Hi everyone. I'm pretty new to OpenPipe and still trying to get my bearings. My team has a few workflows set up, but honestly, I'm not sure if they're actually performing well or if we're just wasting credits.
I come from a sales ops background, so I'm used to measuring everything in Salesforce or HubSpot. With OpenPipe, I'm a bit lost on what metrics I should even be looking at. I know I can see the basic logs, but I feel like I'm missing the bigger picture.
Could you help me understand what a good audit process looks like? Specifically:
* What are the key performance indicators for a workflow? Is it just cost and speed, or should I be tracking accuracy of the outputs somehow?
* How are you comparing the performance of different LLM models or fine-tunes within your pipelines? Is there a way to A/B test this effectively?
* For those using it for sales or support, how do you connect the output quality to actual business outcomes, like lead conversion or CSAT scores?
I'm trying to build a case for either expanding our usage or reworking our approach, and I need to show real ROI. Any advice on how to structure this kind of audit would be really appreciated. What pitfalls should I avoid?
Your sales ops background is actually a great foundation for this. You're right to think beyond basic logs. Treat each workflow as a black box service with inputs and outputs, then instrument it like you would any other business process.
The core KPIs I'd track form a hierarchy. Primarily, you need cost per successful inference (total credits spent / number of tasks that met your quality gate). Second, measure latency at the 95th percentile, not average, to catch erratic performance. Crucially, you must define a business specific accuracy metric, like "extracted field match rate" or "classification correctness," and sample outputs to track it over time. Without that, you're just measuring cheap, fast nonsense.
For A/B testing models, OpenPipe's logging lets you tag workflows with a model version parameter. Route a percentage of traffic to a new model, log the outputs, and compare your KPIs. The pitfall is not having a statistically significant sample size before declaring a winner. To connect to business outcomes, you'll need to correlate your workflow's accuracy metric with downstream results, e.g., does a correctly extracted lead attribute from an email actually predict a higher conversion rate? That's manual but necessary for ROI.
numbers don't lie
That last point about correlating with downstream results is the only thing that matters, but everyone misses it. You can have perfect "extracted field match rate" but if those fields don't impact pipeline velocity or conversion, the workflow is just a cost center.
You need to instrument your CRM to see if the output from OpenPipe actually changes the game. For example, a workflow that enriches leads: track if the "enhanced" leads have a higher MQL-to-SQL rate or shorter sales cycle compared to the baseline. If you can't measure that link, you're just optimizing for academic accuracy.
Everyone's missing the biggest pitfall: you're auditing the wrong thing. You can track all the internal metrics you want, but if you're not checking for vendor lock-in and credit burn, your ROI case is fake.
They want you focused on cost per inference so you tweak models, not so you notice the overall bill is doubling every quarter. Your sales ops instinct about measuring business outcomes is the only one that matters. Tie it directly to pipeline metrics or shut the workflow down.
Start your audit by isolating a single workflow and turning it off for a week. If no one notices or the pipeline doesn't change, you have your answer. All the other KPIs are just noise.