Microsoft Azure's managed access to OpenAI models, deployed within an enterprise Azure tenant with its security and compliance controls.
AI Infrastructure comparison
Azure OpenAI Service vs Baseten
Pricing, pros, cons, and ideal use cases — side by side.
BasetenPaid
A platform for deploying and serving machine-learning models in production, with autoscaling, fast cold starts, and GPU infrastructure managed for you.
At a glance
| Azure OpenAI Service | Baseten | |
|---|---|---|
| Pricing | PaidUsage-based pricing, billed through Azure. Provisioned throughput units available for guaranteed capacity. | PaidUsage-based pricing tied to the compute your deployed models consume. |
| Category | AI Infrastructure | AI Infrastructure |
| Ideal for | Enterprises standardized on Microsoft AzureRegulated organizations needing data residencyTeams wanting OpenAI models under enterprise governance | Teams deploying custom or fine-tuned modelsEnterprises needing dedicated, autoscaling model servingOrgs that want to avoid managing GPU infrastructure |
Pros & cons
Azure OpenAI Service
Pros
- OpenAI models inside the Azure governance boundary
- Private networking and Entra ID identity
- Regional data residency and content filtering
- Azure compliance certifications carry over
Cons
- Ties the AI stack to Azure
- Capacity and quota management can be a project
- New models sometimes land later than on OpenAI directly
Baseten
Pros
- Production model serving without managing GPUs
- Autoscaling with fast cold starts
- Works with open, fine-tuned, and custom models
- Removes most MLOps overhead
Cons
- Unnecessary if you only use hosted frontier APIs
- Compute-based cost grows with traffic
- Still requires model and evaluation expertise
Which should you choose?
Azure OpenAI Service is built around enterprises standardized on microsoft azure; Baseten leans more toward teams deploying custom or fine-tuned models. Shortlist the one whose strengths line up with your biggest constraint.