Microsoft Azure AI is a portfolio, not one all-purpose service. Use a task-specific Foundry Tool for capabilities such as translation, speech, text analysis, or document extraction; Azure AI Search to retrieve relevant material from a collection; a model in Microsoft Foundry to generate or reason over content; Foundry Agent Service to connect a model with tools and knowledge; and Azure Machine Learning when you need to build or train a bespoke model.
The right starting point depends on the input and output you need. The distinction that prevents many wrong turns is that retrieval, generation, and custom model training are different jobs, even when they appear together in one application.
What are Microsoft Azure AI services?
Microsoft’s current documentation groups prebuilt and customizable APIs and models under Foundry Tools. The listed capabilities include Speech, Translator, Language, Content Understanding, Document Intelligence, Vision, Azure AI Search, Content Safety, Custom Vision, and Immersive Reader. These tools address different tasks; they are not interchangeable versions of a general-purpose AI model. Microsoft Foundry is the broader platform terminology for working with models, agents, and tools. Older Azure AI and Cognitive Services pages may use different names, so check the current service page when following setup instructions. Microsoft’s Foundry Tools overview and Foundry overview describe the current grouping.
Which Azure AI service should you use?
Choose by the work your application must perform, not by the word “AI” in a product name. These are useful starting points; a workload may combine more than one service.
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| Your task | Good starting point | What it does |
|---|---|---|
| Analyze text for sentiment, key phrases, entities, summaries, classification, language, question answering, or conversational intent | Azure Language | Provides targeted natural-language capabilities. For document retrieval, use Azure AI Search; for translation, use Translator. Microsoft’s Language overview outlines its capabilities and distinctions. |
| Translate text or documents | Azure Translator | Supports real-time text translation, batch or single-file document translation, and custom translation for specialized terminology. Translator documentation describes the options. |
| Extract fields, tables, or structure from forms and documents | Azure Document Intelligence | Offers prebuilt document models and options for custom extraction models. See the Document Intelligence overview. |
| Extract schema-defined information from varied documents or media using natural-language descriptions | Azure Content Understanding | Consider it when a suitable prebuilt Document Intelligence model does not fit, or when a workflow needs confidence scores, grounding, or Markdown suitable for retrieval-augmented generation (RAG). See the Content Understanding overview. |
| Transcribe audio, synthesize speech, translate speech, or recognize speakers | Azure Speech | Provides speech-to-text, text-to-speech, speech translation, and speaker recognition capabilities. The Speech overview describes the service. |
| Analyze images or video | Azure Vision; consider Content Understanding for broader media extraction | Microsoft’s guidance treats Vision and Content Understanding as options for image and video processing. The right fit depends on the desired output and workflow. Vision overview. |
| Search documents or retrieve relevant passages for a conversational application | Azure AI Search | Indexes and retrieves content. It can supply relevant material to a separate model that generates an answer; it is not itself the generative model. See Microsoft’s RAG guidance. |
| Check user-generated or AI-generated text and images for harmful or unwanted content | Content Safety | Provides content moderation capabilities. Confirm its current product placement and availability for your target deployment in the Content Safety overview. |
| Generate, summarize, reason over, or understand content with a foundation model | Azure OpenAI or another suitable model in Foundry Models | Provides managed access to models; choose a specific model based on its documented capability and current availability. See the Foundry overview. |
| Build an agent that uses a model with tools or knowledge | Foundry Agent Service | Hosts agents connected to a model and, optionally, custom knowledge stores or APIs. Review the Agent Service overview. |
| Train a bespoke model or customize beyond a prebuilt tool’s capabilities | Azure Machine Learning | Supports custom machine-learning work when a prebuilt capability does not meet the requirement. It typically calls for more machine-learning expertise than using a prebuilt API. See the Azure Machine Learning overview. |
How to choose a service for your workload
- Define the output. Decide whether the application must extract document fields, translate text, transcribe audio, classify text, label images, retrieve relevant content, or generate new content. Start with the output because similar inputs can require different services.
- Match the task to a prebuilt tool. If a documented Foundry Tool directly fits, it is usually a more straightforward starting point than building and operating a custom model. Some services also support customization. Microsoft’s Foundry Tools guidance describes the available capability families.
- Keep retrieval separate from generation. Azure AI Search indexes and retrieves relevant content; a language model generates or reasons over content. For answers grounded in private documents, assess both retrieval quality and model behavior rather than expecting the model alone to search your corpus. Microsoft’s RAG guidance covers this pattern.
- Choose custom machine learning for a real gap. Consider Azure Machine Learning when prebuilt capabilities cannot meet the requirement. Weigh the benefit of tailored behavior against the additional data preparation, expertise, operations, and governance involved.
- Check deployment specifics before implementation. Verify service and feature availability in the intended region, model availability, pricing and quota, API version, data handling, security controls, and retirement notices. These details vary by service, model, and deployment and are not settled by a portfolio-wide overview.
How Azure AI Search, Azure OpenAI, and Azure Machine Learning differ
These options often appear in the same project, but solve different parts of it. Azure AI Search retrieves relevant content from a collection. Azure OpenAI or another Foundry model can generate or reason over content. Azure Machine Learning is the custom-model path when you need to train or develop behavior beyond what a prebuilt tool supports. In a document-question-answering application, for example, Search can retrieve relevant passages and a model can formulate a response based on them; custom machine learning is not automatically required.
For a grounded conversational application, evaluate retrieval and generation as separate components. A model’s ability to produce an answer does not establish that it has searched or accurately retrieved your private documents. Add safety and evaluation measures appropriate to the application rather than assuming the model alone covers those needs. Microsoft’s generative AI technology guidance discusses the distinct roles.
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What to verify before choosing a service
Product names and capabilities are only part of the decision. Before committing to an implementation, check the chosen service’s current documentation for:
- Availability of the service, feature, and model in the target Azure region, including any data-residency needs.
- Supported languages, input formats, and file-size or request constraints for the workload.
- Pricing, quotas, expected volume, and latency requirements.
- Current API version, model lifecycle, and any retirement notices.
- Identity, network isolation, data handling, safety, and monitoring controls.
These details can change and cannot be inferred from the product family name. Consult the current service-specific Microsoft documentation before deployment.
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