Arize

Arize is an observability and evaluation platform designed specifically for LLM-powered and traditional machine learning applications, covering the full lifecycle from development to production. It helps teams understand, monitor, and improve the behavior of models and agents by centralizing telemetry, performance metrics, and evaluation workflows in one place. The primary purpose of Arize is to make it easier to detect issues, measure quality, and iterate on AI systems with confidence and traceability.

Arize provides fine-grained tracing for LLM calls and multi-step agents, allowing users to inspect prompts, responses, intermediate tool calls, and latencies at the span level. It offers built-in and customizable evaluation frameworks (including rubric-based, model-based, and human-in-the-loop evaluations) to assess correctness, safety, relevance, and user experience. The platform supports monitoring of drift, anomalies, and regressions across datasets, models, and versions, with alerting and dashboards that connect offline experiments to online behavior. It also includes analytics for prompt variants, retrieval quality, and agent decision paths, enabling systematic debugging and optimization rather than ad hoc trial and error.