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Documentation Index

Fetch the complete documentation index at: https://docs.expanse.sh/llms.txt

Use this file to discover all available pages before exploring further.

Expanse trains and operates its own deep learning models for resource sizing, failure diagnosis, and optimisation suggestions. Those models improve through the Expanse data flywheel: every captured workload adds evidence about what ran, what resources it used, how it finished, and which recommendation helped next time.

1. Workloads run

The daemon captures workload and compute telemetry.

2. Evidence lands

Evidence stays inside the configured deployment.

3. Models learn

Deep learning models improve resource sizing, diagnosis, and optimisation.

4. Answers loop back

Analyse and diagnose recommendations shape the next workload run.
Each run feeds the next one, meaning the models get even better over time, and better answers produce better future runs.

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Planes

PlaneRole
Data planeStores evidence inside the configured deployment.
Intelligence planeRuns model training, analyse, diagnose, and optimisation.
Control planeHandles identity, organisations, endpoint discovery, registration, and licence validation.

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Data boundaries

The daemon runs on your compute and captures very granular data. It is a collector, not a plane. For privacy-concerned enterprises, the data plane and intelligence plane can run on your network. If you prefer Expanse-managed infrastructure, they can run on ours. It is your choice. The hosted Console is available at console.expanse.sh for every deployment. It is the user-facing view of your compute and intelligence workflows, not where telemetry is stored. The control plane handles identity, registration, endpoint discovery, and licence validation. It does not receive any form of workload data we collect. NOT A SINGLE BYTE.

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Trust

Expanse is SOC 2 Type II pending.