Every magic number
was a guess. Stop guessing.
Cache TTLs, retry counts, batch sizes, thresholds, learning rates, timeouts — hard-coded constants someone set once and never revisited. Empire Tune searches them, validates out-of-sample so it doesn't overfit, and ships the config that actually wins on your objective.
See pricing Join the waitlistSmart, not brute-force
Bayesian / TPE search over your parameter space — it spends trials where the payoff is, instead of a combinatorial grid. Get to a good config in dozens of trials, not thousands.
Out-of-sample by default
The trap with tuning is overfitting to the eval set. Tune validates every candidate on held-out data (time-split, k-fold, or your own splitter) and reports the in-sample vs out-of-sample gap, so you ship a config that generalizes.
From study to config file
The winner exports as a config (JSON/YAML/env) you can drop into your app or commit to your repo — with the trial history and the score, so the change is auditable, not magic.
Drift-aware re-runs
The best config last quarter isn't the best one now. Schedule re-tunes and Tune diffs the new winner against your current one, only proposing a change when it's a real improvement out-of-sample.
The methodology, not the magic. We built Empire Tune deriving the constants in our own high-frequency systems — and the discipline that matters isn't "search harder," it's "validate out-of-sample so you don't fool yourself." Tune packages that discipline as a clean engine. It optimizes against the objective you define on your data; it does not contain — and never ships — our own parameters, thresholds, signal definitions, or models.
Who it's for
Backend & platform teams with performance/cost knobs to tune; data & ML teams doing hyperparameter search who want OOS rigor built in; ops teams tuning retries/timeouts/batch sizes by gut today.
Why not just Optuna?
Optuna is an excellent search library. Tune is the hosted product around it: OOS validation by default, drift-aware re-runs, config export, audit trail, and the guardrails that stop you from shipping an overfit config. See the comparison →
The pitch in one line
"That constant set to 42 because it 'felt right' is probably costing you. We find the number that actually wins — and prove it generalizes."
Early-access waitlist
Public launch 2026. Early-access = founding pricing locked (20% off forever) + a free tuning study + priority onboarding.