Azure for Students tier that gives you free cloud credits annually with no credit card required.
Scenario A: You Already Have an Active Azure subscription
Log into Azure AI Foundry and create a new Azure Foundry resource.
Microsoft Foundry resource
Create a new project under your subscription(student subscription if you have one). Select an optimal region for model availability (for my case I selected Sweden Central). Create your resource group.
New foundry project
Navigate to the Model Catalog and choose Explore Models.
Explore model
Select your preferred available coding model.
Model selection
Deploy it using the default settings, making note of the target model name (e.g.,for my case I used gpt-5.3-codex).
Copy your Endpoint URL and API Keys from the model deployment dashboard. Keep these secure we will need them for VS Code.
Scenario B: You Need to Create One
Head over to the official Azure for Students Portal, sign in using your academic university email address (.edu or your institutional domain), and verify your student status. Your cloud sandbox and free credit allotment will provision instantly.
vs code Configuration Blueprint
Instead of wrestling with native extension limitations, we are going to configure your connection as an open-standard, OpenAI-Compatible gateway, while preserving the specialized Responses API handling required by modern Azure Foundry endpoints.
Step 1: Open the Custom Models Panel
- Launch VS Code.
- Open your Copilot Chat panel on the sidebar.
- Click on the model selection dropdown menu at the top or bottom of the chat interface.
- Select "Other Models..." from the bottom of the list.
[η»ε:other models option]
Step 2: Input Your Azure Endpoint Details
A new UI panel labeled "Adding custom endpoint" will appear. Populate the fields using the credentials you copied from your Azure AI Foundry dashboard:
-
Endpoint URL: Paste your Azure endpoint (ensure it targets the base
/openai/v1 route).
-
API Key: Paste your secure Azure endpoint key.
-
Model ID: Match it to your deployment name (e.g.,
gpt-5.3-codex).
custom endpoint
π‘ Pro-Tip: Deep Tweaking via JSON
If you prefer fine-grained control or want to explicitly enforce parameters like context windows or vision support, you can open your user chatLanguageModels.json file via the Command Palette (Ctrl+Shift+P / Cmd+Shift+P -> Chat: Manage Language Models) and drop in this clean schema:
[{"name":"[Azure AI Foundry (BYOK)]","vendor":"customendpoint","apiKey":"${input:chat.lm.secret.YOUR_SECRET_ID}","apiType":"responses","models":[{"id":"gpt-5.3-codex","name":"GPT-5.3 Codex (Foundry)","url":"[https://***.services.ai.azure.com/openai/v1](https://***.services.ai.azure.com/openai/v1)","toolCalling":true,"vision":true,"maxInputTokens":128000,"maxOutputTokens":4096}]}]
Step 3: Clear the IDE Cache Layers
To ensure VS Code registers the new custom endpoint layout:
- Reload your window via the Command Palette (
Developer: Reload Window).
- Open your Copilot Chat panel.
- Click the model dropdown selection menu. You should now see your deployed model available as a selectable model option!
[η»ε:your model]
The Result: Infinite Coding Freedom
Once your endpoint is active and your environment is refreshed, you are ready to build without constraints.
You have officially decoupled your IDE's intelligence layer from restrictive credit pools. By routing your Azure subscription credits straight into your workspace via a BYOK configuration, you gain full autonomy over your development ecosystem completely free of token caps or artificial feature tiers. Happy hacking!
I would love to get your feedback and insights on this.
Are you currently implementing BYOK setups or custom routing within your local teams and IDEs?
How are you balancing API performance and cost management across modern developer environments?