Arize Phoenix vs STAMM

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.

STAMM is an open-source MLOps framework for deploying and maintaining machine-learning soft sensors in industrial processes. It monitors live process data for concept drift and changes in operating regimes, and its registry records model versions, configurations, artifacts, validation results, and metadata. A dashboard shows process measurements, soft-sensor outputs, drift signals, and historical context, with support for human-in-the-loop labelling. STAMM serves Python and R soft sensors through REST inference. Its event-driven orchestrator calls the registry over REST and links predictions to the data snapshot that produced them. The drift detector package offers 10 detectors through a common interface and can also be used separately. Data can enter through equipment REST hooks, MQTT applications, or frameworks such as LEAF. InfluxDB is the reference time-series store; a PostgreSQL adapter is described as in progress. The reference Docker Compose installation supports Linux, macOS, or Windows with Docker 24 or later, and 8 GB RAM or more is recommended. Released under Apache License 2.0, STAMM is intended for process modelers, ML engineers, operators, and production managers.