RAG vs. Fine-Tuning at Scale: Operational, Cost, and Performance Implications for the Enterprise
Scaling an AI solution presents unique challenges. This analysis compares the long-term operational overhead of maintaining a RAG pipeline (managing document updates, vector DBs, embedding costs) versus the lifecycle of a fine-tuned model (retraining cycles, data drift monitoring, versioning). We evaluate infrastructure costs, latency profiles, team skill requirements, and adaptability to change, providing a clear framework for choosing the most sustainable and cost-effective approach for enterprise-wide deployment.
https://www.impressico.com/blog/rag-vs-fine-tuning/