Context engineering can increase upfront development effort, but it often reduces total system cost over time. Without context engineering, teams tend to compensate for poor answers by adding more context, which increases token usage and inference costs. Larger prompts are more expensive and slower, and they often make results worse rather than better.
Well-designed context engineering typically lowers token consumption. By retrieving only relevant chunks instead of entire documents, prompt sizes shrink and inference becomes cheaper. For example, pulling four targeted sections from a vector database is usually far less expensive than injecting hundreds of lines of raw text. This cost reduction compounds at scale, especially in high-traffic systems.
There is some infrastructure cost associated with using external memory like a vector database. However, systems such as Milvus or Zilliz Cloud are designed to be efficient and scalable. In most production scenarios, the savings from reduced token usage and fewer retries outweigh the cost of retrieval infrastructure. In that sense, context engineering is less about spending more and more about spending smarter.