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 Together AI
Pricing, pros, cons, and ideal use cases — side by side.
Together AIPaid
A cloud platform for fast, cost-efficient inference and fine-tuning of open-weight models at production scale.
At a glance
| Azure OpenAI Service | Together AI | |
|---|---|---|
| Pricing | PaidUsage-based pricing, billed through Azure. Provisioned throughput units available for guaranteed capacity. | PaidUsage-based per-token pricing. Dedicated endpoints and fine-tuning are priced separately. |
| Category | AI Infrastructure | AI Infrastructure |
| Ideal for | Enterprises standardized on Microsoft AzureRegulated organizations needing data residencyTeams wanting OpenAI models under enterprise governance | Teams running open-weight models in productionCost-sensitive, high-volume inference workloadsEnterprises fine-tuning private model variants |
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
Together AI
Pros
- Fast, cost-efficient open-model inference
- Wide and current model selection
- Fine-tuning and dedicated endpoints available
- Often cheaper than closed APIs at scale
Cons
- Open models shift evaluation and safety onto you
- Quality varies by model and task
- Not a managed governance platform on its own
Which should you choose?
Azure OpenAI Service is built around enterprises standardized on microsoft azure; Together AI leans more toward teams running open-weight models in production. Shortlist the one whose strengths line up with your biggest constraint.