Your monitoring sees GPUs. It doesn't see models.
Your control plane manages resources. It doesn't optimize them.
ParallelIQ identifies the waste, recommends the fix, routes it through your team for approval, and keeps a signed record of every decision made.
Deploys entirely within your environment. Your data never leaves your cluster.
Models change. Workloads shift. But your infrastructure has no idea.
ParallelIQ addresses the three structural failures at the core of AI operations.
Most teams can't answer basic questions about their own fleet — which models are live, which versions are deployed, how workloads depend on each other. That knowledge lives in someone's head, or nowhere at all.
The result: A config change breaks a pipeline nobody knew existed. A model update causes a cost spike nobody can explain. Every incident starts with the same question: what changed?
High GPU utilization is a false signal. Your GPUs are busy — but busy doing the wrong things. Wrong batch sizes, wrong instance types, wrong concurrency settings. The meter is running. The throughput isn't keeping up.
The result: Your GPU bill grows faster than your throughput. That's not an infrastructure problem. It's a margin problem. The gap is silent, cumulative, and invisible until it's too big to ignore.
Schedulers and autoscalers were built for stateless web services — not memory-heavy inference pipelines with strict latency requirements. By the time your monitoring catches a problem, your users already have.
The result: Scaling events at the wrong moment, latency spikes under load, and engineers firefighting instead of shipping.
Most infrastructure tools treat GPUs like CPUs. ParallelIQ understands what's actually running on them.
The optimization engine is rules-based and deterministic — not model-driven. No AI making infrastructure decisions. Every recommended action shows you the blast radius and requires human approval before it touches your cluster.
See every model, GPU, runtime, and batch configuration across your cluster — automatically, without instrumentation.
Know exactly what each deployment costs per hour and per request — and where tier mismatches, memory pressure, or idle capacity is burning budget.
Anticipate GPU demand before spikes occur — move beyond reactive autoscaling to model-aware capacity planning.
Every recommendation approved by a human. Every action logged permanently. Full chain of custody for every change to your fleet.
The piqc scanner is read-only — it observes your cluster, never writes to it. No telemetry, model weights, inference inputs, or workload data ever leaves your environment.
Before you call Nvidia, find out what you already have.
Recover dark capacity, improve bin packing, and reclaim idle nodes. Paralleliq helps GPU clouds serve more customers from existing infrastructure — deferring capacity orders while you wait for the next hardware batch.
Your control plane has a blind spot. We fill it.
Your control plane manages your cluster. It doesn't optimize it. Paralleliq sits between your control plane and your workloads — surfacing waste, routing fixes through your team for approval, and keeping a signed record of every decision.
Runtime telemetry that actually sees models.
Paralleliq integrates with vLLM, Triton, KServe, and Anyscale — turning runtime signals into actionable optimization recommendations across your entire fleet.
GPU cost is eating your margin. We recover it.
Platform teams running on-prem or in private environments face the steepest GPU cost pressure. Paralleliq identifies the waste before it compounds — without adding instrumentation or changing your stack.
Deep dives into architecture, performance tuning, and operational excellence.
I asked a GPU cloud provider what their biggest pain point was. They said they're running out of GPUs. Here's why I think the real problem is somewhere else entirely.
GPU waste in Kubernetes is largely invisible to standard monitoring. Here is what to look for, which metrics actually surface it, and how to go from suspicion to a concrete dollar figure.
MLOps ends when the model is deployed. FinOps starts when the bill arrives. The operational gap in between — keeping inference fleets healthy, efficient, and production-ready — is InferOps. And most teams are doing it manually.