Inspecting answer quality and checking whether those answers lead to revenue are different problems. Langfuse is built around the first, ABTO around the second.
Tracing, prompt management and evaluation for LLM calls
Compare real outcomes from your users for each model
Call path
Records your app's calls without changing the path
The gateway receives calls and forwards them to the assigned model
What counts as a good answer
LLM-based scores, human review and user feedback scores
Real service records such as payments and sign-ups
Switching models
Change it in code or prompt settings
Adjust the ratio for each model in the dashboard
Operation
Cloud or self-hosted
Gateway operated by ABTO
Descriptions of other tools are based on each company's public documentation as of October 2026. Features and pricing can change, so check their documentation for the latest details.
What Langfuse does well
Langfuse is an open-source tool that traces every LLM call and shows which input produced which answer and what it cost in money and latency. It also supports prompt versioning and dataset evaluation, which makes it strong for refining answer quality during development.
What ABTO does
Whether a highly rated answer makes users pay more has to be checked separately. ABTO splits real users across models by the ratio you set, and when a payment or sign-up happens it records the model that answered that user.
You can then see payment rate and cost side by side for each model.
Can you use both?
Langfuse records calls and ABTO receives calls and sends them to a model, so their roles do not overlap. You can refine answer quality with Langfuse during development and use ABTO after launch to see which model does better for revenue.