Should I use Hugging Face or Azure ML for deploying an open-source model on Azure?

Question

I am working on deploying an open-source model, and my final web application will be hosted on Azure. I am evaluating two options:

1. Deploying the model using Hugging Face.
2. Using Azure Machine Learning (Azure ML) for deployment.

I want to make an informed decision based on the following factors:

• Cost: Which option is more cost-effective in the long run?
• Security: Which provides better security, especially for handling sensitive data?
• Scalability: Which approach is more efficient for handling large-scale inference requests?

I have noticed that Hugging Face introduces latency issues, especially with the tokenizer taking over 10 seconds to load the model. Given these factors, I would appreciate insights from the community on which approach would be the best for deploying an open-source model on Azure.

Thanks in advance!

Answer 1

Hello Harshit !

Thank you for posting on Microsoft Learn.

IMHO, I think your criteria (cost, security, scalability, latency) are exactly the right ones to focus on.

1. Cost

Hugging Face (Inference Endpoints / Spaces)

• Hugging Face charges per model usage or compute instance.
• Inference Endpoints can be expensive for persistent usage, especially with GPU models.
• Tokenizer/model loading time (like you mentioned) can cause inefficiencies in per-request billing models.

Azure ML

• You control the underlying infrastructure (VM sizes, auto-scaling).
• You can use spot VMs or low-priority nodes for cost efficiency.
• With Azure Container Instances (ACI) or Kubernetes Service (AKS), you can scale dynamically and pause when idle.
• Better suited for cost optimization over time due to flexible compute options and billing models.

Winner (Cost): Azure ML, especially if you’re already within the Azure ecosystem and want fine control over infra.

2. Security

Hugging Face

• Shared cloud infrastructure.
• Limited customization of networking.
• May not meet strict data compliance requirements for sensitive or regulated data.

Azure ML

• Supports Private Endpoints, VNet Integration, Managed Identity, Key Vault, and more.
• You can deploy in a fully isolated environment, even air-gapped if needed.
• Integrates with Azure RBAC, Purview, and enterprise-level security tooling.

Winner (Security): Azure ML, without a doubt. It’s enterprise-grade and designed for sensitive workloads.

3. Scalability

Hugging Face

• Good for small-scale demos and prototypes.
• Scaling is limited to their infrastructure settings.
• Can run into cold-start issues and high latency when scaling out (like with Spaces).

Azure ML

• Designed for production and high-throughput scenarios.
• Supports batch endpoints, real-time endpoints, autoscaling, and advanced monitoring.
• Can integrate with Azure Kubernetes Service (AKS) for maximum control and scalability.

Winner (Scalability): Azure ML. Built to scale from a single developer to enterprise-grade ML ops.

Latency (especially tokenizer/model loading)

• Azure ML gives you full control — you can pre-load the tokenizer/model in memory, keep warm containers running, and avoid reloading on each request.
• Hugging Face often has cold starts and may reinitialize the tokenizer/model per request if not persistently running.

Winner (Latency): Azure ML, because you control the deployment behavior.