Morpheus Data

Architect defines a 'GPU-Standard' catalog item in Morpheus.

Data scientist logs into the Morpheus dashboard.

Selects 'GPU-Standard' and chooses their preferred cloud (e.g., AWS or On-prem).

Morpheus validates the budget and quota in real-time.

Morpheus triggers the provisioning workflow and hands over SSH keys.

Establish a Morpheus replication policy between on-prem and public cloud.

Morpheus monitors health of the primary instance.

Upon failure detection, Morpheus triggers an automation workflow.

The application is re-provisioned in the secondary cloud using stored images and IaC.

DNS is updated via native F5 or Route53 integration.

Morpheus ingests billing data from all connected clouds.

The AI engine identifies idle or over-provisioned LLM training clusters.

A notification is sent to the resource owner via Slack.

Owner clicks 'Approve' to downsize the instance directly from Slack.

Morpheus executes the resizing task automatically.

Architect sets a tagging policy requiring 'Data-Classification' tags.

A user attempts to deploy a database to a non-compliant region.

Morpheus intercepts the API call and blocks the deployment.

The user is redirected to a list of approved, compliant regions.

Deployment proceeds only after all governance checks pass.

Developers check model code into GitHub.

GitHub Action triggers a Morpheus API call.

Morpheus provisions a clean, ephemeral environment identical to production.

Tests are run; Morpheus captures the logs.

Upon completion, Morpheus tears down the environment to save costs.