Keeping GPU fabrics healthy at scale
For GPU clouds, AI platforms and inference operators: trace training stalls and inference latency to the GPU, link or scheduler decision underneath — and act without adding risk.
The most expensive infrastructure is the least forgiving.
AI clusters concentrate enormous value into tightly coupled systems. A single degraded link can slow a collective operation across hundreds of GPUs; a single flaky node can stall a training job or quietly inflate inference latency for every request routed to it.
The signals exist — GPU health, fabric counters, scheduler events, serving metrics — but they live with different teams. ML engineers see a slow job or an SLO burn; infrastructure engineers see a counter creeping up; nobody sees the chain between them.
Raincurve connects the serving and training layers to the hardware and network beneath them, so the question "why is this slow?" has a specific, evidenced answer.
Where time is lost today
Symptoms far from causes
Inference latency and job slowdowns are reported at the application layer, several hops from the component responsible.
Partial failures
Components that degrade instead of failing reduce usable capacity without triggering a clear alert.
Blast radius
Tightly coupled jobs mean one bad node or link affects the whole allocation, not one request.
Fast-moving placement
Schedulers move work constantly, so yesterday's dependency map is already wrong.
Risky remediation
Draining nodes or restarting services under load can cause the outage it was meant to prevent.
Split ownership
Platform, ML and infrastructure teams each hold part of the evidence.
How Raincurve works in your cluster
Map
Endpoints, replicas, jobs, nodes, GPUs, interconnect and top-of-rack network in one live graph that follows scheduler placement.
Score
The Curve-1 pre-filter scores GPU, fabric and serving telemetry continuously, surfacing rare, dangerous trajectories early.
Localize
Symptoms are grouped into incidents and traced to the most likely GPU, link or node, with the evidence chain.
Act safely
Drains, restarts and reschedules are verified against capacity and redundancy, and executed in sandboxed microVMs.
A training job slows by 30%. Nothing is down.
The job is healthy by every job-level metric; it is simply slower. Raincurve's timeline shows one leaf uplink's error counters climbing, retransmits on the eight nodes behind it and collective-operation time rising in step.
The recommendation — cordon the eight nodes, reschedule, replace the optic — is verified against remaining capacity before it reaches the on-call engineer.
originconfidence 0.79
effect1 hop
symptomcollective ops slowed
actionverified: capacity headroom 14%
Security for high-value environments
Isolated execution
Every diagnostic or remediation task runs in its own Firecracker microVM, restored from a clean copy-on-write snapshot. A bad run cannot reach the host, other sandboxes or anything outside its task boundary.
Your data stays in your boundary
Open-weight models deploy inside your environment, so cluster telemetry, job metadata and model details never leave it.
Platform capabilities used here
Every solution runs on the same Raincurve platform.
How engagements start
GPU cloud engagements usually scope the Tier 2 pilot to one cluster or one inference fleet.
Infrastructure Assessment
A scoped read of your topology, telemetry and recent incidents. We map cross-layer dependencies, replay past incidents through Curve-1 and show where time to resolution is lost.
Production Pilot
Raincurve runs alongside your existing tools on a defined slice of production — a hall, a region, a cluster — with success criteria agreed up front and measured weekly.
Enterprise Reliability Platform
Estate-wide deployment in your environment: continuous cross-layer reasoning, verified remediation workflows, and integration with your NOC, ticketing and change processes.
Technical documentation
Deployment, integrations and concepts for this environment.
Deployment guide
Private, hybrid and cloud topologies, sizing and security boundaries.
Open docs →DocumentationIntegrations
Telemetry, inventory, orchestration and ticketing sources Raincurve reads and writes.
Open docs →DocumentationPlatform concepts
Topology graph, contracts, hypotheses, incidents and verification.
Open docs →