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LLM costs are easy to calculate after execution, but estimating them before a prompt or agent actually runs is a much harder problem.
In this episode, the focus is on predicting LLM and agent costs before execution, from estimating output length for a single model call to reasoning about the much more variable execution paths of AI agents.
You’ll learn:
* What can be predicted from a prompt before execution? Exploring how token consumption, output length, and model cost can be estimated before sending a request to an LLM.
* Can an agent’s cost be estimated before execution? Understanding the additional uncertainty introduced by multiple model calls, tool calls, retries, reasoning steps, and variable execution trajectories.
* What Python tooling is available today? Reviewing practical libraries and approaches for estimating and calculating LLM and agent costs, including LiteLLM, genai-prices, tokencost, and tiktoken.
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