A while ago i started exploring Azure AI Foundry and ended up going down a rabbit hole of 30+ implementations covering everything from GPT-5 chat to live speech transcription. In this post i will walk you through all the major Azure AI services, what they do, how to implement them, and when to use them — so you don't have to figure it all out the hard way like i did.
You can download git repo and start embedding your Azure OpenAI Service keys in.env file and start executing them as we go along.
Our objective is to understand the complete Azure AI Services ecosystem and how you can combine them to build enterprise-grade intelligent applications.
Azure OpenAI - GPT-5 Chat, Vision and Code
This is where most people start, and for good reason. Azure OpenAI gives you access to GPT-5 with enterprise-grade security, regional deployment and SLAs — unlike calling OpenAI directly.
The basic setup is straightforward. You initialize an AzureOpenAI client with your endpoint and API key, define a system role (something like "you are a helpful travel assistant"), pass in user messages and configure temperature and top_p for response behavior. That's it, you are doing conversational AI.
from openai import AzureOpenAI
from azure.core.credentials import AzureKeyCredential
from dotenv import load_dotenv
import os
load_dotenv()
client = AzureOpenAI(
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
api_key=os.getenv("AZURE_OPENAI_KEY"),
api_version="2024-12-01-preview"
)
What makes it more interesting is Vision. You can encode an image to base64, pass it as image_url in the message content, and GPT-5 will analyze and explain it — diagrams, screenshots, anything. I used this for code explanation too. Point it at a source file with a "you are a teacher" system prompt and let it stream the explanation back. Really useful for documentation generation and code reviews.
Chat Output:
Image Reading Output:
