Mastering API Management for Generative AI in Azure

Name: Mastering API Management for Generative AI in Azure
Rating: 4.4 (42 reviews)

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

Created byHoussem Dellai

Last updated 6/2026

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
Learn to consume generative AI models in Azure using API management, with practical demos of load balancing, authentication, and semantic caching. Secure architectures use private endpoints.
Introduction7:19
Explore Azure API Management for generative AI, covering load balancing, authentication with managed identities and RBAC, caching, policies, monitoring, and semantic caching with vector search for LLM workloads.
Connect with the author0:05

Introduction to connecting API Management to OpenAI1:26
Discover how azure api management secures and scales ai backends with a gen ai gateway, using authentication, token metrics, rate limiting, and load balancing across openai llm models.
[Demo] Connecting API Management to Azure OpenAI on the portal16:13
Connect Azure API management to Azure OpenAI using a managed identity, RBAC, and policies to obtain an access token and call AI services securely.
[Demo] Connecting API Management to Azure OpenAI using CLI12:10
Demonstrates connecting Azure API Management to Azure OpenAI using CLI, enabling managed identity authentication and policy-driven token exchange, and testing a GPT-4 deployment via Azure API Management.
[Demo] Testing connection APIM to OpenAI with Python Jupyter Notebook4:06
Demonstrates testing an Azure API management gateway connection to a GPT four model via Python notebooks, including deployment ID, API version, and subscription key usage.
[Demo] Connecting APIM to OpenAI using Terraform10:42
Demonstrates connecting Azure API Management to OpenAI models in Azure AI Services using Terraform, with managed identity authentication, token-passing policy, and subscription-key testing via the API Management gateway.
[Demo]Testing connecting to APIM using Python Jupyter Notebook2:50
Demonstrates connecting Azure API Management to Azure AI services with a Python notebook, using Terraform outputs for url and key, and invoking ChatGPT 4.0 via HTTP or Azure OpenAI SDK.
Quiz

Introduction to Load Balancing for LLM models2:03
Configure load balancing in azure api management to route requests across multiple LM models in different regions, reducing 429 errors caused by token limits.
[Demo] Configuring Load Balancing for LLMs in API Management5:51
Configure Azure api management to load balance traffic across three Azure AI service backends hosting ChatGPT, with weighted routing (70/20/10), using Terraform deployment and a retry policy for 429 errors.
[Demo] Testing Load Balancing configuration in Python2:47
Demonstrates testing Azure API management load balancing with Python requests, exporting gateway URL and subscription key via Terraform, and plotting 70% UK South, 20% Central, and 10% France Central.
Quiz

Introduction to Token Limit policy in API Management1:43
Learn to apply the Azure OpenAI token limit policy in API management to cap tokens per user at 500 per minute, configuring a counter key and visibility of remaining tokens.
[Demo] Token Limit policy in API Management5:43
Demonstrates deploying Azure API management with a token limit policy for OpenAI via Terraform, testing with Python notebooks, and enforcing 500 tokens per minute per IP address.
Quiz

Introduction to APIM Semantic Caching for LLM models5:32
Learn how semantic caching in Azure API Management caches prompts and responses for LLMs like OpenAI using Redis, enabling semantic search with embedding models and reducing latency and backend load.
[Demo] Configuring Semantic Caching in APIM8:51
Learn to enable semantic caching in Azure API Management with OpenAI, storing user questions and responses as vectors in Redis, using vector comparison and managed identity for secure access.
[Demo] Testing Semantic Caching on the APIM portal2:01
Explore semantic caching in Azure API Management by testing a 60s ttl policy with Redis caching of LLM responses, validating faster subsequent requests in the APIM test console.
[Demo] Testing Semantic Caching using Python Notebook2:42
Demonstrates testing semantic caching in Azure API management via a Python notebook using Redis cache and OpenAI SDK. Ten varied questions reveal identical responses and faster calls due to caching.

Introduction to APIM Logs and Metrics for LLMs4:36
Learn to collect logs and metrics from Azure API Management for OpenAI LLMs, including token counts and per-user chargeback, using Application Insights and Kusto queries.
[Demo] Collecting Logs and Metrics of API Management21:12
Demonstrates collecting logs and metrics from Azure API Management for OpenAI LM models using the Azure OpenAI emit token metric, with Terraform setup and dashboards in Application Insights and Grafana.
[Demo] Visualizing logs and metrics in Grafana5:43
Learn how to visualize Azure Monitor logs and metrics in Grafana, connect to Azure Monitor with a data source, and import dashboards to inspect requests, models, and tokens.

Introduction to securing Azure OpenAI with Private Endpoint2:06
Learn to secure Azure OpenAI deployments with a private endpoint, isolating service behind a private IP and enabling access via api management in the same VNet using private DNS zones.
[Demo] Connecting APIM to AI Services behind Private Endpoint7:25
Connect Azure API Management to Azure AI services, including OpenAI, behind a private endpoint using a dedicated subnet, private DNS zones, and VNet integration.

Introduction to private connectivity architecture5:46
Establish private connectivity for Azure AI services using Azure API Management, Azure Front Door, and Private Link, with private endpoints and DNS zones for secure, public-exposure-free access.
Introduction to private connectivity for APIM2:41
Learn to restrict Azure AI and APIM access with Privatelink private endpoints, disable public endpoints, and expose through Azure Front Door with WAF and global reach.
[Demo] Deploying the resources14:30
Demonstrates deploying Azure API Management resources using a private connectivity lab, Bicep templates, and a Python notebook to enable access to OpenAI services via private endpoints and Azure front door.
[Demo] Testing access through Front Door4:41
Test access to Azure AI services through front door with a post request, receiving a 200 response. Demonstrate private endpoint access to API management via a bastion VM and curl.

Requirements

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

Description

Unlock the full potential of Large Language Models (LLMs) like OpenAI in your enterprise applications with our comprehensive course, "Mastering API Management for Generative AI in Azure." This course is meticulously designed for API developers, cloud solution architects, AI practitioners, IT security professionals, and technical managers who are eager to integrate advanced AI capabilities into their workflows using Azure API Management.

Throughout this course, you will embark on a journey to understand the fundamentals of Generative AI and its integration with Azure API Management. We begin with a general introduction to the key features and capabilities of Generative AI within the Azure ecosystem. From there, we delve into advanced API management techniques, including load balancing, authentication, semantic caching, logging, metrics, token throttling, and retry and circuit breaker patterns.

One of the standout features of this course is the emphasis on security. You will learn how to implement private endpoints to secure your API endpoints, ensuring robust protection for your enterprise applications. Our hands-on modules will guide you through practical implementations, providing you with the skills and confidence to apply these techniques in real-world scenarios.

By the end of this course, you will have a solid understanding of how to optimize and secure LLM models using Azure API Management. You will be equipped with best practices for enterprise integration, enabling you to leverage the power of Generative AI to drive innovation and efficiency in your organization.

Join us on this exciting journey and transform the way you manage and secure AI applications in the cloud. Enroll now and take the first step towards mastering API management for Generative AI in Azure!

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: