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Managed Compute Deployments
Deploy Models to Your Own Infrastructure
For models not available as serverless APIs, or when you need full control, use Managed Compute deployments.
Serverless vs Managed Compute
| Aspect | Serverless API | Managed Compute |
|---|---|---|
| Infrastructure | Fully managed by Microsoft | You manage VM quota |
| Billing | Per-token / PTU | Per-hour (VM hosting) |
| Setup | Minutes | 15-30 minutes |
| Control | Limited | Full (GPU type, scaling) |
| Best For | OpenAI models, quick starts | Open-source models, custom configs |
Managed compute uses Azure ML Online Endpoints under the hood, deploying models to VMs with specific GPU SKUs (like A100, H100).
🚧 Important: Managed compute requires VM quota approval in your Azure subscription. Request quota for GPU SKUs (e.g., Standard_NC24ads_A100_v4) before attempting deployment - approval can take 1-3 business days.
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What is the key difference between Serverless and Managed Compute deployments?
Serverless uses better models
Serverless is fully managed; Managed Compute gives you control over VMs and GPU hardware
There is no difference
Managed Compute is always cheaper