Compare prompt caching and fine-tuning from workload economics

Prompt caching and fine-tuning solve different problems. Caching can reduce repeated-prefix processing; tuning can change behaviour or shorten instructions. Compare them only after defining the workload and holding accepted quality constant.

Calculate a transparent scenario

The adaptation lab uses authored unit costs for 10,000 tasks.

from adaptation_cases import caching_vs_tuning_case

result = caching_vs_tuning_case()
assert result["scenario_costs"] == {
    "baseline": 100.0,
    "cache": 70.0,
    "tune_plus_run": 115.0,
}
assert result["lowest"] == "cache"
assert result["quality_equivalence_assumed"] is True
assert result["authored_costs"] is True

Caching wins this constructed scenario. It is not a provider quote, and quality equivalence is an assumption that a real experiment must test.

Build the workload model

Measure tasks per period, prefix length and stability, cache hit rate and expiry, input/output tokens, retries, accepted-task rate and traffic growth. For tuning, include data creation, review, training trials, storage, deployment, monitoring and future retraining as well as inference.

Calculate total cost and cost per accepted task for the same task IDs. Include latency, engineering time and operational complexity separately. Run sensitivity analysis for volume, hit rate and retraining frequency; one point estimate can reverse with small assumption changes.

Caching is attractive for stable repeated content but requires tenant and authorization-safe keys. Tuning is justified only for demonstrated behaviour gains or durable economics after quality gates. The options can coexist, and neither replaces retrieval for changing facts.

The Generative & Agentic AI course connects workload economics to cache isolation, evals and adaptation reports.

Exercise

Create low/base/high scenarios with real quoted prices dated and sourced separately. Find the break-even task volume, then invalidate any option that fails the paired acceptance set.

Continue learning

This article is part of the Model adaptation and multimodal tasks sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in Document model licensing before using a checkpoint.
• Continue with Vision-language extraction with field-level validation.

Reference: OpenAI prompt caching guide.