Free Tool · RAG & Vector Category · Agentive AI Agents
RAG vs Fine-Tuning AI Architecture Decision Helper
Input your dataset size, update frequency, monthly budget, and primary use case — get a data-driven recommendation: RAG, fine-tuning, or a hybrid approach, with a full breakdown and next steps.
Free · No signup required
01
Configure
02
Analyze
03
Recommend
Project Parameters — Fill all 4 fields
~50K docs
How many documents, records, or data points will the AI need access to? This affects whether retrieval or baked-in knowledge makes more sense.
< 1K10K100K1M10M+
Weekly
How often does your knowledge base or training data change? Frequent updates strongly favour RAG over fine-tuning.
~$500
Estimated monthly budget for AI infrastructure and compute. Fine-tuning requires more upfront GPU spend; RAG is pay-as-you-retrieve.
$0$500$2K$10K$50K+
Q&A
What is the main task your AI system needs to perform? This has the largest impact on the recommendation.
Scoring your inputs…