When I launched PromptLayer a few weeks ago, I described it as AI observability.
The more time I've spent talking to developers and watching real AI systems run, the more I've realised that observability is only part of the problem.
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.
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:
