Programming Tutorials

  For AI architects and CTOs, the decision to build or buy a Retrieval-Augmented Generation (RAG) pipeline often comes down to a specific trade-off: control versus total cost of ownership (TCO). We are witnessing a shift in the enterprise RAG stack. The standard approach—orchestrating OpenAI’s GPT-4o with a search provider (like Tavily or Bing) and a vector database—is powerful but expensive. It introduces multiple points of failure and latency bloat. Perplexity's API (specifically the  sonar-pro  model) offers an enticing alternative: "RAG as a Service." It handles the search, scraping, and synthesis server-side. This post provides a rigorous technical benchmark comparing a custom GPT-4o RAG pipeline against Perplexity’s Sonar-Pro. We will look at hard numbers regarding latency, citation fidelity, and the hidden costs of token overhead. The Root Cause: The Hidden Cost of Custom RAG To understand why teams are switching, we must analyze the anatomy of a standard RAG reque...