What is InferOps?
MLOps ends when the model is deployed. FinOps starts when the bill arrives. Everything in between — keeping inference fleets healthy, efficient, and production-ready — is InferOps. And most teams are doing it manually.
The Gap Nobody Named
When a model is trained and validated, MLOps hands it off to infrastructure. At that point, someone has to answer a set of questions that neither MLOps nor FinOps tooling was designed to handle:
- Which GPU tier does this model actually need — and is it on the right one?
- What happens to this deployment when traffic spikes at 3am?
- Is the KV cache sized correctly for the context lengths we're serving?
- Are we eight percent from an OOM event that nobody has noticed yet?
- When the CPU:GPU ratio drifts on an agentic workload, who gets paged?
Today, the answer is: a senior infrastructure engineer who carries the answers in their head, or a consultant hired to build a runbook. Neither scales. That gap has a name now: InferOps.
A Definition
InferOps is the operational discipline for running AI inference workloads in production — covering detection, diagnosis, remediation, and governance of inference fleets at the model level, not just the resource level.
Generic infrastructure tooling sees that a GPU is at 34% utilization. InferOps tooling sees that a Llama 70B deployment is at 34% utilization because a CPU orchestration bottleneck is starving it — and recommends a specific fix, not a generic alert.
What InferOps Is Not
Not MLOps.
MLOps covers experiment tracking, model registry, and CI/CD for models. It ends the moment a model is live and serving traffic. InferOps begins exactly there — the operational questions after deployment are different in kind, and MLflow, Kubeflow, and Weights & Biases were not built to answer them.
Not FinOps.
FinOps operates at the billing layer. By the time a GPU waste problem shows up as elevated spend, the wrong tier has been locked in for months. FinOps tells you the bill was too high. InferOps finds the problem before the bill arrives.
Not GPU monitoring.
Datadog, Grafana, and Prometheus tell you metrics crossed thresholds. They do not tell you what those metrics mean for the specific model on that specific GPU. A utilization drop on an agentic coding cluster is CPU starvation. The same drop on a batch inference cluster is a scaling opportunity. Monitoring cannot tell the difference — it does not know what is running.
The Consulting Signal
The clearest evidence that InferOps is a real category is that teams are already hiring consultants to do it. Search for inference operations help and you find boutique firms, fractional GPU infrastructure engineers, and AI platform consulting practices — all filling the same gap with human expertise.
This is the pattern that precedes every major DevOps category: DevOps itself, SRE, MLOps, FinOps, Platform Engineering. Consultants appear first. The tooling follows. The teams that adopt the tooling early stop paying for expertise by the hour.
What an InferOps Platform Does
A mature InferOps platform operates across three layers:
Detection
Scan every workload across every cluster — tier misplacement, OOM exposure, KV cache pressure, idle capacity, CPU:GPU imbalance. Per-model findings with specific dollar impact, not aggregate cluster metrics.
Remediation
Surface recommendations with explanations, not raw alerts. Present the proposed action, the reasoning, and the expected outcome. Wait for operator approval before executing. Log every decision.
Governance
An immutable audit trail of every finding, recommendation, approval, and execution across the fleet. Operational knowledge that is transferable rather than tribal.
The Open Source Entry Point
The natural entry point into InferOps is a scanner — a read-only tool that assesses a running inference cluster without agents, instrumentation, or cluster changes. It answers the first InferOps question: what is actually wrong with this fleet right now?
piqc is an open source InferOps scanner for Kubernetes inference clusters. It runs as a Kubernetes Job, reads live deployment and node state, classifies findings by type and severity, and exits — leaving nothing behind. The control plane — recommendations, approval workflows, execution, audit trail — is what comes next.
InferOps is an emerging term. Related language you may encounter: inference operations, inference platform engineering, AI cluster operations. The distinction that matters is model-awareness — tooling that understands what models are running, not just that GPUs are being consumed.
Start with a free InferOps baseline
piqc scans your Kubernetes inference cluster in minutes — no agents, no instrumentation, no cluster changes. See your first InferOps findings before you leave this page.