Most SMB agent projects in 2026 default to fine-tuning when they should default to RAG, and vice versa. Here's the decision tree we use at TrainYourAgent — with real numbers from production accounts on Pinecone, OpenAI fine-tuning, and Anthropic's tool-use API.
In 2024 the answer was easy: do RAG, fine-tuning is too expensive and too slow. OpenAI's hosted fine-tuning on GPT-4o-mini is $3 per million training tokens and the inference markup is ~50%. Anthropic ships Sonnet 4.6 with 1M-token context and prompt caching at 90% off, which means a 200K-token RAG context is no longer absurd. Pinecone's serverless tier is $0.10 per million reads with sub-30ms p95. So the question isn't "which is cheaper" anymore.
So the question isn't "which is cheaper" anymore. It's "which is right for this specific agent." After shipping 32 production agents in the last 18 months at TrainYourAgent, here is the decision tree we actually use. The two questions that decide it Before any vendor pitch, before any cost spreadsheet, ask these two questions: 1. Specifically: do you expect to need to update or correct facts more than once a quarter? Is the style or behavior the hard part, or is the data the hard part?
Most SMB agent projects in 2026 default to fine-tuning when they should default to RAG, and vice versa. Here's the decision tree we use at TrainYourAgent — with real numbers from production accounts on Pinecone, OpenAI fine-tuning, and Anthropic's tool-use API. It is filed under Engineering because that is where operators looking for this problem actually start, and it is written from production work rather than from a content calendar.