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A slow n8n AI Agent is usually doing more work than the task needs: too many tool loops, too big a context, or too heavy a model. Latency and cost come from the same place, the model calls, so here is how to measure them and the levers that cut both at once.
Prompt caching discounts repeated input tokens but still runs the model. A semantic cache embeds each query, searches past query-response pairs, and returns a stored answer on a close-enough match, skipping the call. Here is how it works, how it differs from prompt caching, and
Now that 1M-token windows ship at flat pricing, should you stuff the whole corpus in one prompt or build a retrieval pipeline? This breaks long context vs RAG into reproducible per-query cost math, the recall limits of big windows, and four variables that decide it.
There is a 25x cost spread between the cheapest and priciest LLM tier for the exact same n8n AI Agent workflow. This post prices all three providers across 11 model tiers so you can pick the right Chat Model sub-node and stop overpaying.
