Fiddler AI vs STAMM

Fiddler AI provides observability and security for AI agents and predictive applications. It evaluates agents and models during development, then monitors them in production, with diagnostics spanning more than 100 built-in and custom metrics such as hallucination, toxicity, PII/PHI, drift, performance, and business KPIs. Real-time guardrails can detect risks and pause, reroute, or escalate agent actions. The platform records decisions, actions, evaluations, and policy outcomes to support audit evidence for requirements including GDPR, HIPAA, NAIC, and SR 11-7. It names LangGraph, Amazon Bedrock, and AWS Strands Agents as native framework support, and is built on OpenTelemetry standards. Deployment options include cloud SaaS, VPC, on-premises, and air-gapped environments. Fiddler says it is SOC 2 Type II certified, complies with HIPAA, and encrypts data in transit and at rest. Plans include Free at free, Developer at a price agreed with the maker billed per trace, and Enterprise with no listed price. The company was founded in 2018 and is headquartered in Palo Alto, California.

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.