Endpoints for inference in production

2025-08-13

APPLIES TO: Azure CLI ml extension v2 (current) Python SDK azure-ai-ml v2 (current)

After you train machine learning models or pipelines, or find suitable models from the model catalog, you need to deploy them to production for others to use for inference. Inference is the process of applying new input data to a machine learning model or pipeline to generate outputs. While these outputs are typically called "predictions," inference can generate outputs for other machine learning tasks, such as classification and clustering. In Azure Machine Learning, you perform inference by using endpoints.

Endpoints and deployments

An endpoint is a stable and durable URL that can be used to request or invoke a model. You provide the required inputs to the endpoint and receive the outputs. Azure Machine Learning supports standard deployments, online endpoints, and batch endpoints. An endpoint provides:

A stable and durable URL (such as endpoint-name.region.inference.ml.azure.com)
An authentication mechanism
An authorization mechanism

A deployment is a set of resources and compute required to host the model or component that performs the actual inference. An endpoint contains a deployment. For online and batch endpoints, one endpoint can contain several deployments. The deployments can host independent assets and consume different resources based on the needs of the assets. An endpoint also has a routing mechanism that can direct requests to any of its deployments.

Some types of endpoints in Azure Machine Learning consume dedicated resources on their deployments. For these endpoints to run, you must have compute quota on your Azure subscription. However, certain models support a serverless deployment, which allows them to consume no quota from your subscription. For serverless deployments, you're billed based on usage.

Intuition

Suppose you're working on an application that predicts the type and color of a car from a photo. For this application, a user with certain credentials makes an HTTP request to a URL and provides a picture of a car as part of the request. In return, the user receives a response that includes the type and color of the car as string values. In this scenario, the URL serves as an endpoint.

A diagram showing the concept of an endpoint.

Now suppose that a data scientist, Alice, is implementing the application. Alice has extensive TensorFlow experience and decides to implement the model using a Keras sequential classifier with a ResNet architecture from the TensorFlow Hub. After testing the model, Alice is satisfied with its results and decides to use the model to solve the car prediction problem. The model is large and requires 8 GB of memory with 4 cores to run. In this scenario, Alice's model and the resources—such as the code and the compute—that are required to run the model make up a deployment under the endpoint.

A diagram showing the concept of a deployment.

After a few months, the organization discovers that the application performs poorly on images with poor lighting conditions. Bob, another data scientist, has expertise in data augmentation techniques that help models build robustness for this factor. However, Bob prefers using PyTorch to implement the model and trains a new model with PyTorch. Bob wants to test this model in production gradually until the organization is ready to retire the old model. The new model also performs better when deployed to GPU, so the deployment needs to include a GPU. In this scenario, Bob's model and the resources—such as the code and the compute—that are required to run the model make up another deployment under the same endpoint.

A diagram showing the concept of an endpoint with multiple deployments.

Endpoints: standard deployment, online, and batch

Azure Machine Learning supports standard deployments, online endpoints, and batch endpoints.

Standard deployments and online endpoints are designed for real-time inference. When you invoke the endpoint, the results are returned in the endpoint's response. Standard deployments don't consume quota from your subscription; instead, they're billed with standard billing.

Batch endpoints are designed for long-running batch inference. When you invoke a batch endpoint, you generate a batch job that performs the actual work.

When to use standard deployment, online, and batch endpoints

Standard deployment:

Use standard deployments to consume large foundational models for real-time inferencing off-the-shelf or for fine-tuning such models. Not all models are available for deployment to standard deployments. We recommend using this deployment mode when:

Your model is a foundational model or a fine-tuned version of a foundational model that is available for standard deployments.
You can benefit from a quota-less deployment.
You don't need to customize the inference stack used to run the model.

Online endpoints:

Use online endpoints to operationalize models for real-time inference in synchronous low-latency requests. We recommend using them when:

Your model is a foundational model or a fine-tuned version of a foundational model, but it's not supported in standard deployment.
You have low-latency requirements.
Your model can answer the request in a relatively short amount of time.
Your model's inputs fit in the HTTP payload of the request.
You need to scale up in terms of number of requests.

Batch endpoints:

Use batch endpoints to operationalize models or pipelines for long-running asynchronous inference. We recommend using them when:

You have expensive models or pipelines that require a longer time to run.
You want to operationalize machine learning pipelines and reuse components.
You need to perform inference over large amounts of data that are distributed in multiple files.
You don't have low latency requirements.
Your model's inputs are stored in a storage account or in an Azure Machine Learning data asset.
You can take advantage of parallelization.

Comparison of standard deployment, online, and batch endpoints

All standard deployments, online endpoints, and batch endpoints are based on the idea of endpoints, therefore, you can transition easily from one to the other. Online and batch endpoints are also capable of managing multiple deployments for the same endpoint.

Endpoints

The following table shows a summary of the different features available to standard deployments, online endpoints, and batch endpoints at the endpoint level.

Feature	Standard deployments	Online endpoints	Batch endpoints
Stable invocation URL	Yes	Yes	Yes
Support for multiple deployments	No	Yes	Yes
Deployment's routing	None	Traffic split	Switch to default
Mirror traffic for safe rollout	No	Yes	No
Swagger support	Yes	Yes	No
Authentication	Key	Key and Microsoft Entra ID (preview)	Microsoft Entra ID
Private network support (legacy)	No	Yes	Yes
Managed network isolation	Yes	Yes	Yes (see required additional configuration)
Customer-managed keys	NA	Yes	Yes
Cost basis	Per endpoint, per minute¹	None	None

¹A small fraction is charged for standard deployment per minute. See the deployments section for the charges related to consumption, which are billed per token.

Deployments

The following table shows a summary of the different features available to standard deployments, online endpoints, and batch endpoints at the deployment level. These concepts apply to each deployment under the endpoint (for online and batch endpoints), and apply to standard deployments (where the concept of deployment is built into the endpoint).

Feature	Standard deployments	Online endpoints	Batch endpoints
Deployment types	Models	Models	Models and Pipeline components
MLflow model deployment	No, only specific models in the catalog	Yes	Yes
Custom model deployment	No, only specific models in the catalog	Yes, with scoring script	Yes, with scoring script
Inference server ³	Azure AI Model Inference API	- Azure Machine Learning Inferencing Server - Triton - Custom (using BYOC)	Batch Inference
Compute resource consumed	None (serverless)	Instances or granular resources	Cluster instances
Compute type	None (serverless)	Managed compute and Kubernetes	Managed compute and Kubernetes
Low-priority compute	NA	No	Yes
Scaling compute to zero	Built-in	No	Yes
Autoscaling compute⁴	Built-in	Yes, based on resource use	Yes, based on job count
Overcapacity management	Throttling	Throttling	Queuing
Cost basis⁵	Per token	Per deployment: compute instances running	Per job: compute instances consumed in the job (capped to the maximum number of instances of the cluster)
Local testing of deployments	No	Yes	No

² Inference server refers to the serving technology that takes requests, processes them, and creates responses. The inference server also dictates the format of the input and the expected outputs.

³ Autoscaling is the ability to dynamically scale up or scale down the deployment's allocated resources based on its load. Online and batch deployments use different strategies for autoscaling. While online deployments scale up and down based on the resource utilization (like CPU, memory, requests, etc.), batch endpoints scale up or down based on the number of jobs created.

⁴ Both online and batch deployments charge by the resources consumed. In online deployments, resources are provisioned at deployment time. In batch deployments, resources aren't consumed at deployment time but at the time that the job runs. Hence, there's no cost associated with the batch deployment itself. Likewise, queued jobs don't consume resources either.

Developer interfaces

Endpoints are designed to help organizations operationalize production-level workloads in Azure Machine Learning. Endpoints are robust and scalable resources, and they provide the best capabilities to implement MLOps workflows.

You can create and manage batch and online endpoints with several developer tools:

Azure CLI and Python SDK
Azure Resource Manager/REST API
Azure Machine Learning studio web portal
Azure portal (IT/Admin)
Support for CI/CD MLOps pipelines using the Azure CLI interface & REST/ARM interfaces