LLM Bill Spikes After Adding Context? Check Cache Misses First

On October 4, 2026, DEV Community published an article addressing sudden spikes in LLM API bills. According to the piece, when expenses climb after adding retrieval, longer system prompts, or tool definitions, the root cause is almost always input tokens being reprocessed at full price on every request rather than a poor model choice. The article advises developers to first inspect the cache fields in the response usage object. If cached reads remain at zero across repeated requests sharing a common prefix, the issue stems from a bug rather than pricing. Fixing prefix stability and breakpoint placement incurs no cost, whereas downgrading the model unnecessarily compromises output quality.