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Captain vs. Google RAG Engine

Edgar BabajanyanCTO & Co-Founder

5 min read

What you assemble, and what you get

Google's RAG Engine hands you a corpus, a parser, an embedding model, a vector store and a reranker, each provisioned and billed on its own, with hybrid search only through a Weaviate database you run. Captain indexes straight from the GCS bucket you already have and gives you hybrid search, reranking and layout-aware parsing in one API call.

Every control you would otherwise wire in yourself is already inside the query.

Why choose Captain over Google RAG Engine?

Captain ships hybrid retrieval, reranking, layout-aware parsing and continuous sync in one call. On RAG Engine each of those is a separate component: you wire them together, you pay for each one on its own line, and you keep them running.

Hybrid built in
BM25 and vectors fused in every query. RAG Engine gets hybrid search only through a Weaviate database you provision and run.
Reranking included
About 200 ms, inside the query. RAG Engine's rerankers are a second service, the ranking API or a Gemini call, each billed on its own.
Layout on every chunk
Seven roles, excludable at retrieval. RAG Engine's layout parser exposes none.
Synced from your storage
Continuous sync with reconciliation and rollback. RAG Engine copies files in by batch import, with no rollback.
Multimodal and spreadsheets
Images, audio, video and sheets indexed natively. RAG Engine lists documents only, images through the LLM parser; other types index with lower-quality results, in Google's words.
Any model
Ranked, cited chunks for any model. RAG Engine's Tool needs an Agent Platform model, and its LLM reranker and LLM parser need Gemini.

How the platforms compare

Retrieval control
Indexing & structure

Graph relations

Captain

Typed chunk-to-chunk edges

RAG Engine

No

Bounding boxes

Captain

Optional region data per chunk

RAG Engine

No

Data & operations

Where data lives

Captain

Indexed from your own storage

RAG Engine

Copied into a corpus on Spanner, Vector Search 1.0 or 2.0, Weaviate, Pinecone or Feature Store

Pricing model

Captain

Indexing and query credits; reranking included

RAG Engine

Separate lines: database tier, embeddings, parsers, reranker

Coverage & limits

Edgar Babajanyan

CTO & Co-Founder · Captain

Edgar is co-founder and CTO of Captain, building self-tuning file search for AI agents.