Build vs Buy: Should You Build Your Own RAG System?

Building your own RAG (Retrieval-Augmented Generation) system gives you complete control. It also requires 3-6 months of engineering work and ongoing maintenance. Here's a realistic breakdown to help you decide.

What You're Building

A production RAG system includes:

1. Document Processing Pipeline

   • File upload and parsing (PDF, DOCX, MD, etc.)
   • Text extraction and cleaning
   • Chunking strategies
   • Metadata extraction

2. Embedding Generation

   • Embedding model selection
   • Batch processing
   • Rate limiting and error handling
   • Cost optimization

3. Vector Database

   • Database selection (Pinecone, Weaviate, Qdrant, Chroma)
   • Index configuration
   • Scaling strategy
   • Backup and recovery

4. Retrieval Logic

   • Semantic search implementation
   • Hybrid search (keyword + semantic)
   • Reranking
   • Context window management

5. LLM Integration

   • API wrapper development
   • Prompt engineering
   • Response streaming
   • Multi-provider support (optional)

6. Application Layer

   • REST API design
   • Authentication/authorization
   • Rate limiting
   • Usage tracking

7. Frontend Components

   • Chat UI
   • Streaming responses
   • Error states
   • Mobile responsiveness

8. Operations

   • Monitoring and alerting
   • Cost tracking
   • Model version management
   • A/B testing

Time Estimates (Realistic)

Component	Build Time	Cuadra AI
Document pipeline	2-4 weeks	Included
Embedding generation	2-3 weeks	Included
Vector database	2-4 weeks	Included
Retrieval logic	3-4 weeks	Included
LLM integration	2-4 weeks	Included
API development	2-3 weeks	Included
Frontend components	3-4 weeks	Included (React SDK)
Monitoring/ops	2-3 weeks	Included
Total	18-29 weeks	5 minutes

These estimates assume experienced engineers. Junior teams take longer.

Cost Analysis

Build Your Own (Year 1)

Category	Cost
Engineering (3-6 months)	€60,000 - €150,000
Vector DB hosting	€200 - €2,000/month
LLM API costs	Variable
Infrastructure	€100 - €500/month
Ongoing maintenance	€20,000 - €50,000/year

Year 1 total: €90,000 - €225,000+

Cuadra AI (Year 1)

Category	Cost
Pro subscription (5 seats)	€1,200/year
Additional credits	Variable
Engineering integration	~1 week

Year 1 total: €1,200 - €10,000 (depending on usage)

When Building Makes Sense

Build your own RAG if:

1. AI is your core product — Your competitive advantage depends on custom AI
2. You have specialized requirements — Unusual data types, extreme latency needs
3. You have the team — Experienced ML/AI engineers available
4. You have the time — 6+ months before launch is acceptable
5. You need full control — Regulatory requirements mandate on-premises

When Buying Makes Sense

Use Cuadra AI if:

1. AI enhances your product — But isn't the core product
2. You need speed — Launch in days, not months
3. You want flexibility — Switch LLM providers without rebuilding
4. Engineering is limited — Focus engineering on your core product
5. You want predictability — Known costs vs variable infrastructure

The Hybrid Approach

Some teams start with Cuadra AI to:

• Validate the use case quickly
• Understand requirements through real usage
• Build internal expertise

Then migrate to custom infrastructure if/when justified.

This approach:

• Validates before investing
• Builds team knowledge
• Reduces risk

Making the Decision

Ask these questions:

1. Is custom RAG our competitive moat? → Build
2. Do we need to ship in < 3 months? → Buy
3. Do we have ML engineers available? → Build (if yes)
4. Is provider lock-in acceptable? → Evaluate each option
5. What's our infrastructure budget? → Compare honestly

Conclusion

Building RAG from scratch is a significant investment. For most teams, the question isn't "Can we build it?" but "Should we spend 6 months on RAG infrastructure instead of our core product?"

Cuadra AI exists so you don't have to build the infrastructure. Focus on what makes your product unique.

Start with Cuadra AI →

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