Keep your OpenInference instrumentors and OpenTelemetry spans. Confident AI scores every trace, span and thread, builds your datasets from production, catches drift and simulates conversations. Phoenix or Arize AX, the switch is three steps.
On Phoenix, evals are a job you schedule and host. On Arize AX they run for you, but the metrics are ones you build. Confident AI runs 50+ metrics on every trace as it lands, no job to schedule.
“Confident AI saves us 480+ hours of manual AI evaluation every month — and gives us the data to defend every quality decision in front of engineering, product, and leadership.”
Turning production spans into a dataset on Phoenix means a dataframe and a script, or a manual add. Confident AI curates datasets from production with ingestion tasks, filtered and tagged.
“Before Confident AI, a single improvement cycle took 10 days — I'd create a task, assign it to an engineer, wait for availability, and go back and forth. Now the same cycle takes three hours, and our product managers can run it themselves.”
Arize started as ML monitoring. Product managers and QA do not run evals there. Confident AI connects to your app over HTTP so non-engineers run experiments, annotate results and ship.
“We run a lot of large-scale, multi-turn simulations, and Confident AI made it far easier to design scenarios and execute those tests without piecing together external tools.”
See what changes for your team. Compare workflows for PMs, engineers, QAs, and SMEs.
| Feature | Confident AI | Arize |
|---|---|---|
Validate evals Check automated scores against human judgment | ||
Surface issues automatically Find recurring failures from production feedback | ||
Find product insights Understand patterns in real conversations | Not assessed | |
Find struggling users See which users experience failures and poor responses | Not assessed | |
Cross-functional workflows PMs and QA run evals without engineering |
FinomBefore Confident AI, a single improvement cycle took 10 days — I'd create a task, assign it to an engineer, wait for availability, and go back and forth. Now the same cycle takes three hours, and our product managers can run it themselves.
Confident AI saves us 480+ hours of manual AI evaluation every month — and gives us the data to defend every quality decision in front of engineering, product, and leadership.
Confident AI gave our team one place to turn production failures into datasets, align metrics, and keep regressions out of releases without waiting on custom engineering work.
HumachWe run a lot of large-scale, multi-turn simulations, and Confident AI made it far easier to design scenarios and execute those tests without piecing together external tools.
Thanks to Confident AI, we were able to move to a fine-tuned model and cut our LLM costs by 80%. This opens up whole new use cases now to generate better output with more targeted LLM calls.
