AI Infrastructure Observability

AI infrastructure fails in ways your observability stack was never built to catch.

A thermal-throttled GPU, a breaker approaching capacity, an ungoverned agent read, a congested path between nodes. None of it shows up in an application trace, and general-purpose infrastructure monitoring wasn't built to look. Parlon adds the missing layer: four wedges of the AI infrastructure stack, correlated on one data model, down to a single device.

Request a demo Scope an AI infrastructure proof

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.

47%cite the impact of security controls on AI network performance

42%cite trouble isolating performance issues across networks, applications, and GPUs

34%cite network impact on inference latency and tail latency

33%cite network-induced GPU idle time and underutilization

Why the gap exists

Your tools can see the server. Not what's actually happening around it.

Traditional infrastructure monitoring was built for servers and networks. It doesn't know what a GPU's thermal margin means for a training job, it can't tell you how much breaker headroom a rack has left, it can't validate an MCP server, and it has no concept of an AI agent's identity. That's not a feature gap. It's an architectural one, and it's why the failures pile up quietly until something downstream breaks.

What general-purpose monitoring sees

Cluster averages, after the fact.

CPU, memory, and network at the OS level
Cluster-wide utilization, not per-device
An application trace, once a request is already slow
Nothing about power, agents, or fabric health

What AI infrastructure requires

Device-level signal, correlated in real time.

Per-device GPU health, not a fleet average
Power and breaker capacity as a first-class signal
Every agent read attributed and audited
East-west fabric path, hop by hop

The four wedges

One platform, four atomic units of the AI infrastructure layer.

Each wedge stands on its own. Together they run on the same normalized data model, so a GPU event, a power event, an agent read, and a fabric path issue can be correlated without a swivel chair.

GPU Fleets

A cluster-average dashboard hides the one device that's actually failing. A thermal-throttled GPU or a rising ECC/XID error count degrades a training run or a serving fleet long before any cluster-level metric moves.

01
Device-level utilization. Not a fleet average, the actual per-GPU number.
02
Thermal throttle detection. Catch a throttled device before it silently slows a job.
03
ECC/XID error tracking. Down to a single device, not a cluster rollup.

Parlon's synthetic network checks already detect distributed training node failures 60–120 seconds before frameworks like PyTorch DDP or Horovod notice. Device-level GPU telemetry above extends that same early-warning discipline inside the device itself.

Power & PDU

AI racks pull sustained load that legacy compute density was never designed around. Breaker headroom and feed redundancy are invisible to any tool that only watches compute and network, right up until a rack trips.

01
Breaker-level load. Real-time draw against rated capacity, not an estimate.
02
A/B feed redundancy. Know before a failover path is actually compromised.
03
Capacity runway. How much headroom is left before the next rack addition trips a breaker.

MCP & Agent Activity

AI agents now read and act on production infrastructure data through MCP. Without identity and an audit trail, that's a new blind spot, not a convenience.

01
Every read tied to a known identity. Human or agent, no exceptions.
02
Scoped permissions and a full audit trail. Governance built into the platform.
03
MCP server health checks. Context integrity and response timing validated continuously.

Network Path

East-west traffic inside an AI cluster fabric doesn't look like enterprise north-south traffic, and most network tools were built for the latter. A congested path between GPUs slows training and inference without tripping a conventional alarm.

01
East-west traffic, hop by hop. Fabric health across the AI cluster, not just the edge.
02
Inter-node congestion. Detect the path issue before it shows up as idle GPU time.
03
One data model. Correlates directly against GPU and power signals from the same platform.

ICMP round-trip time and packet loss checks already flag inter-node network congestion inside GPU clusters. Hop-by-hop fabric visibility above builds on that same foundation.

One platform underneath

Four wedges into the same data model.

None of the above is a module, a bolt-on, or a second console. It runs on the same foundation that makes Parlon a credible legacy replacement everywhere else.

Normalization at ingest

Every source mapped to a unified schema the moment it arrives. Correlation is immediate, not reconstructed after the fact.

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. ~70% less noise.

Deploy anywhere

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

Synthetic monitoring for AI-serving endpoints

Five use cases. No new collectors required.

The four wedges above observe AI infrastructure from the inside, at the device, the rack, the agent, and the fabric. Parlon's synthetic testing network also validates AI-serving endpoints from the outside, from 50+ global edge locations, with no instrumentation inside the model. This runs on the same platform underneath, not a separate product.

LLM Inference API Health

Monitor inference endpoints from 50+ global edge locations the way users actually experience them. Enforce SLAs for latency, availability, and certificate validity. Detect regional degradation before users report it, without touching model code. Includes MCP server health checks: context integrity and response timing for AI agent workflows.

50+global edge probe locations

<2 minSLA breach detection time

~70%alert noise reduction via Alert Auto-Tune™

GPU Cluster Node Health

Distributed training across 100+ GPU nodes can fail silently. Parlon detects node failures and inter-node network congestion 60–120 seconds before the training framework notices, the difference between checkpointing and recovering versus losing the run and restarting from scratch.

60–120searly detection vs. framework-native

10 secpolling interval

<2%false positives, target

Model Serving Failover Testing

Test edge-to-cloud failover end-to-end without a live incident. Validate that fallback routing actually works before it's needed at 2am.

Model Registry Availability

Monitor Hugging Face, DockerHub, and private registries for availability. Prevent training jobs from stalling on an unavailable model download.

Dataset Transfer Monitoring

Monitor S3 and Azure data transfer health. Predict completion time and detect network bottlenecks before they blow a training window.

The Parlon difference

What Parlon sees today that general-purpose monitoring doesn't.

General-purpose monitoring platforms were built for servers and networks. None of them were designed around an AI infrastructure stack, and it shows.

Capability	Parlon	General-purpose monitoring
LLM inference SLA monitoring from 50+ global edges	Purpose-built, no instrumentation	Not designed for it
MCP server health checks	Context integrity, response timing	No MCP awareness
GPU cluster node failure detection (60–120s early)	TCP/ICMP/HTTP, 10-sec polling	Possible, but not pre-built for this
Device-level GPU fleet health (thermal, ECC/XID)	Per-device, not a fleet average	Cluster averages only
Breaker-level power and PDU capacity	Real-time draw against rated capacity	Not purpose-built
Model registry & dataset transfer monitoring	Purpose-built	Not purpose-built
Alert Auto-Tune™ noise reduction (~70%)	All AI alerts included	Static thresholds
Normalization at ingestion	Structural, not a feature	Correlation after the fact

Not sure which wedge matches the problem in front of you? A founder will read your note and tell you plainly whether there's a fit, and what a bounded proof would look like.

Scope an AI infrastructure proof