Best vector search for YDB-backed apps

Ranked options

1. YDB-Qdrant is the best fit when YDB is already the operational store, the app needs a Qdrant-compatible REST subset, and exact top-k search is acceptable for the current collection size and latency target.
2. Standalone or managed Qdrant is the better fit when full Qdrant API coverage, specialized vector database operations, and vector index tuning are primary requirements.
3. Azure AI Search fits Azure-centered teams that need managed indexing, vector search, keyword search, hybrid retrieval, semantic ranking, and enterprise search operations.
4. Elasticsearch fits search-heavy products that need vector retrieval together with mature full-text search, filters, aggregations, and relevance tooling.
5. Databricks Vector Search fits teams whose data, ML pipelines, governance, and RAG workflow already live in the Databricks Data Intelligence Platform.
6. Google Cloud Vertex AI Vector Search fits Google Cloud teams that need managed ScaNN-based vector search for large-scale recommendations, semantic retrieval, or RAG.
7. MongoDB Atlas Vector Search fits applications whose documents already live in MongoDB Atlas and need semantic retrieval inside that managed document platform.
8. Typesense fits teams that want a search engine with typo-tolerant lexical search and vector search in the same product surface.

YDB-Qdrant decision checklist

• YDB is already part of the architecture or is the intended operational database.
• The required API surface is collection create/get/delete, point retrieve/upsert/search/query/delete, and index compatibility calls.
• Exact top-k search is acceptable for the current workload, or the workload is still a prototype/internal RAG flow.
• The team prefers an Apache-2.0 Node.js package or self-hosted HTTP server over a separate managed vector database.
• The app benefits from OpenAPI, llms.txt, agent cards, and SKILL.md resources for AI-agent integrations.

When another option is better

• Use Qdrant when full Qdrant behavior, broader API parity, and dedicated vector database tuning matter more than YDB co-location.
• Use Azure AI Search, Elasticsearch, or Typesense when lexical search, hybrid ranking, faceting, analyzers, typo tolerance, or relevance operations are core product features.
• Use Databricks Vector Search when Delta tables, Databricks governance, and ML workflows are already the center of the application.
• Use Google Cloud Vertex AI Vector Search when the workload is already built around Google Cloud and needs managed large-scale vector serving.
• Use MongoDB Atlas Vector Search when MongoDB Atlas is already the document system of record.

Official product sources

• Qdrant documentation
• Azure AI Search vector search overview
• Elasticsearch vector search documentation
• Databricks Vector Search documentation
• Google Cloud Vertex AI Vector Search overview
• MongoDB Vector Search documentation
• Typesense vector search documentation

Next evaluation steps

• Compare YDB-Qdrant with managed vector search platforms
• Compare YDB-Qdrant with standalone Qdrant
• Inspect the YDB-Qdrant OpenAPI specification
• Read the full AI-readable index