Tracing platform that captures model calls, tool execution, latency, token usage, and application context for debugging production AI systems.

Evaluation toolkit connecting datasets, experiments, automated evaluators, and human feedback to measure and improve the quality of AI applications.

Interactive prompt workspace for testing models and parameters against application context before deploying changes into production AI workflows.

Central prompt repository for versioning, testing, and releasing model instructions independently from application code, with SDK retrieval and caching.

Analytics dashboards that aggregate AI application cost, latency, usage, and quality signals to monitor performance and investigate changes.

In-product AI assistant that investigates project traces and metrics, then proposes resource updates and other actions requiring user approval.