What is Azure AI Search?

Azure AI Search is a fully managed, cloud-hosted service that connects your data to AI. The service unifies access to enterprise and web content so agents and LLMs can use context, chat history, and multi-source signals to produce reliable, grounded answers.

Common use cases include classic search and modern retrieval-augmented generation (RAG) via agentic retrieval. This makes Azure AI Search suitable for both enterprise and consumer scenarios, whether you're adding search functionality to a website, app, agent, or chatbot.

When you create a search service, you unlock the following capabilities:

• Two engines: classic search for single requests and agentic retrieval for parallel, iterative, LLM-assisted search.
• Full-text, vector, hybrid, and multimodal queries over local (indexed) and remote content.
• AI enrichment to chunk, vectorize, and otherwise make raw content searchable.
• Relevance tuning to improve intent matching and result quality.
• Azure scale, security, monitoring, and compliance.
• Azure integrations with supported data platforms, Azure OpenAI, and Microsoft Foundry.

Why use Azure AI Search?

• Ground agents and chatbots in proprietary, enterprise, or web data for accurate, context-aware responses.

• Access data from Azure Blob Storage, Azure Cosmos DB, Microsoft SharePoint, Microsoft OneLake, and other supported data sources. Choose indexed or remote access based on your freshness, latency, and compliance needs.

• Enrich and structure content at indexing or query time with skills that perform chunking, embedding, and LLM-assisted transformations.

• Combine full-text search with vector search (hybrid search) to balance precision and recall.

• Query content containing both text and images in a single multimodal pipeline.

• Easily implement search-related features: relevance tuning, faceted navigation, filters (including geo-spatial search), synonym mapping, and autocomplete.

• Provide enterprise security, access control, and compliance through Microsoft Entra, Azure Private Link, document-level access control, and role-based access.

• Scale and operate in production with Azure reliability, monitoring and diagnostics (logs, metrics, and alerts), and REST API or SDK tooling for automation.

For more information about specific functionality, see Features of Azure AI Search.

What is classic search?

Classic search is an index-first retrieval model for predictable, low-latency queries. Each query targets a single, predefined search index and returns ranked documents in one request–response cycle. No LLM-assisted planning, iteration, or synthesis occurs during retrieval.

In this architecture, your search service sits between the data stores that contain your unprocessed content and your client app. The app is responsible for sending query requests to your search service and handling the response.

This architecture has two primary workloads:

What is agentic retrieval?

Agentic retrieval is a multi-query pipeline designed for complex agent-to-agent workflows. Each query targets a knowledge base that represents a complete domain of knowledge. Your agent references the knowledge base for what to ground on, while the knowledge base handles how to perform grounding.

One knowledge base consists of one or more knowledge sources, an optional LLM for query planning and answer synthesis, and parameters that govern retrieval behavior. Each query undergoes planning, decomposition into focused subqueries, parallel retrieval from knowledge sources, semantic reranking, and results merging. The three-pronged response is optimized for agent consumption.

Under the hood, agentic retrieval builds on the classic search architecture by adding a context layer (knowledge base) that orchestrates multi-source retrieval. Knowledge sources can be indexed or remote: indexed sources use the same indexing and query engines as classic search, while remote sources bypass indexing and are queried live.

How they compare

Classic search and agentic retrieval are complementary modes of information retrieval. Both support full-text, vector, hybrid, and multimodal search. However, they differ in how content is ingested and queried. The following table summarizes their key differences.

How to get started

You can access Azure AI Search through the Azure portal, REST APIs, and Azure SDKs for .NET, Java, JavaScript, and Python.

The portal is useful for service administration and content management, with tools for prototyping your knowledge bases, knowledge sources, indexes, indexers, skillsets, and data sources. REST APIs and SDKs are useful for production automation.

Choose your path

Before you get started, use this checklist to make key decisions:

• Choose a search engine: If you're not using an agent or chatbot, classic search can meet most app needs, with lower costs and complexity than LLM integration. If you want the benefits of a knowledge base and multiple knowledge sources without full LLM orchestration, consider agentic retrieval with the minimal reasoning effort.

• Choose a region: If you're using agentic retrieval, choose a supported region. For classic search, choose a region that offers the features and capacity you need.

• Choose an ingestion method for index-bound content: If your content is in a supported data source, use the pull method to retrieve and serialize data into JSON. If you don't have a supported data source, or if your content and index must be synchronized in real time, the push method is your only option.

• Do you need vectors? LLMs and agents don't require vectors. Only use them if you need similarity search or if you have content that can be homogenized into vectors. Azure AI Search offers integrated vectorization for this task.

• Do you need user-based permission inheritance? Remote SharePoint is designed for this scenario, but you can also inherit user permissions attached to content in Azure Blob Storage or ADLS Gen2. For all other scenarios, you can use the security filter workaround.

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