Understand what is slowing your AI
Connect model-serving latency and errors to the GPUs, interconnects, schedulers and networks underneath them — and localize the failure in minutes instead of hours.
The model is slow. The cause is three layers down.
Inference latency is the sum of everything beneath it: GPU health, memory pressure, interconnect congestion, a scheduler decision, a storage stall, a flapping top-of-rack link. ML platform teams see the symptom; infrastructure teams own the cause; neither sees both.
Raincurve treats the inference path as one dependency chain — from endpoint to replica to GPU to fabric to network — and reasons across all of it.
What Raincurve connects
Serving to hardware
Endpoint latency and error rates mapped to the replicas, GPUs and hosts serving them at that moment.
GPU and interconnect health
Thermal, memory and error signals alongside fabric congestion and link state between accelerators.
Scheduler decisions
Placement, preemption and scaling events on the same timeline as the symptoms they cause.
Network beneath the cluster
Top-of-rack and spine behavior that silently degrades collective operations and request paths.
Capacity and degradation signals
Early warning when a partially failed component is quietly reducing usable capacity.
Failure localization
A ranked origin — the GPU, link or node most likely responsible — with evidence.
p99 latency doubles on one endpoint.
The serving dashboard shows the symptom. Raincurve's incident timeline shows the chain: a single optic on a leaf uplink began degrading, retransmits rose on the pod behind it, and requests routed to two replicas on that pod slowed down.
The recommended action — drain the two replicas and schedule the optic swap — is verified against current redundancy before it is proposed.
14:02:11origin · confidence 0.82
14:02:40triggered · 1 hop
14:03:05triggered · same pod
14:03:30symptom
actionverified: no endpoint loses redundancy
Safe by construction
Sandboxed execution
Diagnostics and remediation run in isolated Firecracker microVMs with copy-on-write snapshots, never on shared long-lived processes.
Inside your boundary
Open-weight models deploy in your environment, so cluster telemetry and model metadata do not leave it.
Research
Method, results and limits for the techniques on this page.
Firecracker sandboxing for secure execution
MicroVM isolation and copy-on-write snapshots for remediation workloads.
Read paper →ResearchCurve-1: a reasoning architecture
The four-stage pipeline behind cross-layer root-cause ranking.
Read paper →SolutionAI / GPU Infrastructure
How GPU cloud and AI platform teams use Raincurve.
Explore →