Arize AX vs STAMM
Arize AX
arize.comSTAMM
stamm.inrae.frSide by side
| Figure | Arize AX | STAMM |
|---|---|---|
| Price from | $50/mo | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 3 | 0 |
| Spec score · Machine Learning Model Monitoring Software | 8.3 (#6) | 7.1 (#14) |
| Purpose | Arize AX is a platform for continuous improvement of AI agents that turns production behavior into better agents. | STAMM is an open-source MLOps framework for deploying, monitoring, and maintaining machine-learning soft sensors in industrial processes. |
| Support | AX Free includes community troubleshooting support, AX Pro includes email support and AX Enterprise includes dedicated support. | The project page lists David Camilo Corrales at INRAE, Toulouse Biotechnology Institute, as a contact. |
Plans
In their own words
Arize AX
Arize AX is a platform for improving AI agents by turning production behavior into investigations, evaluations, and experiments. Signal surfaces important issues, helps identify causes, and prepares fixes for review. Managed Agents can investigate failing traces, fix repository bugs, automate recurring checks, or be created from scratch. Swarm Observability tracks status and performance across an agent swarm in real time. Agent-as-a-Judge uses telemetry to produce scalable evaluations that can surface unknown failures, while end-to-end experiments help validate changes. Integrations include OpenAI, Anthropic, Google GenAI, Amazon Bedrock, LangChain, LangGraph, LlamaIndex, CrewAI, and Vercel AI SDK; tracing follows OpenInference conventions and OpenTelemetry. AX Free costs nothing and includes 10 Signal issues/month, 25k trace spans/month, 1 GB ingestion/month, and 15-day retention. AX Pro costs $50 a month, with 25 Signal issues/month, 50k trace spans/month, 10 GB ingestion/month, and 30-day retention. Enterprise pricing is custom. Self-hosted deployment keeps observability data, authentication, APIs, and UI within customer infrastructure; air-gapped deployment is available.
STAMM
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.