ParallelIQ
Pricing

Pricing from first scan to fleet-wide rollout.

Every fleet is different. Fleet size, GPU mix, and whether you need on-prem deployment (air-gapped on request) all change the picture. Tell us about your setup and we'll scope the right tier.

Let's map it to your fleet

Detect, Decide & Fix, and Fleet Scale are scoped to your fleet size, GPU mix, and deployment model: cloud or on-prem, with air-gapped on request. Tell us about your setup and we'll walk you through what a pilot looks like.

Get pricing for your fleet

The platform

Step 1

Detect

Your cluster's X-ray.

Understand the relationship between every model in your fleet and the hardware it runs on. A lightweight agent reads the metrics your stack already exposes, continuously, with no changes to your serving stack. Collection is moving to a standard OpenTelemetry Collector.

  • Discovers every vLLM, KServe and Ray Serve deployment and the hardware it runs on
  • Judges model-to-hardware fit, not just GPU utilization
  • No added load on workloads and no changes to your serving stack
Learn more about Detect →
Step 2

Decide & Fix

Detect. Recommend. Fix, with a human in the loop.

Every finding Detect surfaces becomes a recommendation here, with its dollar impact shown before you approve it. Every action requires operator sign-off. Every change is reversible. The opposite of a black-box autoscaler.

  • Dollar impact on every finding
  • Nothing runs without operator approval
  • Every change is reversible and logged
  • Findings available through the API
Learn more about Decide & Fix →
Step 3

Fleet Scale

The same detect-and-fix loop, across every cluster you operate.

Not a separate product: Detect and Decide & Fix, running across your infrastructure and your customers' infrastructure in one view. Whether you run a hosted model API across your own nodes or a deployment platform spanning dozens of customer clusters, nothing here replaces what you already operate.

  • Every cluster, cloud and region in one view
  • Findings tied to workload, team and cluster
  • Cost and capacity across the whole fleet
Learn more about Fleet Scale →

The same principles, regardless of tier

  • Nothing executes without explicit human approval
  • Full audit trail: every finding, decision, and action logged under a named operator
  • Deployed entirely inside your environment: model weights, inference data, and customer traffic never leave it
  • Evaluated by a deterministic rules engine, not a black-box model