There is a myth that AI is cheap — that all you need is API access to an LLM. In reality, AI infrastructure is one of the most complex technical investments a company can make. You need a vector database, ingestion pipelines, embedding workflows, retrievers, verifiers, orchestrators, monitoring tools, evaluation frameworks, and more. Before you even launch your first AI feature, you’ve already sunk months of effort and significant spend.
Small and medium enterprises feel this pain the most. Hiring ML engineers at $300K–$500K/yr each is unrealistic. Even when they do find talent, those engineers have to build everything manually: data normalization, RAG composition, test harnesses, prompt evaluation loops. A single misstep can double the cost or delay the timeline by weeks.
The truth is, AI infrastructure needs to be automated. Without automation, companies end up spending their entire AI budgets on foundational plumbing instead of actual business-facing features. This post breaks down the real cost structures and why SME AI roadmaps stall early.
