One platform for your entire AI lifecycle

What does LUML cover end-to-end?

Experiment tracking for both ML and LLM workloads, an artifact registry, one-click deployments to your own infrastructure, and production monitoring (coming soon). One platform instead of stitching together separate tools.

Is LUML open-source?

Yes. The core platform is open-source under a permissive license. You can inspect the code, contribute, and self-host if needed.

Where does my data live?

In your cloud storage. LUML connects to your S3 or Azure buckets but never stores your artifacts or data. Everything stays where you put it.

How is LUML different from MLflow / Weights & Biases?

LUML covers the full lifecycle in a single tool: experiments, registry, deployment, and monitoring. No need to integrate multiple platforms. It handles both classical ML and LLM workloads out of the box.

How is LUML different from SageMaker / Vertex AI / Azure ML?

Cloud ML platforms lock you into a single provider and come with significant complexity. LUML is cloud-agnostic: connect your own storage on AWS, Azure, or anywhere else. It’s simpler to set up, easier to use day-to-day, and you keep full control over your infrastructure.

How is LUML different from Langfuse / LangSmith / Arize?

Those tools focus primarily on LLM observability. LUML covers both ML and LLM workloads across the full lifecycle, from experiment tracking through deployment, in a single platform.

Can we adopt gradually?

Yes. Start with experiment tracking on one project, then expand to the registry, deployments, and monitoring as your team is ready. No migration required.

How quickly can we get started?

Minutes. Create a free account, connect your cloud storage, install the SDK with pip install luml-sdk, and track your first run. No procurement, no platform team needed.