RAG-as-a-Service

Retrieval-Augmented Generation.

End-to-end RAG pipeline. Ingest documents, auto-chunk, embed, store in vector DB, and query with LLMs. Zero infrastructure to manage.

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Millions

Docs

< 50 ms

Retrieval

Any model

LLMs

Auto

Chunking

RAG, managed.

Ingest → Embed → Retrieve → Answer.

Auto-ingest

PDF, Word, HTML, Markdown, and 30+ file formats.

Sub-50ms retrieval

Vector search with reranking in under 50ms.

Smart chunking

Semantic chunking with overlap. Context-aware splitting.

Any LLM

Works with GPT-4, Claude, LLaMA, or your own models.

guardrails

Built-in hallucination detection and citation generation.

Hybrid search

Combine vector similarity with keyword BM25 search.

Getting started

Launch your first instance in three steps. CLI, console, or API — your choice.

Terminal

ur ai rag create docs-qa \
  --embedding=ada-002 \
  --llm=gpt-4

RAG patterns.

Customer support and legal Q&A.

Customer support AI

Answer customer questions from knowledge base docs.

View tutorial

Suggested configuration

RAG · Guardrails · Citations

Estimate your costs

Create detailed configurations to see exactly how much your architecture will cost. Pay for what you use, down to the second.

Configuration 1

Estimated: $35.20/mo

RAG Pipeline

LLM Backend

Usage Volume

Queries (K/mo)

Indexed Documents (K)

Infrastructure

Replica Count

Model / Data Storage (GB)

Options

Premium SLA (99.99%)+25% for guaranteed availability

Config 1 cost$35.20

Cost details

$35.20

Ingest → Chunk → Embed → Store → Query. Zero infra.

Configuration 1

$35.20

2× standard Replica(s)$29.20

Request Processing$1.00

Storage$5.00

Works seamlessly with

LLM API

Vector DB

IAM

Logging

Analytics

Frequently asked questions

RAG, managed.

Ingest → Embed → Retrieve → Answer. Zero infra.

Create pipeline View docs