FULL-STACK AI OBSERVABILITY
Your tools can see the server.
Not the model.
When inference slows down, most teams open four tools and start a war room. Parlon fires one alert — with the affected layers, the root cause, and the dollar cost. Automatically. In under 60 seconds.
THE PROBLEM
AI infrastructure has no external observer.
Inference is slow. Is it the model? A regional routing issue? A certificate expired? A GPU node failed silently and the training framework hasn’t noticed yet?
Traditional infrastructure monitoring was built for servers and networks. It doesn’t know what an LLM inference endpoint is, it can’t validate an MCP server, and it can’t tell you whether your distributed training cluster is healthy before your gradient sync stalls.
Parlon applies its global synthetic testing network to AI workloads, delivering full-stack AI observability from the outside, from 50+ edge locations, without any instrumentation inside your models.
AVAILABLE TODAY
Five use cases.
No new collectors required.
Powered by Parlon’s existing synthetic testing infrastructure, these use cases are available today.
LLM Inference API Health
Monitor inference endpoints from 50+ global edge locations. Enforce SLAs for latency, availability, and certificate validity. Detect regional degradation before users report it without touching model code.
GPU Cluster Node Health
Detect distributed training node failures 60–120 seconds before PyTorch DDP or Horovod notices — preventing $100K+ training jobs from wasting GPU hours on a dead node.
Model Serving Failover Testing
Test edge-to-cloud failover end-to-end without a live incident. Validate that your fallback routing actually works — before you need it 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.
GPU Metrics
DCGM, gNMI, RoCEv2, OTel GenAI spans, dollar cost per incident, training burst detection.
UC-1 DEEP DIVE
LLM Inference API Health Monitoring
Monitor your inference endpoints from 50+ global edge locations — the way your users actually experience them. SLA enforcement, cert monitoring, and regional degradation detection. No instrumentation inside the model.
Global SLA Enforcement for Inference Endpoints
HTTP · SSL/TLS · DNS · TCP · MCP · 50+ edge locations
What Parlon Monitors
- HTTP latency (TTFB as TTFT proxy) — detect inference slowdowns without model access
- SSL/TLS certificate chain validation with 30-day expiry warnings
- DNS resolution latency per region — separate from endpoint performance
- TCP connectivity and response validation
- MCP server health checks — context integrity and response timing for AI agent workflows
What You Get
- SLA compliance proof: audit trail from 50+ global probes per endpoint
- Regional cost visibility — know which regions are expensive, optimize deployment
- Incident pinpointing: distinguish latency spike from DDoS, MitM, or routing failure
- YAML-based SLA policy templates — set thresholds without writing custom logic
- Alert Auto-Tune™ — ~70% noise reduction, so SLA alerts are worth acting on
UC-2 DEEP DIVE
GPU Cluster Node Health Monitoring
Distributed training across 100+ GPU nodes can fail silently. Parlon detects node failures and network partitions 60–120 seconds before the training framework notices — before GPU hours start burning.
What Parlon checks
TCP · ICMP · HTTP health · DNS · 10-second polling
- TCP port 29500 — PyTorch DDP connectivity per node
- ICMP RTT and packet loss — detect inter-node network congestion
- HTTP health endpoint — training coordinator /health check
- DNS resolution for all training node hostnames
Why 60 seconds matters.
A training job across 512 GPUs that fails silently at hour 6 doesn’t just lose 6 hours — it often loses the whole run. The training framework’s own health detection can lag 2–5 minutes behind network-level failures.
Parlon’s external probes catch node failures and inter-node latency spikes before the framework does — giving operators a window to intervene, checkpoint, and recover rather than restart from scratch.
60-120s node detection time
vs. 2–5 min for framework-native detection
THE PARLON DIFFERENCE
What Parlon sees today that your current tools don’t.
Most infrastructure monitoring wasn’t designed with AI workloads in mind. Parlon’s synthetic testing network was.
| Capability | Parlon (Phase A, Today) | General-Purpose Monitoring(Datadog, Prometheus, Grafana) |
|---|---|---|
| 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 |
| Model registry & dataset transfer monitoring | ✓ | Not purpose-built |
| Model serving failover validation | ✓ — end-to-end, without live incident | Manual or not available |
| YAML SLA policy templates for AI endpoints | ✓ — pre-built, no custom logic | Custom build required |
| Alert Auto-Tune™ noise reduction (~70%) | ✓ — all AI alerts included | Static thresholds |
| Normalization at ingestion (foundation for cross-layer) | ✓ — structural, not a feature | Correlation after the fact |
47.7%
of enterprises have AI training or inference workloads deployed today
35%
believe their current tools are completely ready to manage AI network performance
50+
global edge probe locations monitoring your AI endpoints, with no model instrumentation required
Parlon
BUILT ON PARLON'S PLATFORM
AI observability isn’t a module.
It’s a consequence of the architecture.
Parlon’s AI use cases don’t require new collectors because they’re built on the same synthetic testing infrastructure that powers the core platform. Phase B’s deeper correlation is possible (at the architecture level) because normalization at ingestion means every future data source will speak the same schema.
Normalization Engine
Every data source (today’s synthetic probes, Phase B’s GPU and network telemetry) maps into a unified schema at ingestion. The foundation that makes cross-layer correlation possible without custom parsers or brittle scripts.
Alert Auto-Tune™
ML-based alerting that learns your AI workload’s behavior. Cuts ~70% of noise while preserving real SLA breaches and node failures. When Parlon alerts, it’s worth acting on.
LLM-Aware Synthetics
Active synthetic tests for AI workflows — including MCP server health checks — run continuously from 50+ global locations. No instrumentation inside the model. No polling lag.
See what your AI infrastructure looks like from the outside.
We’ll walk through a live scenario using your inference endpoints and show you what 50+ global probes see that your current tools don’t.