Azure AI Search - The Search That Doesn't Suck

Azure AI Search is Microsoft's managed search service that actually works pretty well. Originally called "Azure Search" when it launched in 2014, Microsoft renamed it to "Azure Cognitive Search" in 2019 because everything had to be "cognitive" back then. Then in late 2023, they renamed it again to "Azure AI Search" because now everything has to be "AI." Same service, just marketers doing their thing.

It handles two main things: regular search (like finding documents in your company's knowledge base) and the new RAG stuff everyone's building for chatbots. Unlike basic search that just matches keywords, this thing can actually understand what you're looking for and works with any kind of content - PDFs, Word docs, images, whatever.

Core Architecture

It's basically a distributed search cluster that Microsoft manages for you. There are different tiers - Free for testing (gets you 15GB, which fills up after indexing about 3 real documents), Basic for small stuff (~$75/month), Standard tiers (S1, S2, S3) for real workloads, and Storage Optimized (L1, L2) when you need to store massive amounts of data and don't care about query speed.

The nice thing is it hooks into other Azure services without the usual Microsoft integration hell. If you're already using Azure OpenAI or other AI services, everything talks to each other without having to build custom connectors or debug OAuth flows for 3 hours. But here's the catch - you're pretty locked into the Azure ecosystem once you go down this path. Want to switch to AWS later? Good luck with that data migration.

The AI Stuff That Actually Works

The main difference from basic search is the AI processing pipeline. It comes with about 15 built-in skills that can extract text from images (OCR actually works better than expected), detect languages, analyze sentiment, and find entities in your documents. You can also hook up your own custom models if the built-ins aren't cutting it.

Vector search is where shit gets interesting for modern AI apps. It uses HNSW algorithms (good luck pronouncing that without sounding like you're sneezing) to find semantically similar content. Instead of just matching keywords like it's 1995, it can find documents that mean the same thing even if they use completely different words.

Here's where you'll get burned: vector dimensions matter. Most people throw 1536-dimension vectors from OpenAI embeddings at it and wonder why performance tanks. We had to drop to 512 dimensions and retrain our embeddings to get decent query times. Also, HNSW parameters (efConstruction, M) are basically magic numbers - the defaults suck for most real workloads.

Companies are using it for knowledge bases, product search, legal document retrieval, and those chatbots that actually know what they're talking about. The adoption is pretty solid across industries, though Microsoft obviously isn't going to tell you about the failed implementations.

Getting Your Data In (And the Pain Points)

You can either push data to Azure Search or let it pull from your databases. The pull method works with about 15 different data sources - SQL, Cosmos DB, Blob Storage, SharePoint, etc. Here's what they don't tell you: the SharePoint indexer breaks randomly on weekends and the Cosmos DB indexer chokes on complex nested JSON.

The AI enrichment pipeline is actually pretty decent. It can extract text from images (OCR works better than expected), detect languages, and find entities in your documents. The new integrated chunking and vectorization saves you from building your own preprocessing pipeline, but it's not magic - you still need to tune chunk sizes or your search relevance will be garbage.

Language support is solid with 50+ languages, though the quality varies wildly. English and major European languages work great. Everything else... test thoroughly before committing to production.

Query Capabilities (The Good and the Ugly)

You can mix full-text search with vector search in the same query, which is actually pretty slick. Traditional keyword matching works like you'd expect, and the vector search uses HNSW algorithms for semantic similarity. Hybrid queries let you combine both approaches, though tuning the weight balance between text and vector results will make you question your life choices.

Query syntax supports both simple OData filters and full Lucene syntax. The OData stuff is fine for basic filtering, but if you need complex searches with fuzzy matching or proximity searches, you'll be diving into Lucene query hell. Vector queries work well for single vectors, but multi-vector scenarios get complicated fast.

Geographic search works for basic location queries, but don't expect PostGIS-level sophistication. Faceted navigation is decent for e-commerce use cases, though the performance tanks when you have too many facets or large result sets.

Security (Works Great Until It Doesn't)

RBAC integration with Azure AD works smoothly if you're already in the Microsoft ecosystem. Document-level security is powerful but hits performance hard - expect 30-50% slower queries when you enable per-user filtering. Also, the permission system breaks in weird ways if you have complex nested groups.

Data encryption is automatic and you can bring your own keys if you're paranoid. Network security includes firewall rules and private endpoints, though setting up private endpoints properly took us three attempts because the documentation is garbage.

The compliance certifications (SOC 2, ISO 27001, FedRAMP, HIPAA BAA) are solid if you need them for enterprise sales. Just don't assume they cover every edge case of your specific compliance requirements.

Performance and Scaling Reality

Basic tier is single-replica only, so don't use it for anything important - no high availability, no performance scaling. Standard tiers let you add replicas and partitions, but scaling costs add up fast. Storage Optimized tiers (L1, L2) can handle up to 2TB per partition, which sounds great until you realize query performance degrades significantly as you approach those limits.

Scoring profiles for relevance tuning are powerful but a nightmare to debug. We spent weeks tweaking profiles only to discover our relevance was still shit because we hadn't properly weighted our boost functions. Result caching helps with repeated queries, but cache invalidation during index updates will bite you.

Vector search quantization can improve performance but reduces accuracy - yet another trade-off you'll need to test thoroughly. The 99.9% SLA sounds good but requires two replicas minimum, which doubles your costs. Also, availability zones aren't available in all regions, so check before you commit.