Infrastructure
Three tools, in the order they are used. All open source. All reproducible from published traces.
Adaptive placement, defined
The continuous selection, sizing, and migration of a declared workload class across the full space of chips, providers, and price bases that can serve it.
The workload class is declared as a model, a workload shape, and a service level. Feasibility is gated on TTFT p99 and TPOT p99. Feasible candidates are ranked by estimated cost of useful work at a stated price basis.
The ranking is re-derived as workload shape, prices, capacity, stack versions, and available silicon change. A move executes only when the estimated gain exceeds switching cost plus the confidence interval on the estimate.
the estimator
Predicts what a workload costs and how fast it runs on a given accelerator, before you rent it.
You describe the workload: model, concurrency, prompt and output lengths, and the latency bound you have to hold. berth returns dollars per million served tokens, time to first token, and time per output token, for every placement it knows about.
$ berth place --model llama3-8b --slo-ms 800
the harness
Measures the real thing and checks the prediction.
It drives a live endpoint, records what actually happened, and produces the traces that say whether the prediction was right.
the agent
Watches for change, adapts the placement, and proves what the change saved.
A placement is not a decision, it is a position that decays. A model version ships. A price moves. Traffic shifts. Most teams decide once and never check, because checking means re-measuring and nothing says when it is worth doing.
The code is open source and free, like everything else here. Holding a class correct over time, and proving what it saved, is what we are paid for.
Pick the class you spend the most on. Estimate it, measure it, and compare the two. If the estimate is wrong, tell us where, and bring traces.
$ pip install berth-placement