A vector database alternative for RAG (skip the infrastructure)

What a vector database actually does

A vector database stores and searches embeddings by similarity. That's genuinely useful — but it's a component, not a solution. On its own it doesn't convert your documents, chunk them well, rerank results, generate answers, or cite sources. You build all of that around it.

• Stores embeddings and finds nearby vectors — fast similarity search
• Doesn't convert, chunk, rerank, generate, or cite — that's on you
• One layer of a RAG stack, not the whole thing

The hidden cost of running one

Standing up a vector database is the easy part. Keeping a production RAG system healthy means an ingestion and embedding pipeline, a chunking strategy you tune, a reranker for precision, re-embedding when documents change, and monitoring — plus the LLM bill for generation. That's real engineering time before you answer a single question well.

• Embedding pipeline + re-embedding on every document change
• Chunking and retrieval tuning to get precision right
• A separate reranker, or you settle for vector-only results
• Ops, sync, and cost — on top of your LLM tokens

Do you need a vector database?

If you're researching vector infrastructure, you're building a pipeline. If your actual goal is “let my app or agent answer questions from my documents, with citations,” a managed knowledge base gets you there without owning any of that infrastructure.

• Building custom retrieval infra? A vector DB is one part of it.
• Want grounded, cited answers over your docs? You want a managed KB.
• Most teams want the outcome, not the pipeline.

The managed alternative: Kit for AI

Kit for AI handles the whole retrieval stack for you. Add documents or URLs; they're converted to clean Markdown, chunked, embedded, and indexed. Questions retrieve with hybrid search (vector + keyword) and cross-encoder reranking — more precise than vector similarity alone — and answers cite their source, on private local models. No vector database, no embedding pipeline, no chunking to manage.

• Zero infrastructure: no vector DB, embeddings, or chunking to run
• Hybrid retrieval + reranking beats vector-only precision
• Cited, grounded answers on private models — over API and MCP

FAQ

Do I need a vector database for RAG?

Only if you're building the retrieval pipeline yourself. If your goal is grounded, cited answers over your documents, a managed knowledge base like Kit for AI removes the need for a vector database, embedding pipeline, and chunking entirely.

Is Kit for AI a vector database alternative?

It's an alternative to running one for RAG. Kit for AI manages embeddings, indexing, hybrid retrieval, and reranking behind an API and MCP, so you get the outcome — cited answers — without operating vector infrastructure.

Is managed retrieval as good as a custom vector setup?

Often better out of the box: Kit for AI fuses vector and keyword search with RRF and reranks with a cross-encoder, which is more precise than the vector-similarity default most stacks ship with.

What about privacy?

Your documents are embedded and answered on our own local models — they aren't sent to a third-party LLM.