# Zero-Infrastructure RAG Agent Deploys LegalTech FastAPI App > Source: > Published: 2026-06-16 10:49:50.316664+00:00 # Zero-Infrastructure RAG Agent Deploys LegalTech FastAPI App DigitalOcean's community tutorial demonstrates how to build a zero-infrastructure RAG agent for legal document retrieval using an all-DigitalOcean stack: **Spaces** for file storage, **Knowledge Bases** for managed vector indexing and retrieval, **MCP** to wire the knowledge base into any compatible agent framework, and **Serverless Inference** to run the LLM without provisioning GPUs. The result is a FastAPI application that answers questions over legal case files without the developer managing chunking, embedding models, or vector store infrastructure. LawVo, a LegalTech startup, reported via DigitalOcean's blog that this managed stack reduced their RAG pipeline setup from weeks to one day. The pattern is relevant for developers who want a production document Q&A app without assembling retrieval plumbing from scratch. ### What the Tutorial Covers DigitalOcean's June 2026 community tutorial walks developers through wiring four managed services into a single RAG pipeline for legal document Q&A. The stack: Spaces (S3-compatible object storage) for uploading case files, Knowledge Bases (GA) for automated ingest, chunking, embedding, and hybrid retrieval, MCP (Model Context Protocol) to expose the knowledge base as a one-line retrieval tool in any compatible agent, and Serverless Inference to run an LLM without provisioning a dedicated GPU. A FastAPI application wraps the retrieval and generation steps into a deployable HTTP API. ### Platform Context The tutorial is part of DigitalOcean's Gradient AI Platform positioning as a fully integrated stack for production RAG. Knowledge Bases reached general availability alongside DigitalOcean's April 2026 "AI-Native Cloud" announcement. Per DigitalOcean's published docs, pricing starts at $19.60 for a knowledge base with embedding tokens at $0.02 per million. The MCP integration means developers connect the knowledge base to any MCP-compatible agent framework with a single config line rather than writing custom retrieval glue code. ### LegalTech Precedent LawVo, a LegalTech startup, is cited by DigitalOcean as a production user of Knowledge Bases. Hovsep Seraydarian, Co-founder and CTO of LawVo, was quoted in DigitalOcean's Data & Learning blog (June 3, 2026): "Before DigitalOcean Knowledge Bases, we were looking at weeks of work to stand up a production RAG pipeline behind our LawvoAI offering -- vector DB, embeddings, chunking, the whole stack. With DigitalOcean, we had a citation-backed knowledge base running in a day." These outcomes are vendor-reported, not independently benchmarked. ### Practitioner Relevance The pattern applies to teams building document Q&A on domain-specific corpora -- legal filings, support docs, compliance manuals -- who want to avoid self-managing Weaviate, Qdrant, or a custom embedding pipeline. The tradeoff is tight coupling to DigitalOcean's proprietary services; portability to other clouds requires re-plumbing storage and retrieval layers. For teams already on DigitalOcean infrastructure, the zero-egress integration and single-invoice billing reduce both latency and operational overhead. ## Scoring Rationale A vendor tutorial demonstrating DigitalOcean's managed RAG stack (Knowledge Bases + MCP + Serverless Inference) for LegalTech use cases. Useful for practitioners evaluating managed RAG options and relevant given MCP's growing adoption, but tightly platform-coupled and promotional in nature. LawVo's cited outcomes are vendor-reported, keeping this in the solid-but-niche range. Practice with real FinTech & Trading data 90 SQL & Python problems · 15 industry datasets [Active Verified Users by Income TierEasy](/problems/sql/active-verified-users-by-income) [Technology Stocks with High BetaMedium](/problems/sql/technology-stocks-with-high-beta) [Portfolio Performance ScorecardHard](/problems/sql/portfolio-performance-scorecard) 250 free problems · No credit card [See all FinTech & Trading problems](/problems/datasets/fintech)