Totally agree. Asking "how do you isolate provider latency" is now my demo opener. But I'd also watch their body language. A good technical answer is one thing, but genuine eagerness to *show* you the proof is the real green flag.
The pricing list is a great canary in the coal mine. If that's stale, imagine what's happening with their real-time latency calculations.
Totally true about cached responses and pre-processing calls. It's like the tool needs to understand your entire call graph, not just the final output.
We got burned by a similar issue with parallel RAG retrievers. The system would fire off five smaller model calls simultaneously, but the dashboard only showed the cost and latency of the main, final generation step. All that retrieval overhead was just... missing. Made our app look way cheaper and faster than it was.
Your forecasting point nails it too. A flat projection based on token volume is useless if you're A/B testing model providers or have a fallback chain for errors. The forecast has to model your actual routing logic.
Data is the new oil - but it's usually crude.
You've laid out a great initial framework for latency and cost breakdowns. I'd add a critical data layer requirement: the tool must expose its breakdown logic as queryable, time-series data, not just a dashboard.
For your latency decomposition, the key is whether the SDK itself generates high-resolution timestamps at the client side (pre/post-processing, network dispatch) versus deriving timings from HTTP-level observability. A client-side instrumented SDK can measure the time between your code issuing a call and the actual HTTP request being dispatched, which is often a significant source of overhead that gets misattributed to "network." Ask if you can query the raw span data with a millisecond-resolution timestamp for each phase. If they can't provide that, their "breakdown" is an estimation.
On cost per user or per workflow, your checklist needs a drill-down into token allocation for nested/parallel calls, as others noted. The forecasting feature is only useful if it can model your specific cost routing, like fallback chains. A flat projection based on average token count will be wrong if you're dynamically choosing between GPT-4 and a cheaper model based on query complexity. You need to verify the forecast model can ingest and weight separate cost series for each distinct logical workflow you've tagged.