PODCAST — LLM Cost Prediction: From Single Prompts to Agentic Systems

Prefer reading instead? The full article is available here. The podcast is also available on Spotify and Apple Podcasts. Subscribe to keep up with the latest drops.

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.

👉 Enjoyed this episode? Subscribe to The AI Practitioner to get future articles and podcasts delivered straight to your inbox: aipractitioner.substack.com

Get full access to The AI Practitioner at aipractitioner.substack.com/subscribe