Projects

Use projects to organize your LLM traces

Organize your traces and annotations into projects

Projects provide organizational structure for your AI applications, allowing you to logically separate your observability data. This separation is essential for maintaining clarity and focus.

With Projects, you can:

• Segregate traces by environment (development, staging, production)

• Isolate different applications or use cases

• Track separate experiments without cross-contamination

• Maintain dedicated evaluation spaces for specific initiatives

• Create team-specific workspaces for collaborative analysis

Projects act as containers that keep related traces and conversations together while preventing them from interfering with unrelated work. This organization becomes increasingly valuable as you scale - allowing you to easily switch between contexts without losing your place or mixing data.

The Project structure also enables comparative analysis across different implementations, models, or time periods. You can run parallel versions of your application in separate projects, then analyze the differences to identify improvements or regressions.

Explore a Demo Project

Arize Phoenix