Azure ML Commitment Plans Management Client

Name: Azure ML Commitment Plans Management Client API
Brand: Azure ML Commitment Plans Management Client
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferenceoauth212 EndpointsREST

For Agents

Create, update, and move Azure Machine Learning commitment plans and inspect plan usage history so an agent can manage committed-capacity contracts for Azure ML web services.

Connect the Azure ML Commitment Plans Management Client to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Azure ML Commitment Plans Management Client, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Install Jentic One Beta (Self-Hosted)

Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Azure ML Commitment Plans Management Client API.

Create or update Azure ML commitment plans inside a resource group with their SKU and capacity settings

List commitment plans across an entire subscription or scoped to a resource group

Move a commitment association from one commitment plan to another using the move action

Retrieve usage history for a commitment plan to reconcile consumed capacity against the contract

GET STARTED

Start building with Azure ML Commitment Plans Management Client API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for Azure ML Commitment Plans Management Client, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Azure ML Commitment Plans Management Client, keeping it validated and agent-ready. The API manages Azure Machine Learning commitment plans and their child commitment association resources under the Microsoft.MachineLearning provider. It supports full CRUD on commitment plans, listing and reading commitment associations, moving associations between plans, and retrieving plan usage history so finance and platform teams can track committed ML capacity.

Use Cases

Patterns agents use Azure ML Commitment Plans Management Client API for, with concrete tasks.

★ Provision a Committed Capacity Plan for Azure ML Web Services

Programmatically create an Azure ML commitment plan with the correct SKU and capacity tier so a downstream Azure ML web service can be billed against committed capacity rather than pay-as-you-go. The PUT on /commitmentPlans/{commitmentPlanName} accepts the plan name, location, SKU, and tags. Once created, web services can be associated with the plan via commitmentAssociations to lock in the contracted rate.

Create a commitment plan named 'prod-inference-plan' in resource group 'ml-rg' at location 'westus' with the S1 SKU and capacity 1, then return the plan's resource id

Reassign a Web Service Between Commitment Plans

Move a commitment association from one plan to another to rebalance committed capacity without redeploying the underlying Azure ML web service. The move operation under /commitmentPlans/{plan}/commitmentAssociations/{association}/move accepts a destination plan id and atomically rebinds the association. This avoids any downtime for the inference endpoint while accounting moves to the new plan.

Move the commitment association 'web-svc-a' from plan 'prod-inference-plan' to plan 'overflow-plan' in resource group 'ml-rg'

Reconcile Committed Capacity Usage

Pull the usage history of a commitment plan to compare consumed transactions and compute hours against the contracted capacity for finance reporting. The plan-usage endpoint returns paginated usage records with units consumed and overage indicators, scoped to a single commitmentPlanName. An agent can run this monthly and emit a reconciliation report or trigger a plan resize when consumption trends near the cap.

Retrieve the usage history for commitment plan 'prod-inference-plan' for the last billing cycle and emit a summary of total units consumed versus contracted

Agent-Driven Capacity Audit via Jentic

An AI agent uses Jentic to enumerate every commitment plan in a subscription, walk each plan's associations, and pull plan usage history to flag plans with no associations or persistent overage. The agent searches Jentic for 'list azure ml commitment plans', loads the schema, and chains the response into the associations and plan-usage endpoints. Azure AD tokens are sourced from the Jentic vault per call.

Search Jentic for 'list azure ml commitment plans', list every plan in the subscription, then for each plan retrieve its associations and usage history and emit a single audit report

Key Endpoints

12 endpoints — jentic publishes the only available openapi specification for azure ml commitment plans management client, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

PUT

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/commitmentPlans/{commitmentPlanName}

Create or update a commitment plan

GET

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/commitmentPlans/{commitmentPlanName}

Get a commitment plan

GET

/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearning/commitmentPlans

List commitment plans in a subscription

GET

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/commitmentPlans/{commitmentPlanName}/commitmentAssociations

List associations on a commitment plan

POST

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/commitmentPlans/{commitmentPlanName}/commitmentAssociations/{commitmentAssociationName}/move

Move an association to another plan

GET

/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearning/skus

List Microsoft.MachineLearning SKUs

PUT

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/commitmentPlans/{commitmentPlanName}

Create or update a commitment plan

GET

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/commitmentPlans/{commitmentPlanName}

Get a commitment plan

GET

/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearning/commitmentPlans

List commitment plans in a subscription

GET

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/commitmentPlans/{commitmentPlanName}/commitmentAssociations

List associations on a commitment plan

POST

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/commitmentPlans/{commitmentPlanName}/commitmentAssociations/{commitmentAssociationName}/move

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Azure AD client credentials are stored encrypted in the Jentic vault. A short-lived bearer token scoped to https://management.azure.com/ is fetched per call; the agent never sees the raw secret or refresh token.

Intent-based discovery

Agents search Jentic by intent (e.g., 'create an Azure ML commitment plan') and Jentic returns the matching Microsoft.MachineLearning operation with its full input schema so the agent calls the right endpoint without browsing the ARM reference.

Time to first call

Direct integration with Microsoft.MachineLearning: 1-2 days for ARM auth, polling, and pagination. Through Jentic: under an hour from search to first successful call.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

Azure ML Web Services Management Client

Manages the Azure ML web services that get bound to commitment plans via associations.

Use alongside Commitment Plans when the agent needs to deploy or update the web service that consumes committed capacity.

Alternative

Azure Machine Learning Workspaces

Modern Azure ML workspace API; the successor surface for current Azure ML deployments.

Choose this for current Azure ML workspaces, datasets, and compute targets; commitment plans cover the legacy ML Studio (classic) commitment model only.

Complementary

Azure Machine Learning Model Management Service

Manages registered models that can be deployed as Azure ML web services.

Use to register and version models before deploying them as a web service that consumes commitment plan capacity.

FAQs

Specific to using Azure ML Commitment Plans Management Client API through Jentic.

Why is there no official OpenAPI spec for Azure ML Commitment Plans Management Client?

Microsoft Azure does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Azure ML Commitment Plans Management Client via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Azure ML Commitment Plans Management Client use?

The API uses Azure Active Directory OAuth 2.0 (the azure_auth scheme) with the user_impersonation scope on https://management.azure.com/. Through Jentic, the bearer token is fetched from the Jentic vault and injected at call time so the agent never sees the raw client secret.

Can I move a commitment association between plans without downtime?

Yes. POST to /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/commitmentPlans/{commitmentPlanName}/commitmentAssociations/{commitmentAssociationName}/move with the destination plan resource id. The association is atomically rebound; the underlying Azure ML web service continues serving traffic.

What are the rate limits for the Azure ML Commitment Plans Management Client?

The spec does not declare per-API limits; calls are throttled by Azure Resource Manager's per-subscription read and write budgets for Microsoft.MachineLearning. On HTTP 429, honour the Retry-After header before retrying.

How do I list every commitment plan in a subscription through Jentic?

Search Jentic for 'list azure ml commitment plans', load the schema for GET /subscriptions/{subscriptionId}/providers/Microsoft.MachineLearning/commitmentPlans, and execute it. Jentic injects the Azure AD token automatically so the agent only supplies the subscription id.

Is the Azure ML Commitment Plans API free?

Control plane calls are free. The underlying commitment plan itself is a paid contract — pricing depends on the chosen SKU and capacity tier and is billed monthly against committed transactions and compute hours.