Ducky Review 2026

Ducky is fully managed AI search and RAG infrastructure — chunking, embedding, hybrid retrieval, and reranking behind a single API. The pitch is to skip the Pinecone + Cohere + LangChain glue and get a tuned retrieval pipeline in one HTTP call.

Ducky is a developer-facing 'RAG as a service' platform: you POST documents and it handles chunking, embedding, storage, hybrid (vector + keyword) retrieval, and reranking, returning ranked passages and citations ready to feed an LLM. The pitch is to skip the standard mid-size AI startup pain of choosing a vector database, an embedding model, a chunker, and a reranker — and then operating them as separate services — and to instead get a tuned pipeline behind one HTTP call. Ducky targets developers who are tired of LangChain + Pinecone + Cohere reranker glue code and want their RAG stack to be a one-line dependency. The product handles ingestion at scale, supports filters and metadata, and exposes both retrieval-only and full RAG-completion endpoints. It is most appealing to early-stage AI startups, internal tools teams, and agencies who need accurate retrieval without becoming search experts. Pricing is usage-based on storage and queries with a free tier for development; enterprise plans add dedicated capacity and SLAs. Compared to building on Pinecone or Turbopuffer directly, Ducky is higher-level and more opinionated.