Your GPU bill is
mostly waste.
Idle capacity, fragmented VRAM, memory-bound kernels running at a fraction of the roofline — none of it shows up on a dashboard until the invoice does. Kernalin finds the efficiency, proves the speedup is real, and turns it into a number your finance team can bank.
Illustrative UI. Figures are examples, not customer claims.
Where the money actually leaks
You pay for capacity you don’t use
Idle GPUs and fragmented VRAM burn budget continuously, and nothing in your monitoring stack attributes the cost to a cause.
Unverified “speedups” cost you twice
A kernel that’s fast but wrong makes you pay for compute AND ship worse results. An unproven benchmark is a budget decision made on a coin flip.
Regressions inflate cost silently
A dependency bump slows a hot kernel, latency creeps, you scale out to compensate — and the bill rises for a bug nobody flagged.
Fewer GPUs for the same work — provably.
Cost intelligence
Track utilization, idle cost, inference cost, and cost-per-token, with savings insights tied to specific workloads and kernels.
Verified speedups only
Every efficiency gain clears correctness, anti-cheat, and a statistical benchmark before it’s reported — so projected savings are grounded, not optimistic.
Underutilization detection
Find low-utilization GPUs and workload-redistribution opportunities with utilization heatmaps — reclaim capacity you’re already paying for.
Regression gate in CI
A credible slowdown becomes a hard BLOCK before it merges, so cost never drifts up behind an unnoticed performance loss.
A projected saving is only real if the speedup is.
99.4% of the posterior mass sits below the ROPE — convincingly beyond what this GPU's jitter could fake. This one earns the number.
Stop paying for waste and wrong answers.
See where your GPU budget leaks — and reclaim it with speedups you can actually trust. Join the early-access program.