About Parlon
We built the platform we always knew was missing.
Parlon is the infrastructure layer every AI workload runs on: GPU fleets, the power feeding them, the AI agents now operating inside them, and the network path between all three, normalized, correlated, and governed in one platform. Founded by three operators who spent careers building the observability platforms that came before it.
The problem
GPU fleets, AI agents, and infrastructure that changes by the hour outran the tools meant to watch it.
Legacy monitoring platforms were designed for static, pre-cloud infrastructure. Your network is now hybrid, containerized, and AI-driven. Their tools weren't, and their new owners aren't moving fast enough to change that.
Blind Spots
Legacy platforms lack visibility into GPU health, AI agent activity, and LLM workflows. Only 35% of teams believe their tools are ready for AI-era failure modes (EMA 2026).
Ops Overhead That Doesn't Scale
Administering legacy platforms requires dedicated engineers who should be building instead of maintaining tooling.
Renewal Shock
Acquisitions and consolidation have changed the economics: subscription conversions, mandatory bundles, $1M+/yr contracts.
What we do
The observability platform built for how infrastructure works today.
That includes the AI infrastructure layer itself: GPU fleets, power and PDU capacity, AI agent activity, and the network path between them, running on the same normalized foundation as everything else.
- Normalization before correlation. Parlon normalizes at ingestion, every metric, event, and path mapped into a consistent schema from day one.
- Active, not passive. Parlon continuously validates real paths, AI workflows, and critical endpoints, surfacing latency, drift, and failure modes before users feel the impact.
- Signal over noise. Alert Auto-Tune learns system behavior over time, suppresses low-value noise, and correlates alerts into a single escalating stream.
- Flexible by design. No lock-in. Open APIs, streaming export, vendor-agnostic collection, and flexible deployment: SaaS, on-premise, or air-gapped.
Why we built it
“We watched teams drown in alerts but miss what actually mattered. We saw AI workloads surge while the data needed to observe them stayed scattered across disconnected tools. And we realized that no amount of incremental improvement was going to fix what was fundamentally an architectural problem.”
The three co-founders spent careers inside the infrastructure observability industry, building and deploying tools enterprises have been forced to work around.
Founded by observability and enterprise software veterans, the Parlon leadership team is building the next generation of AI-native infrastructure monitoring.
Matt Goldberg
CEO + Co-founderSoftware engineer turned enterprise infrastructure executive. Matt led global strategic solutions at a major network performance management company and co-founded and served as COO at an IT automation platform acquired by IBM. He built Parlon to solve the architectural problems he spent a decade watching enterprises struggle with.
LinkedIn →
Chris Rohter
CRO + Co-founderGo-to-market leader in enterprise infrastructure and observability. Chris has led sales, marketing, and alliance functions at Akamai and across two companies he helped build that were acquired by IBM. He knows the enterprise observability buyer because he's spent his career serving them, and listening to what the tools they had weren't doing.
LinkedIn →
Soumo Nandi
Chief Architect + Co-founderInfrastructure technologist with deep roots in carrier network operations. Soumo's path ran from the Verizon Wireless Network Leadership Development Program through solutions architecture at multiple observability and DNS infrastructure companies, including two IBM acquisitions. He designed the architectural foundation that makes Parlon's normalization-first approach possible.
LinkedIn →Principles
The principles behind how we build.
- Architecture over feature lists. The right foundation makes everything else possible. Normalization at ingestion isn't a feature, it's the structural decision that makes alerting, correlation, and AI trustworthy.
- Time to value is a product decision. Months of professional services before seeing results isn't implementation reality, it's design failure. Parlon runs synthetics within hours. First value on day one is the expectation.
- No lock-in, ever. Open APIs, streaming export, vendor-agnostic collection, flexible deployment. The platform you choose should make you more capable, not dependent.
- Signal over noise is the whole point. An observability platform generating untrusted alerts has failed its fundamental job. When Parlon alerts, it matters.
Join us
Join us as we build the infrastructure observability company teams have been waiting for.
We don't have a careers page. If you're drawn to hard infrastructure problems, care about doing things right, and want to work somewhere your contributions are visible from day one, we'd rather hear from you directly.
Meet Ping.
Ping is Parlon's fearless mascot. He shows his true colors, and he's happiest when he's green.
Where we are
Cambridge, MA
We're based in Cambridge, close to MIT, Harvard, and a dense ecosystem of enterprise infrastructure talent and customers. We're a small team building something we believe is important.
Cambridge, MA 02138
See how Parlon covers your AI infrastructure and the rest of your stack, in one platform.