We didn't design this from the outside.
Paralleliq is built by a small team with years in high-performance computing, data center networking, AI infrastructure, and product — at the scale of Google, Meta, and beyond.
Every leap AI has made lately rides on infrastructure most people never see. GPU capacity is scarce enough that the wait for more of it is measured in months — and at the same time, a meaningful share of the GPUs already running sit doing less than they're paid to do. That gap, between real scarcity and real waste, is where GPU cloud bills quietly eat the margin out of otherwise healthy AI businesses.
Paralleliq is the model-aware layer that closes that gap — the layer that knows not just that a GPU is busy, but whether the model running on it belongs there at all.
We didn't arrive at that idea from a whiteboard. It came from years spent running production AI infrastructure ourselves — watching the same waste show up on the same bills, under different logos, at company after company — and getting tired of tools that could see utilization but not the model.
Our journey
Paralleliq started as piqc — a free, read-only scanner we built to answer one question in under a minute: how much of this GPU fleet is actually doing what it's paid to do. The platform grew from there — the same detect-then-fix loop, just running continuously instead of once.
Our mission
Operators stay in the loop, by design — every recommendation is reviewable, reversible, and auditable, never a black box making silent changes. Inference isn't web traffic; GPU workloads have memory shape, KV cache, and batch dynamics that generic schedulers were never built to see. We built for that reality instead of asking teams to bolt one on.
Our secret
The team's combined background spans decades in high-performance computing, data center networking, AI infrastructure, and product — built and run at the scale of Google and Meta, not studied from a distance. That's the difference between a tool that estimates waste and one that knows exactly why a specific model, on a specific GPU, is burning money it doesn't need to — and ships it in a way operators actually want to use.