Mastering API Management for Generative AI in Azure

Name: Mastering API Management for Generative AI in Azure
Rating: 4.5 (46 reviews)

Optimizing and Securing LLM Models with Azure API Management: Load Balancing, Authentication, Semantic Caching, and Priv

Created byHoussem Dellai

Last updated 10/2026

English

English [Auto],

What you'll learn

Understand the Fundamentals of Generative AI in API Management
Implement Advanced API Management Techniques for LLM models
Apply Best Practices for Enterprise Integration
Enhance Security with Private Endpoints

Course content

8 sections • 27 lectures • 2h 42m total length

Teaser1:35
Preview how Azure API Management supports generative AI applications and the topics covered in this course.
Introduction7:19
Explore the course structure and the role of API Management in connecting, securing, and observing generative AI services.
Connect with the author0:05

Introduction to connecting API Management to OpenAI1:26
Understand how API Management sits between client applications and Azure OpenAI to manage model requests.
[Demo] Connecting API Management to Azure OpenAI on the portal16:13
Follow the Azure portal workflow for connecting API Management to an Azure OpenAI backend.
[Demo] Connecting API Management to Azure OpenAI using CLI12:10
Use command-line tools to configure the connection between API Management and Azure OpenAI.
[Demo] Testing connection APIM to OpenAI with Python Jupyter Notebook4:06
Send requests from a Python Jupyter notebook to test the API Management connection to Azure OpenAI.
[Demo] Connecting APIM to OpenAI using Terraform10:42
Review how Terraform provisions and connects API Management and Azure OpenAI resources.
[Demo]Testing connecting to APIM using Python Jupyter Notebook2:50
Use a Python Jupyter notebook to test requests to the API Management endpoint.
Quiz

Introduction to Load Balancing for LLM models2:03
Understand how load balancing distributes LLM requests across model backends.
[Demo] Configuring Load Balancing for LLMs in API Management5:51
Configure API Management to distribute requests across LLM backends.
[Demo] Testing Load Balancing configuration in Python2:47
Use Python requests to test the load-balancing behavior configured in API Management.
Quiz

Introduction to APIM Semantic Caching for LLM models5:32
Understand semantic caching and how it can reuse responses to semantically similar LLM requests.
[Demo] Configuring Semantic Caching in APIM8:51
Configure semantic caching for LLM requests in API Management.
[Demo] Testing Semantic Caching on the APIM portal2:01
Test semantic caching from the API Management portal and inspect the resulting responses.
[Demo] Testing Semantic Caching using Python Notebook2:42
Use a Python notebook to send requests and test semantic caching behavior.
Semantic Caching: Configuration and Validation

Introduction to APIM Logs and Metrics for LLMs4:36
Explore the logs and metrics available for observing LLM traffic through API Management.
[Demo] Collecting Logs and Metrics of API Management21:12
Configure and inspect API Management logs and metrics for generative AI requests.
[Demo] Visualizing logs and metrics in Grafana5:43
Explore Grafana visualizations of API Management logs and metrics.
Observability: Logs, Token Metrics and Grafana

Introduction to private connectivity architecture5:46
Review the architecture and request path for private connectivity to AI services.
Introduction to private connectivity for APIM2:41
Understand the networking considerations for private connectivity in API Management.
[Demo] Deploying the resources14:30
Follow the resource deployment for the private-connectivity architecture introduced in this section.
[Demo] Testing access through Front Door4:41
Test access through Azure Front Door to verify the deployed request path.
Front Door: Private Connectivity and Access Tests

Requirements

Basic Understanding of APIs
Familiarity with Azure
Basic Programming Knowledge in Python
An active Azure Subscription

Description

Put a managed gateway between your applications and your language models. This course shows how Azure API Management can help you connect to Azure OpenAI, distribute requests, control token usage, reuse suitable responses, and understand what is happening across your generative AI traffic.

Rather than treating an LLM endpoint as an isolated API call, follow a practical progression from the first connection to a more controlled network architecture. Portal demonstrations, command-line configuration, Terraform examples, and Python notebooks show the different parts of the solution working together.

What is inside the course

Connect API Management to Azure OpenAI through the Azure portal, command-line tools, and Terraform examples.
Test requests from Python Jupyter notebooks so you can examine the client-to-gateway-to-model request path.
Configure load balancing across LLM backends and use Python to test the resulting distribution of requests.
Apply a token-limit policy to govern model usage at the API gateway.
Configure semantic caching and test its behavior from both the portal and a Python notebook.
Collect API Management logs and metrics, then explore their visualization with Grafana.
Connect API Management to AI services behind private endpoints.
Follow a private-connectivity architecture that brings together Azure Front Door, private API Management, and private AI services, including deployment and access testing.

Understand the decisions, not just the settings

Each topic addresses a practical concern when applications depend on LLM services: how requests reach a model, how usage is governed, when a cached response can be useful, how traffic is observed, and where private networking fits. Connecting these concerns helps you evaluate gateway configurations in the context of the application you are building.

Who will benefit

This course is for developers integrating AI APIs, Azure cloud engineers, platform engineers, and solution architects who want a focused, hands-on look at API Management for generative AI. Basic familiarity with Azure, HTTP APIs, and language-model services will help you follow the demonstrations.

Use the examples to build a clearer mental model of the AI gateway layer and a practical starting point for your own Azure OpenAI integrations.

Who this course is for:

API Developers and Engineers
Cloud Solution Architects
AI and Machine Learning Practitioners

Mastering API Management for Generative AI in Azure

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 9min

Connecting API Management to Azure OpenAI6 lectures • 47min

Load Balancing for LLMs in API Management3 lectures • 11min

Token Limit policy in API Management2 lectures • 7min

Semantic Caching for LLMs in API Management4 lectures • 19min

Observability features in API Management3 lectures • 32min

Connecting APIM to AI Services behind Private Endpoint2 lectures • 10min

Connecting Front Door to private APIM and Private AI Services4 lectures • 28min

Requirements

Description

Who this course is for: