Azure AI Search feels too locked into Microsoft stack

I get that. Azure AI Search can feel pretty “Azure-native,” especially if you want something more portable or less tied to Microsoft services.

A few common reasons people feel locked in:

• Data/source integration leans toward Azure services
• Semantic ranking / vector search is convenient but tied to the Azure ecosystem
• Management and tooling are very portal-driven
• Auth/governance patterns often assume Entra ID, Azure networking, etc.

If your goal is to reduce vendor lock-in, you have a few options:

1. Use Azure AI Search only as an indexing layer

Keep your app logic separate and treat search as an external service. That makes it easier to swap later.

2. Abstract behind your own search interface

Build a small internal API like:

• search(query)
• getDocument(id)
• upsertDocuments(...)

Then your application doesn’t depend directly on Azure-specific SDKs.

3. Consider more portable alternatives

Depending on what you need:

• Elasticsearch / OpenSearch – broad ecosystem, strong full-text search
• Meilisearch / Typesense – simpler, lightweight, developer-friendly
• Postgres + pgvector / full-text search – good if you want fewer systems
• Pinecone / Weaviate / Qdrant – especially if vector search is the main need

4. Separate retrieval from orchestration

If you're doing RAG, you can keep embeddings, chunking, and reranking in your own layer and use almost any backend search engine underneath.

5. Be careful with Azure-specific advanced features

Things like semantic configurations, skillsets, indexers, and integrated AI enrichment are useful, but they increase coupling.

If you want, I can help you compare:

• Azure AI Search vs OpenSearch
• Azure AI Search vs PostgreSQL + pgvector
• A low-lock-in architecture for RAG/search