ParallelIQ
Solutions

Built for teams running inference in production.

Standard monitoring tells you GPU utilization. Paralleliq tells you whether the model on that GPU is on the right hardware, correctly configured, and generating the revenue or reliability it should.

for enterprise ai teams

Enterprise AI Teams

AI application companies running their own inference stack. You need cost control, reliability, and a governance layer your security team will accept. When the next GPU budget request gets pushed to next quarter, optimization is how you keep shipping. Getting 30% more out of existing hardware is faster than any procurement cycle.

  • Per-model cost intelligence — know which deployment is responsible for which spend
  • Human-in-the-loop approval before any infrastructure change executes
  • Immutable audit trail — every decision logged under a named operator
  • Rollback any approved action with full pre-change state preserved
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InferOps: the operational category nobody named yet →GPU fleet observability: what to monitor and why →
for gpu cloud providers

GPU Cloud Providers

Companies that own GPU hardware and rent capacity to customers. You sell the same H100s as every competitor. While you wait for the next NVIDIA shipment, 20–40% of what you already have isn't working as hard as it could. Every point of recovered utilization is a customer you can onboard today — without a procurement cycle.

  • Recover idle capacity across tenants — fragmented allocations, over-provisioned nodes, best-effort tier gaps
  • Detect dark nodes allocated and billed but serving no customer traffic
  • Move from selling raw GPU hours to selling managed, auditable GPU infrastructure
  • Give enterprise tenants the audit trail and governance they require — without building it yourself
  • Differentiate against CoreWeave, Lambda, and RunPod when the hardware is identical
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The GPU shortage that isn't →GPU clouds becoming optimization platforms →
for hosted model api providers

Hosted Model API Providers

Companies that host open-source models and charge customers per token. Every GPU inefficiency hits your P&L directly — there is no customer buffer. While you wait for the next hardware shipment, customers in your pipeline are evaluating alternatives. Recovering 20–40% of existing fleet capacity is how you onboard them now.

  • Identify which models are on the wrong GPU tier and what it costs per hour
  • Detect dark capacity — nodes allocated and billed but serving zero tokens
  • Surface throughput suppression before it destroys your margin per token
  • Expand effective capacity without procurement — a 30% efficiency gain is 30% more customers served from existing hardware
  • Lower your cost per token — and price below competitors without sacrificing margin
  • Dollar-impact recommendations scoped to each model in your fleet
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How to reduce LLM inference costs without adding GPUs →How to detect GPU waste in a Kubernetes cluster →
for inference deployment platforms

Inference Deployment Platforms

Companies that host their customers' models — on the platform's own cloud or the customer's infrastructure. You feel waste through support tickets and churn, not your own P&L. Every OOM event and cold start failure is a customer weighing whether to stay on your platform or move to a competitor. Catching those before they happen is how you protect retention without expanding infrastructure.

  • Catch OOM risk, cold start failures, and misconfiguration before the customer notices
  • Surface findings to your ops team and optionally to the customer directly
  • Works whether the customer's cluster is on your cloud, AWS, GCP, or on-prem
  • One kubectl apply bundles the agent into your existing onboarding flow
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vLLM OOM errors: root cause diagnosis →Serverless GPU cold start latency: causes and solutions →

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