Explore prompt caching versus fine-tuning for LLM cost reduction in startups.
Key takeaways
Prompt caching can yield up to 70% savings on LLM costs.
Fine-tuning is effective but requires significant upfront investment.
Choosing between caching and fine-tuning depends on usage patterns.
Implementing caching can enhance response times significantly.
Key Takeaways LLM API spending doubled from $3.5B to $8.4B in 2025 — most of the growth is from...
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There is a whole open-source ecosystem aimed at cutting LLM cost. The trick to evaluating any of it...
LLM costs accumulate in ways that are not always obvious. Tokens consumed by system prompts, repeated context windows, and…
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Exact-match caching is cheap and never wrong but hits rarely. Semantic caching catches near-duplicates but risks false positives.…
