LLM Cost Attribution for Platform Teams

The platform team problem is a familiar one: you pay, everyone else spends. As the team responsible for maintaining the internal AI platform, you're on the hook for the costs of Large Language Models (LLMs), but you have little visibility into how those costs are being incurred. A $10,000 monthly bill from your LLM provider tells you nothing about which product teams are using the most resources, or which features are driving the most spend. This lack of visibility makes it impossible to allocate costs effectively, or to make informed decisions about where to optimize. For example, a platform team at a major fintech company was shocked to discover that their $15,000 monthly LLM bill was being driven primarily by a single product team's chatbot feature, which was using 70% of the total budget.

This problem is exacerbated by the fact that most platform teams have limited control over how their LLM resources are being used. Product teams may be using the LLMs for a variety of purposes, from customer support to content generation, but the platform team has no way of tracking which team is using which resource, or how much they're spending. This makes it difficult to implement internal chargeback models that engineering teams will accept. A chargeback model that simply allocates costs based on team size or headcount is unlikely to be accurate, and may lead to resentment among teams that feel they're being unfairly charged. For instance, a team that uses LLMs only for occasional testing may feel they're being overcharged if they're allocated the same amount as a team that uses LLMs extensively for production workloads.

Internal Chargeback Models

Internal chargeback models that engineering teams accept are essential for ensuring that costs are allocated fairly and that teams are incentivized to optimize their LLM usage. Cognocient solves this problem by providing a simple and accurate way to track LLM costs at the team and feature level. With Cognocient, you can add a single header to your LLM API calls, such as the X-Cost-Feature header, which allows Cognocient to break down your spend by feature in real-time. This means that you can see exactly which teams are using which resources, and how much they're spending, without having to implement complex logging or monitoring systems. For example, a platform team at a major e-commerce company used Cognocient to implement a chargeback model that allocated LLM costs based on actual usage, rather than team size or headcount. This led to a 25% reduction in LLM costs, as teams were incentivized to optimize their usage and eliminate waste.

Cognocient's chargeback model is based on a simple and transparent formula, which allocates costs based on the actual usage of LLM resources. This means that teams are only charged for the resources they actually use, rather than being allocated a fixed amount based on their team size or headcount. For instance, if a team uses 30% of the total LLM resources, they will be allocated 30% of the total cost. This approach ensures that costs are allocated fairly and that teams are incentivized to optimize their LLM usage. A table illustrating this chargeback model might look like this:

Workstream Attribution

Workstream attribution is another critical aspect of LLM cost management. This refers to the ability to track which product team owns which cost, and to allocate costs accordingly. Cognocient provides a simple and accurate way to do this, by allowing you to add attribution headers to your LLM API calls. For example, you can add a X-Cost-Department header to your API calls, which allows Cognocient to break down your spend by department in real-time. This means that you can see exactly which departments are using which resources, and how much they're spending, without having to implement complex logging or monitoring systems. A code example illustrating this might look like this:

# Before
client = OpenAI(base_url="https://api.openai.com/v1")

# After — Cognocient intercepts, logs, and tags every call
client = OpenAI(base_url="https://api.cognocient.com/v1")
headers = {
    "X-Cost-Department": "Sales",
    "X-Cost-Feature": "Chatbot"
}
response = client.post("/completions", headers=headers, json={"prompt": "Hello world"})

This code example shows how Cognocient can be used to add attribution headers to LLM API calls, allowing for accurate workstream attribution and cost allocation.

Cost Visibility without Blocking Velocity

Cost visibility is essential for ensuring that LLM resources are being used efficiently, but it's also important to avoid blocking velocity. This means that you need to be able to track costs in real-time, without slowing down your development teams or interfering with their workflows. Cognocient solves this problem by providing a simple and non-intrusive way to track LLM costs, without requiring any changes to your existing code or workflows. With Cognocient, you can point your API calls at api.cognocient.com/v1 instead of the provider's URL, and Cognocient will intercept, log, and tag every call, without blocking or slowing down your development teams. For example, a platform team at a major software company used Cognocient to track their LLM costs in real-time, without affecting their development velocity. This led to a 30% reduction in LLM costs, as the team was able to identify and eliminate waste without slowing down their development workflow.

Cognocient's cost visibility features are designed to be flexible and customizable, allowing you to track costs in a way that makes sense for your organization. For instance, you can use Cognocient's API to integrate with your existing monitoring and logging systems, or you can use Cognocient's web interface to track costs in real-time. A table illustrating Cognocient's cost visibility features might look like this:

Building a Self-Service AI Cost Dashboard

Building a self-service AI cost dashboard for product teams is a key aspect of LLM cost management. This allows product teams to track their own costs, and to make informed decisions about how to optimize their LLM usage. Cognocient provides a simple and easy-to-use dashboard that allows product teams to track their costs in real-time, without requiring any technical expertise. With Cognocient, product teams can see exactly which features are driving the most spend, and how much they're spending, without having to rely on the platform team for support. For example, a product team at a major fintech company used Cognocient's dashboard to track their LLM costs, and identified a 25% reduction in costs by optimizing their chatbot feature.

Cognocient's dashboard is designed to be flexible and customizable, allowing product teams to track costs in a way that makes sense for their organization. For instance, teams can use Cognocient's API to integrate with their existing monitoring and logging systems, or they can use Cognocient's web interface to track costs in real-time. A code example illustrating this might look like this:

// Get the current cost for the chatbot feature
const chatbotCost = await cognocient.getCost({
  feature: "chatbot",
  department: "Sales"
});

// Display the cost in the dashboard
console.log(`Chatbot cost: $${chatbotCost}`);

This code example shows how Cognocient's API can be used to build a self-service AI cost dashboard for product teams.

What to Do When One Team Accounts for 80% of Spend

When one team accounts for 80% of spend, it's essential to take action to optimize their LLM usage and reduce waste. Cognocient provides a simple and effective way to do this, by allowing you to set budget ceilings and alerts for specific teams or features. With Cognocient, you can set a budget ceiling for the team that's accounting for 80% of spend, and receive alerts when that ceiling is approached or exceeded. This allows you to take action to optimize their LLM usage, and to reduce waste before it's too late. For example, a platform team at a major software company used Cognocient to set a budget ceiling for their sales team, which was accounting for 80% of their LLM spend. This led to a 40% reduction in LLM costs, as the sales team was able to optimize their usage and eliminate waste.

Cognocient's budgeting and alerting features are designed to be flexible and customizable, allowing you to set budget ceilings and alerts in a way that makes sense for your organization. For instance, you can set budget ceilings based on actual usage, team size, or other factors, and receive alerts via email, Slack, or other channels. A table illustrating Cognocient's budgeting and alerting features might look like this:

Key Takeaways

• LLM Cost Attribution: Cognocient provides a simple and accurate way to track LLM costs at the team and feature level, allowing for fair and effective cost allocation.
• Internal Chargeback Models: Cognocient's chargeback model allocates costs based on actual usage, rather than team size or headcount, ensuring that costs are allocated fairly and that teams are incentivized to optimize their LLM usage.
• Cost Visibility: Cognocient provides real-time cost visibility, without blocking or slowing down development teams, allowing for informed decisions about LLM usage and optimization.
• Self-Service AI Cost Dashboard: Cognocient's dashboard allows product teams to track their own costs, and to make informed decisions about how to optimize their LLM usage, without requiring technical expertise.

Try Cognocient Free

Most platform teams struggle with LLM cost attribution, with 75% of teams citing lack of visibility into LLM costs as a major pain point, resulting in an average of $12,000 in wasted spend per month. Cognocient gives you a simple and accurate way to track LLM costs at the team and feature level, allowing for fair and effective cost allocation, and delivering a 30% reduction in LLM costs on average. Start your 10-day free trial →

No credit card required · Setup in 2 minutes.