Azure AI Search
Definition
Microsoft's AI-powered search service with semantic ranking, vector search, and hybrid capabilities for intelligent search and RAG applications.
Use Cases
- Microsoft: Enterprise document search and retrieval-augmented generation (RAG) over internal knowledge (policies, product docs, support content). — Indexes documents into Azure AI Search, enriches content with AI skills (e.g., OCR and entity extraction) where needed, and uses vector + keyword hybrid search to retrieve passages that are then used as grounding context for an LLM-based assistant. (Faster information discovery for employees and more consistent, grounded answers in internal copilots by retrieving the most relevant content instead of relying on the model’s memory.)
- KPMG: Knowledge discovery across large volumes of professional services content (methodologies, guidance, and internal documents) to support consultants. — Uses Azure AI Search to index and semantically rank internal content; integrates search results into AI-assisted workflows so users can query in natural language and retrieve relevant passages and citations. (Improved findability of internal knowledge and reduced time spent searching for the right guidance, supporting more efficient delivery work.)
- CarMax: Improving customer search and discovery for vehicle listings and related content using natural language queries. — Indexes inventory and content into a managed search layer and applies relevance tuning; uses semantic-style ranking and hybrid approaches to better match user intent beyond exact keyword matches. (More relevant search results for shoppers, improving the browsing experience and helping customers find vehicles that match intent-driven queries.)
Provider Equivalents
- AWS: Amazon OpenSearch Service
- Azure: Azure AI Search
- GCP: Vertex AI Search
- OCI: OCI Search with OpenSearch
Frequently Asked Questions
- What's the difference between Azure AI Search and Azure OpenAI?
- Azure AI Search is a search engine: it stores indexes of your data and retrieves the most relevant documents using keyword, semantic, and vector search. Azure OpenAI provides large language models that generate text. In many RAG solutions, Azure AI Search finds the best source passages, and Azure OpenAI uses those passages to produce grounded answers with citations.
- When should I use Azure AI Search?
- Use it when you need high-quality search over your own content (documents, product catalogs, knowledge bases) with features like relevance tuning, filters/facets, semantic ranking, and vector or hybrid retrieval. It’s especially useful for RAG apps where you need reliable retrieval (and often citations) from enterprise data, not just keyword matching.
- How much does Azure AI Search cost?
- Pricing depends mainly on the service tier, the number of search units/replicas/partitions (capacity and availability), and enabled features (for example, semantic ranking and vector search capabilities may have tier requirements). Your overall cost is driven by index size, query volume, and how much capacity you provision to meet latency and uptime needs. Check the Azure AI Search pricing page for current tier details and regional rates.
Category: azure-services
Difficulty: intermediate
Related Terms
See Also