Vendor-neutral AI runtime intelligence that correlates GPU silicon diagnostics with inference performance. From symptoms to root causes — in seconds, not hours.
Existing tools show utilization percentages and temperature readings. They can't tell you why inference costs doubled overnight or why token latency spiked at 3 AM.
Traditional monitoring tells you GPU utilization hit 98%. It doesn't tell you whether that's healthy saturation or a memory thrashing loop burning cycles without producing tokens.
GPU metrics live in one tool. Inference logs live in another. Cost data lives in spreadsheets. Nobody connects silicon behavior to business outcomes.
General-purpose observability platforms bolt on GPU metrics as an afterthought. They weren't built for the unique telemetry chain from silicon physics to token economics.
Ceptua AI connects four layers that have never been unified in a single platform — from silicon physics to business impact.
When your LLM slows down, the problem could be anywhere: a thermal throttle on one GPU die, a KV-cache eviction pattern, a misconfigured batch scheduler, or a memory bandwidth bottleneck. Ceptua traces the causal chain across all four layers to identify the actual root cause — and tells you exactly what to fix.
Deploy the lightweight agent alongside your existing stack. Ceptua starts correlating in minutes — no rip-and-replace required.
Install the Ceptua agent on your GPU nodes via container image or package. It collects 50+ silicon-level metrics with zero inference overhead.
Attach the Ceptua SDK to your inference engine. It captures token timing, KV-cache behavior, batch scheduling, and request queuing automatically.
The root cause engine continuously correlates silicon events with inference anomalies — surfacing causal chains, not just threshold alerts.
Receive actionable recommendations with estimated impact. Know exactly which GPU, which workload, and which fix — before your SLA is breached.
Lightweight agent collects 50+ GPU metrics at sub-second intervals. Deploys as a sidecar with zero performance impact on your inference workloads.
CoreDrop-in SDK hooks into your inference engine to capture token generation timing, KV-cache utilization, batch scheduling, and request queuing.
CoreHeuristic engine correlates GPU silicon events with inference anomalies to surface actionable root causes — not just alerts and thresholds.
CoreCustom analytics dashboard with correlated timelines, GPU topology views, and cost-per-token attribution. Built for GPU operations, not repurposed from generic monitoring.
CoreAlerts that tell you why, not just what. "GPU:3 thermal throttle → 40% latency increase on model-v2" is actionable. "GPU temperature high" is not.
CoreHardware Abstraction Layer designed for vendor-neutral observability. NVIDIA today, AMD on the roadmap — same platform, same insights, any silicon.
RoadmapNeo-cloud, sovereign infrastructure, on-premise clusters, or GPU-as-a-service — every component runs inside your perimeter. No data egress. No vendor lock-in.
Whether you operate a neo-cloud GPU fleet, sovereign infrastructure, on-premise clusters, or GPU-as-a-service — Ceptua deploys inside your environment.
Purpose-built for GPU-native cloud providers and GPU-as-a-service platforms that need fleet-wide observability across thousands of accelerators.
Deploys within data sovereignty boundaries with no external telemetry egress. Fully air-gap capable for sensitive workloads.
Enterprise security built as a prerequisite, not an afterthought. Authentication, authorization, and data isolation on every path.
Run alongside your existing monitoring with zero interference. Validate Ceptua's root cause insights before committing to production.
Stop correlating GPU metrics, inference logs, and cost dashboards manually. Ceptua identifies the causal chain from silicon to SLA breach — reducing investigation from hours to seconds.
Understand which GPUs are delivering tokens efficiently and which are burning cycles on memory thrashing, thermal throttling, or misconfigured batch schedulers.
Connect GPU silicon behavior to token economics. Know exactly what each model, each workload, and each customer costs — down to the GPU die.
GPU-native cloud platforms serving AI workloads at scale
National AI programs and data-sovereign GPU infrastructure
Colocation and GPU-as-a-service operators building AI capacity
Organizations running private inference on their own GPU clusters
Ceptua integrates with the GPU hardware, inference engines, and alerting systems you already use.
We're onboarding select GPU operators for shadow-mode deployment. Run Ceptua alongside your existing monitoring — zero risk, full visibility.