Azure OpenAI vs OpenAI: Choosing the Right Path

Clients ask this constantly: “Should we use OpenAI directly or go through Azure?”

After building production systems on both, here’s the honest comparison.

When to Use Azure OpenAI

Enterprise compliance. Your data stays in your Azure tenant. No data sent to OpenAI’s servers. This matters for regulated industries.

Existing Azure investment. If you’re already on Azure, Azure OpenAI integrates with your existing identity, networking, and monitoring.

Content filtering. Azure adds built-in content filtering. Good for customer-facing applications.

SLA requirements. Azure OpenAI comes with enterprise SLAs. Direct OpenAI doesn’t.

Private networking. Azure OpenAI supports private endpoints. Your traffic never touches the public internet.

When to Use OpenAI Directly

Speed of access. New models appear on OpenAI first. Sometimes weeks before Azure.

Simpler setup. Sign up, get an API key, start building. No Azure subscription needed.

Latest features. Assistants API, GPTs, new model variants—OpenAI usually has them first.

Side projects. For personal or experimental projects, OpenAI’s simplicity wins.

Cost for small scale. No Azure overhead for low-volume usage.

The Practical Decision Framework

Is this for an enterprise client?
├── Yes → Azure OpenAI
│   ├── Regulated industry? → Azure OpenAI (no question)
│   ├── Already on Azure? → Azure OpenAI
│   └── Need latest models immediately? → Consider both
└── No
    ├── Production app? → Depends on scale
    │   ├── High scale → Azure OpenAI (SLAs matter)
    │   └── Low scale → OpenAI direct
    └── Prototype/experiment? → OpenAI direct

What Most People Get Wrong

“Azure OpenAI is more expensive.” Not really. The per-token pricing is comparable. Azure adds management overhead, but also adds value.

“OpenAI has better models.” Same models. Same capabilities. Azure just gets them later sometimes.

“You have to choose one.” You don’t. Many of my clients use OpenAI for prototyping and Azure OpenAI for production. The APIs are nearly identical.

My Setup

For client work: Azure OpenAI. Always. Compliance, SLAs, and integration with their existing Azure infrastructure make it the obvious choice.

For personal projects: OpenAI directly. Faster iteration, latest features, simpler billing.

For production side projects: Azure OpenAI. Because I’ve been burned by rate limits at 2 AM.

Migration Between Them

The good news: switching isn’t hard.

# OpenAI
from openai import OpenAI
client = OpenAI(api_key="sk-...")

# Azure OpenAI
from openai import AzureOpenAI
client = AzureOpenAI(
    azure_endpoint="https://my-resource.openai.azure.com",
    api_version="2024-10-21",
    azure_ad_token_provider=get_token
)

# Same API from here
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello"}]
)

The client library is the same. The API calls are the same. Switching is mostly configuration.

The Bottom Line

Don’t overthink this. Match the platform to your requirements.

Enterprise? Azure. Experiment? OpenAI. Both? Both.

The model capabilities are identical. The difference is in the operational wrapper around them.