Conservative reading frame
RAG costs are often input-heavy because retrieved documents are sent into the model. Budgeting gets easier when you separate prompt, retrieval, and answer tokens.
Compare with the same token assumptions
Do not treat unknown prices as zero
Verify the official source before production
Measure retrieved context
The biggest RAG cost driver is often the number and length of retrieved chunks. Track average retrieved token volume per request.
Use cache where prompts repeat
Stable system prompts and repeated retrieval patterns can benefit from caching when the provider supports it. Use PromptFinderAI RAG-oriented templates as baseline inputs when measuring retrieved context volume.
Watch context limits
Large retrieval sets can exceed context windows. Compare long-context models against cheaper models plus tighter retrieval.
