Arize Phoenix vs Domino Model Monitor

Arize Phoenix is free, open-source software for tracing, evaluating, debugging, and improving AI applications. It captures model calls, retrieval, tool use, and custom logic as OpenTelemetry traces. Teams can score traces and spans with LLM evaluators, code checks, or human labels. Prompt tools provide versioning, side-by-side testing, and call replay, while dataset experiments compare evaluation results across application versions. Documented integrations include LlamaIndex, LangChain, DSPy, OpenAI, Bedrock, and Anthropic, plus evaluation libraries such as Harbor, Ragas, Cleanlab, and MLflow. Phoenix runs locally or can be self-hosted with Docker, Kubernetes, or a cloud environment; Arize says self-hosting keeps traces in the user’s environment. Self-hosted authentication supports OAuth2 identity providers with an OpenID Connect well-known endpoint. The Phoenix plan costs nothing and leaves trace spans, ingestion, projects, and retention under user management. The project is ELv2 licensed and built by Arize AI and the open-source community.

Domino Model Monitor evaluates production machine-learning models and alerts when degradation warrants expert review. It monitors data drift and model quality from a central dashboard showing status, summaries, customized alerts, and monitoring results. Automated prediction-data pipelines and metric generation support ongoing checks. When remediation is needed, teams can recreate a model’s development environment, retrain with production data, and redeploy a version. Models built in Python, R, SAS, MATLAB, TensorFlow, or DataRobot can be registered. The Elastic Monitoring Engine is designed to scale and ingest data from Amazon S3 and Hadoop-compatible storage, including Azure Blob Storage, Azure Data Lake, Google Cloud Storage, and HDFS. Domino supports API, web, and self-hosted use, including cloud VPCs, on-premises systems, and air-gapped environments. Pricing is on request, with Premium, Domino Cloud, and Enterprise plans listed.