Azure OpenAI Service, explained end to end
The GPT-5 model family, Azure AI Foundry architecture, Provisioned Throughput pricing, and a working technical guide for migrating off the direct OpenAI API — everything a Microsoft-first enterprise needs to plan an Azure OpenAI deployment.
Data verified July 2026 · sourced from Microsoft's official Azure OpenAI documentation
What is Azure OpenAI Service?
Azure OpenAI Service is Microsoft's enterprise-hosted version of OpenAI's models — GPT-5, GPT-4.1, the o-series reasoning models, and GPT-4o — deployed inside your own Azure subscription. Instead of calling OpenAI directly, you get the same models wrapped in Azure's identity layer (Microsoft Entra ID), private networking (VNet, Private Link), and compliance certifications (SOC 2, HIPAA, FedRAMP High). It's managed today through Azure AI Foundry, the unified portal that replaced the standalone Azure OpenAI Studio.
Every model in Azure OpenAI today
From the flagship GPT-5 to cost-optimized nano variants and the wider Azure AI Foundry model catalog.
GPT-5
FlagshipComplex reasoning, multi-step agentic workflows, and highest-quality generation across enterprise workloads.
GPT-5 mini / nano
Cost-optimizedHigh-volume classification, summarization, and chat where latency and cost matter more than peak reasoning depth.
GPT-4.1
Long-contextLarge-document analysis, codebase-wide reasoning, and retrieval-heavy applications needing very long context.
o-series (reasoning models)
Deep reasoningStep-by-step chain-of-thought tasks — math, scientific analysis, and multi-stage planning that need deliberate reasoning.
GPT-4o
MultimodalVision, audio, and text together in one call — document understanding, image Q&A, and voice-enabled assistants.
Azure AI Foundry Model Catalog
Open-weight & third-partyLlama, Mistral, DeepSeek, and other open-weight models served serverlessly alongside GPT models in the same Foundry project.
Input token pricing by model
Pay-as-you-go pricing per 1 million input tokens — output tokens are billed separately and typically cost 4-8x more.
Azure OpenAI Pay-As-You-Go Input Pricing
USD per 1 million input tokens
Deployment types & pricing models
Azure OpenAI offers four ways to buy capacity — three pay-as-you-go tiers plus reserved Provisioned Throughput.
Global Standard
Requests route to whichever Azure region has capacity, giving the best throughput and lowest cost per token for workloads without strict data-residency needs.
Data Zone Standard
Keeps processing within a geography (e.g. US or EU) for regulatory requirements, at a small premium over Global Standard.
Regional Standard
Pins processing to one specific Azure region — the strictest data-residency option, typically at the highest per-token rate of the three pay-as-you-go tiers.
Provisioned Throughput Units (PTU)
Dedicated, reserved model capacity starting around $2,448/month, with 15-50 PTU minimums depending on deployment type. Can cut effective per-token cost by up to 70% once volume is high and consistent enough to justify the commitment.
Built for regulated, Microsoft-first environments
Microsoft Entra ID Auth
Keyless, identity-based authentication instead of long-lived API keys — the recommended pattern for production Azure workloads.
Azure AI Content Safety
Built-in content filtering for hate, violence, self-harm, and sexual content categories, plus prompt-injection ("jailbreak") detection.
Private Networking
VNet integration and Azure Private Link keep model traffic off the public internet, meeting the same network controls as the rest of your Azure estate.
Compliance Coverage
SOC 2 Type II, ISO 27001, HIPAA (with a signed BAA), and FedRAMP High in Azure Government — audited alongside your existing Azure compliance posture.
Azure AI Foundry
A unified hub-and-project structure for organizing models, agents, evaluations, and deployments — the successor to the standalone Azure OpenAI Studio.
Microsoft 365 & GitHub Copilot Integration
The same Azure OpenAI models power Microsoft 365 Copilot and GitHub Copilot Enterprise, so custom apps and off-the-shelf Copilots share one governance model.
Migrating from the OpenAI API to Azure OpenAI
For most codebases this is a small, mechanical change — not a rewrite.
Provision & Deploy
Create an Azure OpenAI resource inside an Azure AI Foundry project, then deploy a model — this generates a deployment name distinct from the underlying model name.
Resource creation, model deployment, quota (TPM) planning
Swap Authentication
Replace your OpenAI API key with an Azure OpenAI endpoint and key — or better, Microsoft Entra ID for keyless auth in production.
AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_API_KEY, or Entra ID token
Update the Base URL
Point your OpenAI SDK at your Azure endpoint using the v1 API path — the new v1 surface no longer requires an api-version parameter on every call.
base_url: https://YOUR-RESOURCE.openai.azure.com/openai/v1/
Use Deployment Names, Not Model Names
Azure OpenAI always requires the deployment name in requests — even when your code passes what looks like a model name, it must map to a deployment you created.
Deployment-to-model mapping, per-environment deployment names
Validate & Cut Over
Azure AI Foundry's portal generates working code snippets prefilled with your project's variables — run those to confirm parity before routing production traffic.
Side-by-side output comparison, latency and rate-limit testing
How Azure OpenAI got here
Azure OpenAI Service GA
General availability with GPT-3.5 and early GPT-4 access, deployed through Azure OpenAI Studio.
PTU Program Expands
Provisioned Throughput Units roll out across Global, Data Zone, and Regional tiers with lower minimum commitments.
Azure AI Foundry Unification
Azure OpenAI Studio is absorbed into Azure AI Foundry — a single hub-and-project workspace spanning OpenAI models, the broader model catalog, and agent tooling.
GPT-5 Family & v1 API
GPT-5, GPT-5-mini, and GPT-5-nano land in Azure OpenAI alongside a simplified v1 API surface that drops the mandatory api-version parameter.
Where enterprises deploy Azure OpenAI
Enterprise Copilots
Custom assistants embedded in internal tools, extending the same Copilot experience employees already use in Microsoft 365.
Contact Center Intelligence
Real-time call summarization, sentiment detection, and agent-assist copilots layered onto existing telephony and CRM systems.
Code Generation at Scale
GitHub Copilot Enterprise and custom coding assistants built on the same GPT-5 and o-series models available through Azure OpenAI.
Document Intelligence & RAG
Retrieval-augmented generation over contracts, policies, and knowledge bases using Azure AI Search alongside Azure OpenAI embeddings.
See how it stacks up
Bedrock vs. Azure OpenAI vs. Gemini
The full three-way comparison — model breadth, pricing model, and governance — plus an interactive model explorer.
Compare all three →Google Gemini Enterprise
Gemini 3.5, Agent Builder, and Google's newly rebranded enterprise AI platform, explained in the same depth.
Read the Gemini guide →Frequently asked questions
Planning an Azure OpenAI deployment?
We'll assess your workloads, right-size PTU vs. pay-as-you-go, and handle the migration from your existing OpenAI integration.