#tokens

Developer advanced tutorial: once an agent is live, you need visibility into it. Use traces and spans to record the process, monitor latency, cost, errors, user feedback and accuracy, and run automated evaluations.

An experience piece (members only): for occasional use (chat, copywriting, code edits), an API is cheaper; for high-frequency batch work (daily transcription, classification, processing large volumes of material, fixed workflows), running locally may pay off. The core dividing line is monthly token consumption, plus whether the task needs top-tier model quality. The article works out the numbers and the hidden costs on both sides for you.