Track LLM Token Usage and Costs

This skill enables developers to monitor and measure token consumption when using Semantic Kernel AI models. It provides token counting, usage tracking, and cost estimation capabilities for both standard and streaming chat interactions, helping teams understand and control their AI infrastructure costs.

Use this skill when you need to track token consumption across AI model calls, estimate costs before deploying to production, or monitor usage patterns to optimize prompt engineering. It is essential when working with metered AI services where token usage directly impacts billing, or when you need to enforce usage quotas and budgets.

Do not use this skill if you are working with AI models that do not expose token-level metrics, or if your application does not require cost tracking or usage monitoring.

Users provide their Semantic Kernel chat interactions - either standard synchronous calls or streaming responses. The skill instruments these interactions to capture token metrics.

The output includes token counts (prompt tokens, completion tokens, total tokens), usage statistics across multiple calls, and cost estimates based on the token consumption and model pricing.

This skill is designed for Python developers building AI applications with Semantic Kernel who need visibility into token usage. It serves engineering teams managing AI infrastructure budgets, DevOps engineers monitoring production AI workloads, and product managers who need to forecast costs for AI-powered features. Data scientists optimizing prompt efficiency will find the token-level metrics valuable for iterating on prompt designs and comparing different approaches to the same task.