenAI, Anthropic, or others), and set up those crucial budget limits and alerts to keep spending under control. And since it's designed to fit into existing workflows, you don't need to overhaul your current setup to start getting these insights.

LLM usage across organizations is only going to increase. Without proper attribution in place, those costs will grow unpredictably and potentially create friction around AI adoption.

With attribution systems like Portkey, you get the visibility and control needed to scale your LLM applications confidently, turning cost from a potential barrier into a manageable aspect of your AI strategy.

Looking to implement this for your organization? Try Portkey today!