Galileo vs STAMM

Galileo is an observability, evaluation and guardrail platform for generative AI and agentic applications. It provides ready-made evaluations for RAG, agents, safety and security, as well as custom evaluators. Observability tools analyze agent behavior to surface patterns and failure modes, while Galileo Experiments let teams evaluate datasets and prompts systematically and compare results. Evaluation scores can guide agent actions, tool access and escalation paths. Developers can use Python and TypeScript SDKs or the API, with integrations including CrewAI, Google ADK, LangChain and LangGraph, OpenAI, Microsoft Agent Framework, Strands Agents and Vercel AI SDK. Deployment options include SaaS, virtual private cloud and on-premises. The Free plan includes 5,000 traces per month, unlimited users and unlimited custom evaluations. Pro costs $100 a month, billed yearly, and includes 50,000 traces per month; pricing scales with trace volume. SSO supports OIDC and SAML only on a customer's own Galileo cluster, not the free version or hosted app.galileo.ai service.

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.