Decision Framework

Build vs Buy RAG: The Complete Guide

An honest, data-driven guide to help engineering leaders decide whether to build custom RAG infrastructure or use a platform.

TL;DR

For most teams: Use a RAG platform. The 6-12 week build time and $30K+ first-year cost of building custom rarely makes sense unless RAG is your core product or you have unique requirements no platform can meet.

Build custom when: RAG is your competitive moat, you have a dedicated ML infra team, or you have requirements that genuinely cannot be met by any platform.

Timeline Comparison

What it actually takes to get RAG into production

Build Custom
6-12 weeks typical
Week 1-2
Vector database setup & configuration
40-60 hours
Week 3-4
Embedding pipeline development
40-60 hours
Week 5-6
Orchestration code & API layer
40-60 hours
Week 7-8
Testing, debugging, optimization
60-80 hours
Week 9+
Integration & production hardening
40-60 hours
Total: 220-320 engineering hours
Use Platform
3 days typical
Day 1
Account setup & data upload
2-4 hours
Day 2
Pipeline configuration & testing
4-8 hours
Day 3
API integration & deployment
4-8 hours
Total: 10-20 engineering hours
Cost Analysis

First Year Total Cost

All costs included: development, infrastructure, maintenance

Build Custom
  • Developer time (6-8 weeks @ $100/hr)$22,000-32,000
  • Vector DB (Pinecone/Qdrant Cloud)$2,400-6,000/yr
  • Embedding service$1,200-3,000/yr
  • Compute (API servers)$1,200-2,400/yr
  • Ongoing maintenance (20% dev time)$4,400-6,400/yr
$31,200-49,800
First year total
Use ShinRAG
  • Setup time (3 days @ $100/hr)$2,400
  • Platform (Developer plan)$588/yr
  • Vector DBIncluded
  • Embedding serviceIncluded
  • Maintenance~$500/yr
$3,488
First year total
Save $27,700-46,300 in Year 1

Decision Matrix

Side-by-side comparison of key factors

FactorBuild CustomUse Platform
Time to Production6-12 weeks3 days
First Year Cost$29,000-39,000$3,500-5,000
Ongoing Maintenance20% of dev timeNear zero
CustomizationUnlimitedHigh (API + visual)
Team Skills RequiredML/Infra expertiseAny developer
Scaling ComplexityHighManaged
Vendor Lock-in RiskNoneLow (API-first)

The Honest Answer

When to build custom vs when to use a platform

Build Custom When...
  • RAG is your core product differentiation
  • You have dedicated ML infrastructure team
  • Unique requirements that no platform supports
  • Data residency requires on-premise only
  • Budget for 6+ month development cycles
Use a Platform When...
  • Need to ship AI features quickly
  • Team lacks ML infrastructure expertise
  • Want to focus on application logic, not infra
  • Budget-conscious or startup environment
  • Standard RAG use cases (search, Q&A, support)

Key Insight

The question isn't "can we build this?"—most teams can. The question is "should we?" Building custom RAG is like building your own database: technically possible, rarely the right choice. Your competitive advantage is in your application logic, not in reimplementing infrastructure that's already been solved.

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