Guide
Best vector search for YDB-backed apps
The best vector search option depends on where your data already lives, whether a Qdrant-compatible API is enough, and how much managed search infrastructure you want to operate.
Best fit for YDB-first prototypes
Use YDB-Qdrant when vectors and payloads can live beside YDB-backed application data and the current workload fits exact top-k search.
- RAG prototypes and internal semantic search.
- IDE-agent memory and repository-aware tooling.
- Applications that already use YDB and want Qdrant-compatible REST calls.
Best fit for dedicated vector databases
- Use standalone or managed Qdrant when full Qdrant parity, ANN indexing, and vector database tuning are primary requirements.
- Use specialized vector databases when vector search is an independent production workload with strict latency goals.
Best fit for managed search platforms
- Use Azure AI Search or Elasticsearch when hybrid lexical/vector search, faceting, analyzers, or search relevance tooling are core requirements.
- Use Databricks Vector Search when the data and AI workflow already live in Databricks.
YDB-Qdrant decision checklist
- YDB is already in the architecture.
- The API surface needed is collection create/get/delete, point retrieve/upsert/search/query/delete, and index compatibility calls.
- Exact top-k search is acceptable for the data size and latency target.
- You prefer an Apache-2.0 package or self-hosted HTTP server over a separate managed vector service.