TiECON East 2026
AI doesn't fail in the chatbot.
It fails in the infrastructure underneath it.
Parlon watches the layer every AI workload runs on: GPU fleets, the power feeding them, the network path between them, and the agents operating inside them.
Source: EMA 2026
Operators from SevOne, IBM, Pliant, and Akamai. We know the customers, the pain, and the accounts to call.
Watch
Observability for the infrastructure AI is changing.
What breaks underneath AI workloads, why legacy tools miss it, and how Parlon sees it.
The platform
One platform to see, understand, and act across your entire AI infrastructure.
Built from scratch on one data model. No AI feature bolted onto an old dashboard, and no five tools stitched together.
34.1% of teams cite tool sprawl as a top challenge · EMA 2026
- 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.
- Network PathEast-west traffic and fabric health across the AI cluster, hop by hop.
- MCP & Agent ActivityEvery AI agent read, attributed and audit-logged.
- Infrastructure & ApplicationsThe network, servers, cloud, and applications everything else depends on.
Normalized data
Every source maps into one schema at ingestion. Correlation lives in the data, so nobody rebuilds it during an incident.
Deploy anywhere
SaaS, on-prem, hybrid, or fully air-gapped. Same platform, same features, in every model.
Alerts that matter
Alert Auto-Tune™ learns your baselines and cuts alert noise by about 70%. When Parlon alerts, it's worth acting on.
Proof
Idea to enterprise proof.
We founded Parlon in March 2026. Six months later, enterprises run their infrastructure on it.
Met us at TiECON?
Let's keep the conversation going.
Tell us what you run and where it breaks. We'll show you what Parlon sees.