As AI applications become more complex, are people actually tracking token usage and costs at a workflow level?
It's easy enough to see usage for individual model calls, but once a feature spans multiple prompts, models, tools, retries, and background jobs, I've found it much harder to answer questions like:
PromptLayer is AI observability for developers. Trace requests, workflows, token usage, latency, costs, and failures through a single timeline and waterfall view. Follow complete execution paths across multi-step AI systems, understand where failures occur, identify slow or expensive workflow steps, and debug AI applications with the same visibility developers expect from modern software systems.
