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AI Infrastructure Observability

The infrastructure layer every AI workload runs on.

GPU fleets, the power feeding them, the agents now operating inside them, and the network path between all three. Seen, correlated, and governed in one platform. Legacy observability was never built to reach any of it.

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GPU fleetsUtilization, thermal throttle, and ECC/XID errors, down to a single device.
Power & PDUBreaker-level load, A/B feed redundancy, and capacity runway before a rack trips.
MCP & agent activityEvery AI agent read, attributed and audit-logged. Included in the platform.
Network pathEast-west traffic and fabric health across the AI cluster, hop by hop.
~70%Reduction in alert noise with Alert Auto-Tune™
HoursTime to first value, synthetics live same day
250+Legacy vendor certifications carried over, not re-earned
3–8×Lower three-year TCO than legacy platforms

Choose your route

Start where your problem is.

Three entry points, one platform underneath. Pick the one that matches the decision in front of you.

What you already know

AI workloads run on layers your observability does not reach.

A thermal-throttled GPU, a rack approaching breaker capacity, an agent reading production telemetry without attribution, a congested east-west path. None of these appear in an application trace, and none of them wait for a dashboard to be reconciled.

47.7%of enterprises already run AI training or inference workloads.
35%believe their tools are ready to observe them.

EMA Network Management Megatrends 2026, n=352.

What Parlon does

  • 01
    Device-level GPU health. Utilization, thermal throttle, and ECC/XID errors on a single device, not a cluster average.
  • 02
    Power as a first-class signal. Breaker-level load, A/B feed redundancy, and capacity runway before a rack trips.
  • 03
    Agent activity, attributed. Every AI agent read tied to a known identity and audit-logged. Governance is in the platform, not a paid add-on.
  • 04
    Fabric path, hop by hop. East-west traffic and fabric health across the cluster, on the same data model as everything above.

The first question worth answeringCan you see GPU health, power headroom, agent activity, and fabric path in one place today?

What you already know

The renewal arrives before the evidence does.

Dissatisfaction without a fair comparison becomes another renewal. The platforms holding your estate together predate cloud and AI, and their economics have moved faster than their capability.

73%of IT professionals are likely to replace an observability tool within two years.
200–300%renewal cost increases reported after private-equity acquisition.

EMA 2026 · Published SolarWinds pricing analysis, 2025.

What carries over, what gets replaced

  • Keep
    Device coverage and certifications. 250+ vendors. Nobody re-cables a network or recertifies a device to run Parlon.
  • Keep
    On-premises operating maturity. Deployment history in regulated environments carries straight over.
  • Replace
    The point-tool sprawl. One normalized data model instead of correlation across four to ten consoles.
  • Replace
    Alert volume and admin load. Alert Auto-Tune™ recommends thresholds with evidence and a human approves them.

The first question worth answeringWhich capabilities in your estate are unique, which are duplicated, and which are replaceable?

What you already know

Telemetry tells you what happened. It does not tell you whether the path works right now.

Passive collection reports a condition after it exists. Active validation tests the path on purpose, on a schedule you set, before a user or a workload finds the fault for you.

4–10tools used by the typical IT organization to monitor one network.
34%cite the lack of integration between them as a top challenge.

EMA Network Management Megatrends 2026, n=352.

What Parlon does

  • 01
    Native synthetics, not a module. Latency, availability, and path analysis in the same system as the telemetry.
  • 02
    Active tests within hours. Including inside fully air-gapped environments.
  • 03
    LLM-aware workflow checks. Validate the AI-dependent path, not only the network beneath it.
  • 04
    One data model behind both. A synthetic result and a device metric correlate without a swivel chair.

The first question worth answeringHow long after a change do you know the path still works?

Why the constraint persists

More coverage has not produced more understanding.

Context is reconciled after collection, by people, under time pressure. That is a data-model problem, and adding another tool does not solve it.

The current model

Separate collection, later reconciliation.

  1. Collect separately
  2. Interpret separately
  3. Correlate by hand
  4. Decide with incomplete context

What is required

One context-aware evidence path.

  1. Collect with context
  2. Normalize at ingest
  3. Validate actively
  4. Interpret coherently

One platform underneath

Three routes into the same data model.

The routes above are entry points, not products. Nothing here is a module, a bolt-on, or a second console.

Normalization at ingest

Every source mapped to a unified schema the moment it arrives, with vendor detail preserved. Correlation is immediate rather than reconstructed.

Synthetics and telemetry, unified

Active testing and continuous collection in one native system, including LLM-aware workflow checks.

Alert Auto-Tune™

Threshold recommendations with the evidence behind them, approved by a human. When Parlon alerts, it is worth acting on.

Deploy anywhere

SaaS, on-premises, hybrid, and fully air-gapped, with customer-controlled boundaries. In production today.

How to prove it

Prove the case before you make the decision.

A bounded proof, run alongside what you already have, on one question that matters. No estate-wide commitment, and no assumption about the result.

1
Name the triggerThe event, renewal, or capacity constraint that opened the decision.
2
Pick one questionMaterial, timely, and testable inside 30 to 60 days.
3
Agree the baselineA fair comparison, and a written note of what each measure can establish.
4
Run in parallelAlongside the incumbent. Nothing gets decommissioned to find out.
5
Review the evidenceImprovement, limitations, contradictions, and gaps, all recorded.
6
DecideProceed, extend, restrict, coexist, or stop.

Every outcome counts

A credible proof can fail to justify a change.

A null result, an incomplete integration, retained incumbent capability, or a weak full-cost case are all valid conclusions. We would rather you reach one of them in 60 days than discover it in year two.

ProceedExtendRestrictCoexistStop

The full-cost review that sits alongside it covers current contracts and operating burden, proof and transition cost, coexistence scope, decommissioning feasibility, and residual risk.

Evidence, with the record attached

One deployment, and what it does and does not establish.

Enterprise healthcare, air-gapped and HIPAA-compliant

A multi-vendor stack across more than 1,000 clinic locations, datacenters, and remote branches, consolidated into one deployment: one data model, one console, one contract. Synthetics were active within hours inside a fully air-gapped environment with strict data-residency requirements.

~50%lower observability spend than the prior multi-tool footprint
2–3 → <0.5FTE of operational effort to administer
2 → 1legacy tools replaced by one platform, one contract
Hoursfrom deployment to active synthetic tests
We found Parlon's capabilities beyond parity with the legacy vendors, and the simplicity of deployment and the cost were a significant value in themselves. Network Infrastructure Lead, enterprise healthcare provider

The evidence record

What this result establishes

  • Where observed: one production enterprise healthcare deployment.
  • How measured: against the documented cost and staffing of the replaced multi-tool footprint.
  • Configuration: on-premises, fully air-gapped, HIPAA-compliant, device monitoring and synthetic path testing.
  • What it does not establish: a universal payback period, or a result in a cloud-first or GPU-dense environment.
  • Named-use authority: anonymized here by agreement. Reference available for qualified, late-stage opportunities.

No logo wall, no generic ROI calculator. Every number on this page carries a record like this one.

What comes next

See the infrastructure first. Govern the AI that acts on it next.

The environments that matter most, regulated, sovereign, and air-gapped, cannot let telemetry, credentials, or write access leave the boundary. They need AI's help the most and cannot get it by loosening that boundary. So the governance has to be part of the substrate.

Live today

Identity, audit, and metering

  • ✓ Every read tied to a known identity, human or agent
  • ✓ Scoped permissions and a full audit trail
  • ✓ Usage metering and quotas to control AI spend
  • ✓ Bring your own model. Nothing leaves the boundary
Building next

Approval workflow and rollback

  • → Write actions inheriting the same identity model
  • → Required approvals before execution
  • → A record of what was touched, and rollback
  • → Policy that persists across model generations

Start the conversation

The first step is not migration. It is defining the decision.

Upcoming Webinar: The Four Blind Spots in AI Infrastructure

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