The Azure OpenAI Service gives you access to powerful language models like GPT-5, GPT-4.1, GPT-4o, and Codex in a secure, enterprise-ready environment. Running these models on Microsoft Azure helps you meet needs around security, regional deployment, private networking, and integration with other Azure tools.

Azure OpenAI Service supports use cases like building agents, copilots, or integrating generative AI into internal systems. It offers flexibility and control, without requiring teams to manage the underlying infrastructure.

What Is Azure OpenAI Service?

Azure OpenAI Service provides API-based access to OpenAI’s large language models via Azure infrastructure. As part of a broader suite of AI services, it enables automation and advanced applications such as natural language processing, code generation, summarization, and semantic search.

Here is a comprehensive list of all the models supported by Azure OpenAI:

Reasoning and Chat Models (GPT-5, GPT-OSS & O-series)

Reasoning and Chat Models (GPT-4.1, GPT-4o & GPT-3.5)

Specialized Models, Multimodal & Developer Tools

Embeddings, Image, Video & Audio Models

Enterprise-ready Foundry Models and Azure AI Foundry Models are available within the Azure AI Foundry platform, providing advanced capabilities with straightforward integration and customization for a variety of AI applications.

These models can be accessed through REST APIs or Azure SDKs. Configuration options include system messages, function calling, temperature, and stop tokens, with the ability to integrate external tools such as Azure AI Search or Azure Functions.

Key Features and Capabilities

• Latest model lineup with modality support - Azure OpenAI provides access to the GPT-5, o-series reasoning models (including o3, o3-mini, o4-mini), and GPT‑4o/Mini - supporting text, image, and audio input. These models cater to a full range of use cases from code generation to multimodal agents, including AI chat applications that interact with users through natural language and multimodal inputs.
• Chain-of-thought reasoning with o-series - Reasoning models like o3, o1, and o4-mini implement chain-of-thought processing, breaking down complex tasks into step-by-step reasoning. This improves performance in structured domains like mathematics, planning, and logical inference, while improving safety behavior.
• Fine-grained capability routing (model-router) - The model-router automatically selects the best underlying model for a given request based on workload, response type, or prompt structure. Deployments can be tailored for specific tasks and limited, predefined purposes, helping with cost and efficiency optimization.
• Multimodal understanding (GPT-5, GPT‑4o and related) - GPT-5, GPT‑4o and GPT‑4o Mini support multimodal input: combined analysis of text, images, and audio. They power advanced interfaces like vision-enabled chat and voice agents in a single model call. These models can process audio content and speech, including speech recognition and synthesis, enabling integration of audio content into AI workflows for enhanced media and communication solutions.
• Embeddings & Retrieval-Augmented Generation (RAG) - Embedding models are available to process text into dense vectors for semantic similarity, RAG workflows, and knowledge retrieval over private or indexed data sources.
• On Your Data (guided retrieval) - Use the On Your Data pipeline to connect private content sources (e.g., SharePoint, Cosmos DB, Blob Storage) to the OpenAI engine for source-grounded responses. Azure AI Search handles chunking and indexing, while the OpenAI model uses the resulting context during inference.
• Fine-tuning support (preview) - Custom model fine-tuning, available for GPT‑4o Mini (and selected o-series models), is now in preview, enabling domain-adapted versions of core models with improved accuracy and behavior customization.
• SDK and API support - Azure OpenAI supports REST APIs and SDKs in Python, C#, Java, Go, and JavaScript compatible with Azure’s standard control-plane lifecycle (v1 API, batching, deployments).
• Enterprise-grade integration - Services integrate with Azure Entra ID, RBAC, Private Link, and Azure Policy. Automatic model updates, governance hooks, and deployment-level content filtering are available for secure production setups.
• Text and image generation - Models support the use of text prompts to guide outputs, enabling generation of images, speech, and other content from user-provided instructions.

Everything runs within your Azure subscription and can be integrated into virtual networks, secured with RBAC, and monitored using Azure-native tools. Users can interact with AI models through various interfaces, enabling automation and enhanced user experiences.

Getting Started with Azure OpenAI

Getting started with Azure OpenAI is straightforward for both developers and businesses. First, create an Azure account to access the full range of Azure services. From the Azure portal, request access to the Azure OpenAI Service. Once approved, set up a dedicated Azure OpenAI resource where you can configure network security, enable private access if needed, and apply tags for management and compliance.

With the resource in place, you can use advanced models such as GPT-5, GPT-4.1, GPT-4o, and GPT-image-1. These models support a wide range of scenarios, including natural language processing, text generation, image creation, and conversational AI. Applications can range from AI-powered agents and copilots to workflow automation and customer-facing tools.

The Azure OpenAI Service is built for accessibility, security, and scalability, giving organizations a practical way to adopt AI and create solutions that match their operational and innovation goals.

Common Use Cases

1. Internal Agents and Copilots

Build assistants that handle HR questions, IT troubleshooting, or financial workflows. Combine GPT-5 with Azure AI Search to ground responses in internal documentation. Conversational agents can facilitate natural language interactions for HR, IT, or finance, enabling users to get answers and complete tasks efficiently.

2. Customer Support Automation

Deploy AI agents that understand user questions, escalate complex cases, and summarize interaction history. Use embeddings to enable search across knowledge bases. These solutions help customers by providing faster, more tailored support experiences, improving satisfaction and efficiency.

3. Content Generation and Summarization

Generate long-form content, product descriptions, or extract key insights from reports, legal documents, and support logs. AI can also be used for translation tasks, supporting multilingual content and communication to reach a global audience.

4. Code Generation and Developer Tools

Use Codex-based models to build IDE assistants, code translators, or internal tooling for engineering teams.

5. Voice and Multimodal Interfaces

Use GPT-5 for tasks that require interpreting text, images, or audio, such as smart assistants, document reviews, or multimodal chatbots. Predictive analytics and real-time insights can optimize operations in industries such as manufacturing, enhancing productivity and efficiency.

Why Use Azure OpenAI Service Instead of the Native OpenAI API?

• Data Residency and Regional Deployment - Azure allows deployment of OpenAI models in specific geographic regions. This supports data localization policies and reduces cross-border data transfer risks.
• Identity and Access Management - Azure OpenAI integrates with Microsoft Entra ID (formerly Azure AD). You can use managed identities to authenticate, no separate API key storage, and enforce role-based access control (RBAC) at the resource and model level.
• Private Networking - The service supports VNet integration and Azure Private Link, allowing you to restrict inference traffic entirely within your network boundary and block public internet access.
• Compliance and Certification - Azure OpenAI is covered under Microsoft’s compliance framework, including ISO/IEC 27001, SOC 1/2/3, GDPR, HIPAA, and FedRAMP High.
• Monitoring and Policy Enforcement - Usage is logged via Azure Monitor and Log Analytics. Teams can track request volume, latency, errors, and model usage. Azure Policy and Defender for Cloud are allowing enforcement of resource location, quota limits, and permitted model types.
• Cost Controls and Forecasting - Token usage is visible in Azure Cost Management. Budgets, alerts, and historical trends can be applied at the subscription or resource group level to manage consumption. Azure OpenAI uses a pay-as-you-go pricing structure, so you only pay for the resources you consume, making it cost-effective and flexible for different needs.
• Service Integration - Azure OpenAI connects directly with Azure AI Search, Azure Functions, Azure Key Vault, and other platform services.
• Support and SLAs - The service benefits from Microsoft’s enterprise support and service-level agreements, including region-based support options and escalation management.
• Deployment and Version Control - You control which models are deployed and when they are updated. New model versions can be tested in staging deployments before moving to production.

Pricing Overview

Azure OpenAI Service uses token-based billing. You’re charged per million tokens consumed, both input and output, and rates vary depending on the model.

Here’s a list of all the models and their respective pricing.

Azure OpenAI Pricing

Below are the prices for all Azure OpenAI models and features at the time of writing. While we strive to keep our articles up to date, some changes may occur that are not yet reflected here. Please refer to the official Azure OpenAI Services page for the most current pricing information.

Reasoning and Chat Models

GPT-4.1 Series

GPT-4o & GPT-4o Mini

Image Models

Embedding Models

Audio & Realtime Models

Quick Summary of Other Pricing Options

• Token calculation includes system messages, instructions, chat history, and generated content.
• Batch API usage offers ~50% discount, but is asynchronous and subject to job processing delays.
• Cached prompt tokens may be billed at discounted rates depending on reuse and model.
• PTUs (Provisioned Throughput Units) provide reserved capacity with lower per-token prices for high-volume, predictable workloads.
• Regional availability of models may vary. Codex‑mini is currently available in select regions.
• Token counts typically map to ~4 characters or ~0.75 words in English. A 1,500-word document ≈ 2,000 tokens.

Use the Azure Pricing Calculator for accurate region-specific estimates.

Considerations Before You Deploy

• Latency and Real-Time Performance - GPT-4.1 Nano and GPT-4o Mini offer the lowest latency and fastest response times. Microsoft recommends GPT-4o Mini for latency-sensitive audio workflows, and benchmarks indicate GPT-4.1 Nano returns the first token in under 5 seconds with large context inputs (~128K tokens), far faster than GPT‑4.1 full model. GPT-4.1 Mini is a mid-tier option with lower latency than GPT‑4.1 but slightly slower than Nano.
• Prompt Efficiency and Token Limits - Use concise system messages and streamlined context to minimize token usage. Overshooting context limits (e.g., >300K tokens) can trigger errors even though models support up to 1M tokens. Best practice: keep prompt content focused, reuse cached instructions, and break workflows into pipeline steps when necessary.
• Model Lifecycle and Version Changes - The GPT-5 and GPT-4.1 family, including GPT-4.1, Mini, and Nano, replaced legacy models like GPT-4 Turbo and GPT-3.5. Tools such as Azure portal notifications and the Models List API help you track model availability and deprecations.
• Security and Secrets Management - Store tokens and keys in Azure Key Vault. Enable logging via Azure Monitor and use private endpoints to restrict model inference traffic within your network boundary. Audit all usage via Log Analytics and enforce Azure Policy to control deployment scope and model access.
• Quotas and Throttling Control - Each model has rate limits per region and subscription (tokens per minute, requests per minute). For example, GPT-4.1 standard deployments typically allow up to 5M TPM (tokens/min) and 5,000 RPM (requests/min) per region. Track usage especially in multi-region setups or under burst traffic loads.
• Cost Optimization - For recurring high-volume workloads, use the Batch API (~50% cheaper for asynchronous jobs). Prompt caching discounts can reduce token charges by up to 75% for repeated inputs. Use Provisioned Throughput Units (PTUs) or reserved capacity for predictable predictability and volume discounts.

About Us

At ITMAGINATION, we’ve been delivering AI and Machine Learning solutions since 2016, well before the recent surge in generative AI adoption. This early start has given us the experience to navigate both the technical and strategic aspects of AI at scale.

In the past two years, we’ve expanded our generative AI capabilities and delivered multiple projects that are already creating measurable business impact for our clients. With Azure OpenAI Services, we combine Microsoft’s enterprise-grade AI infrastructure with our proven expertise to help organizations move from experimentation to production-ready solutions faster, more securely, and with a clear path to value.

Book a call with our team of experts to explore how Azure OpenAI Services can accelerate your AI roadmap and deliver tangible results for your business.