Assisters vs. Building Your Own RAG Pipeline

You need RAG (Retrieval Augmented Generation). Should you build custom or use a managed platform?

What Building Requires

A production RAG system needs:

Document Processing

• File parsing (PDF, DOCX, TXT, HTML)
• Text extraction and cleaning
• Chunking strategy
• Metadata extraction

Embedding Infrastructure

• Model selection and integration
• Batch processing
• Cost management
• Model versioning

Vector Database

• Database selection (Pinecone, Weaviate, pgvector)
• Index configuration
• Scaling and backup

Retrieval & Generation

• Query preprocessing
• Similarity search tuning
• LLM integration
• Context window management

Production Infrastructure

• API layer
• Rate limiting
• Monitoring
• Authentication

Time & Cost

Building Your Own

• **Development**: 8-16 weeks (senior engineer)
• **Cost**: $50,000-$150,000+
• **Ongoing**: 20-40% of engineer time
• **Infrastructure**: $200-$3,000/month

Using Assisters

• **Setup**: Hours, not weeks
• **Cost**: Pay per conversation
• **Ongoing**: Zero maintenance

Decision Framework

Build If:

• RAG is your core product
• On-premises deployment required
• Unique technical requirements
• Extreme scale (100M+ queries)
• ML engineering team available

Use Assisters If:

• RAG is a feature, not the product
• Need to ship quickly
• Prefer OpEx over CapEx
• Lack ML expertise
• Standard Q&A use case

The Hidden Costs of DIY

Teams underestimate:

• Edge cases (80% of work for 20% of scenarios)
• Ongoing tuning and optimization
• Debugging production issues
• Documentation and knowledge transfer
• Opportunity cost

We built Assisters so you don't have to build RAG infrastructure.

[Try It Free →](/signup) | [API Docs](/docs/api)