Arpeggio AI
Enterprise-grade observability and real-time guardrails for LLM-powered applications.
The leading AI observability platform for ML and LLM monitoring, debugging, and evaluation.
Arize AI is an industry-standard AI observability platform designed for the complex lifecycle of machine learning and large language models (LLMs). Its 2026 market positioning centers on 'Model Intelligence,' moving beyond simple monitoring to proactive troubleshooting and automated evaluation. The platform's technical architecture is built on OpenTelemetry, allowing seamless data ingestion from diverse environments. Arize excels in root-cause analysis by utilizing high-dimensional embedding visualizations to identify where models fail—whether through data drift, performance degradation, or hallucination in RAG pipelines. With its open-source library, Phoenix, Arize has democratized LLM tracing and evaluations, providing developers with the tools to visualize LLM spans and traces in real-time. For enterprises, Arize provides a robust governance layer, ensuring model compliance and security across global deployments. As AI moves from experimental to mission-critical in 2026, Arize serves as the 'control room' for AI engineers to maintain model health, optimize costs, and guarantee the reliability of generative AI applications.
Uses UMAP and t-SNE algorithms to project high-dimensional model embeddings into 3D space for cluster analysis.
Enterprise-grade observability and real-time guardrails for LLM-powered applications.
The open-source AI observability platform for LLM evaluation, tracing, and data exploration.
The lightweight toolkit for tracking, evaluating, and iterating on LLM applications in production.
The Intelligent AI Observability Platform for Enterprise Scale MLOps.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Otel-compatible tracing for multi-step LLM chains, tracking latency, token usage, and accuracy at every node.
Statistical monitoring of feature distributions comparing production data against training baselines using KL Divergence.
Quantitative evaluation of Retrieval-Augmented Generation systems using context-relevance and faithfulness metrics.
Automated checks for disparate impact and equal opportunity across protected classes in model predictions.
Detection of null values, type mismatches, and out-of-range features in the ingestion pipeline.
Compare the performance of a champion model against a challenger model in real-time without impacting users.
Chatbots providing incorrect or 'hallucinated' information to customers.
Registry Updated:2/7/2026
Review the retrieval context to identify missing data.
Fraud models losing accuracy as attacker behavior evolves over time.
Multi-step LLM chains taking too long to respond to users.