Table of Contents
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.