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Stitching tools together around Langfuse?
Migrate to Confident AI.
Both take your OpenTelemetry traces. Confident AI scores every one of them, builds your datasets from production, catches drift and simulates conversations. Switch in three steps and keep your spans.
TRUSTED BY 500+ LEADING AI COMPANIES
Evals ran to date[ 0+ ]
HUMAN ANNOTATION
Using Langfuse with Google Forms? Bring feedback home.
You shouldn't need to export traces just to annotate them. Customize review forms, annotate traces, and turn feedback into test cases.
“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.”
Igor KolodkinHead of AI Quality, Finom
AUTOMATIC ISSUE DETECTION
Stop hunting traces. Let evals surface them.
Automatically flag failures, explain what went wrong, and route traces to the right SMEs for annotation.
“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.”
Anoop MahajanDirector of QA, Amdocs
CONVERSATION SIMULATION
Finally, native observability & evals for chat agents.
Simulate users, test follow-ups, and evaluate full conversations before your agents reach production.
“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.”
Sean AustinChief AI Officer, Humach
The difference
Where Langfuse stops and Confident AI starts.
See what changes for your team. Compare workflows for PMs, engineers, QAs, and SMEs.
| Feature | Confident AI | Langfuse |
|---|---|---|
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 |
TESTIMONIALS
Trusted by companies that take AI seriously.
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.
Igor Kolodkin,Head of AI Quality, Finom
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.
Anoop Mahajan,Director of QA, Amdocs
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.
SD
Senior Director of Engineering,Fortune 500 medical device company
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.
Sean Austin,Chief AI Officer, Humach
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.
John Lemmon,AI Lead, Supernormal