For GPU-first clouds
Build it right. Lower your opex.
Root cause across your GPU fleet in under 5 minutes, with your team's hand on the gate. You run dense accelerator capacity on InfiniBand and NVLink fabrics under aggressive tenant SLAs, with an SRE team a fraction of a hyperscaler's size. Every hour a node, link, or scheduler misbehaves is capacity you cannot bill. NeuBird keeps production running from bring-up to sold out.
The reality of running GPU fleets
At GPU scale, failure isn't an edge case.
In one published 54-day training run on 16,384 GPUs, the job was interrupted 419 times unexpectedly, roughly once every three hours. About 78% of those interruptions traced back to hardware.
Every failure crosses layers
A stalled job might be a failing GPU, a flapping InfiniBand link, a thermal event, a saturated storage tier during checkpointing, a scheduler misconfiguration, or a bug in the customer's code. Your team has to rule each one in or out, across different tools and teams.
Is it us or them?
Customers open tickets saying your cluster is slow. Proving whether the cause is your infrastructure or their workload takes hours of log-pulling, and getting it wrong costs you credibility or SLA credits.
Bad nodes hide
A GPU throwing intermittent errors can keep poisoning jobs for days before anyone connects the pattern. Every rerun on that node wastes the customer's time and your capacity.
Fleets grow faster than teams
You add thousands of GPUs a quarter. Your on-call rotation doesn't grow with them, and tribal knowledge lives with a few senior engineers.
Today vs. with NeuBird
The GPU cloud status quo, and the center.
Cluster bring-up
The status quo
Ad-hoc burn-in scripts. Driver drift, link flaps, and firmware regressions surface only after tenants onboard.
With NeuBird
Blind spots surfaced upstream: unmonitored nodes, missing SLOs, and configuration drift flagged before the first workload schedules.
P1 multi-node incidents
The status quo
War rooms lose hours switching across Prometheus, GPU counters, Slurm, and fabric telemetry.
With NeuBird
Root cause in under 5 minutes at 94% accuracy. 15+ sources in parallel, one evidence-backed causal chain, fix staged.
Cross-site knowledge
The status quo
Resolutions live in private Slack threads. Site B repeats the bring-up pain solved at Site A.
With NeuBird
Institutional memory: verified conclusions and approved fixes inside your perimeter, never raw telemetry. Site 2 inherits Site 1 on day one.
Telemetry and egress bills
The status quo
Replicating GPU counters, job logs, and link metrics into a central lake inflates opex.
With NeuBird
Zero telemetry storage. Queried in place across 50+ integrations. LLMs metered once, roughly 90% less token waste.
Execution governance
The status quo
Reluctance to let any agent near a multi-tenant fabric: an unvetted cordon or restart is a customer outage.
With NeuBird
Suggest, Recommend, Act per environment. 100% human-approved actions, one audit trail.
Two moments, one platform
From the first cluster online to GPUs in production.
Standing up
Bring-up, done right.
New sites, new SKUs, new fabric. Faults daily, no runbooks yet, and whoever debugged the last cluster is already on the next one.
- Connect once, in place. Prometheus, Grafana, Kubernetes, Slurm, bare-metal and cloud telemetry: 50+ integrations, zero data copying.
- Pre-flight blind-spot discovery. Unmonitored nodes, missing alert coverage, and drift after a firmware roll, surfaced before tenants schedule.
- Start memory on day one. Every burn-in diagnosis and hardware triage recorded, versioned, and cited. The second site inherits what the first learned.
Fully operational
Production, at lower opex.
Clusters sold out and under SLA. Pages come from tenants as often as monitors, every P1 pulls senior engineers into a war room, and failures recur rack after rack.
- Resolve in minutes. Job, host, and switch state correlated across 15+ sources: tenant application error or failing transceiver? Causal chain shown, fix staged for approval.
- Prevent the page. Silent fabric link errors, PCIe bottlenecks, and memory degradation caught 30 to 60 minutes before thresholds trip. 80% fewer P1 war rooms.
- Operate continuously. Noisy alerts tuned, recurring failure patterns weeded out. 40% of ops capacity returned to cluster expansion, and incident costs cut by over 60%.
The Production Ops Agent
What NeuBird does for GPU clouds.
Prevent
- Degradations detected 30 to 60 minutes before thresholds trip.
- Unmonitored nodes, missing SLOs, and drift after driver or firmware rolls surfaced upstream.
Resolve
- 15+ sources in parallel, root cause in under 5 minutes at 94% accuracy: the GPU, the link, the job, or the tenant?
- Causal chain shown, fix and rollback staged for approval.
Operate
- Between incidents: alerts tuned, recurring failure patterns found, drift eliminated.
- 40% of ops capacity returned, incident costs cut by over 60%.
Scenarios
Example scenarios.
Training job hang
Before
A multi-node job stops making progress. Engineers check GPU health, then the fabric, then storage, then the scheduler. Four hours later, a single degraded link is found.
With NeuBird
The investigation runs across every layer at once and points to the link, the affected nodes, and when it started. Minutes, not hours.
The customer escalation
Before
A top customer reports throughput dropped 30%. Your team spends a day proving the cluster is healthy.
With NeuBird
Evidence shows the regression began with the customer's own code change, with timestamps and supporting signals. The ticket closes the same day.
The node that keeps failing
Before
An intermittent GPU error causes scattered job failures across different customers for a week.
With NeuBird
Memory across investigations connects the failures to one node, with zero telemetry storage. It's drained before the next job lands on it.
A 2am page
With NeuBird in the loop.
- 01
A tenant's training job stalls on a 512-GPU partition. Scheduler, fabric, and node exporter alert at once, and the tenant opens a ticket.
- 02
The Production Ops Agent correlates GPU health, link errors, job logs, and last night's changes in parallel, recalls a matching link-flap profile from a prior burn-in, and posts the causal chain to Slack in under five minutes: a flapping switch transceiver after last night's firmware roll, with a Slurm drain-and-reschedule staged and a rollback ready.
- 03
The on-call engineer reviews the receipt and approves. Action, evidence, and approver land in the audit trail. The conclusion joins memory, and the job resumes before SLA penalties accrue.
Teams
What your teams get.
Fleet reliability and SRE
Fewer war rooms, faster RCA, less toil.
Customer success and support
Answers for customers backed by evidence, not escalations.
Infrastructure leadership
Visibility into what's failing, where, and how often, across the whole fleet.
Your own agents
Governed access to production context over MCP for any internal automation you build.
How it works
Build the center first, then run the agent on it.
Access
GPU, fabric, scheduler, storage, and cloud telemetry queried where it lives across 50+ integrations. Zero telemetry storage. LLMs metered once: roughly 90% less token waste.
Remember
Every investigation recorded, versioned, and cited inside your perimeter. Memory holds conclusions and approved fixes, never raw telemetry. The fix on one rack is known before the next rack fails the same way.
Serve
Engineers, leads, and your custom agents over MCP, inside Slack, Jira, and ServiceNow. Suggest, Recommend, Act per environment: nothing executes without an approval on the audit trail.
Outcomes
What changes once the center is running.
Proof of value
A 30-day proof, on your fleet.
Week 1
Connect
One burn-in cluster or one active production region, at Suggest. Deployable in under an hour across your existing tools, in your VPC, on-prem, or air-gapped. Zero telemetry storage, SOC 2 Type II.
Week 2
Baseline
Scheduler, fabric, and GPU telemetry read where it lives. Deliverable: an audit of unmonitored nodes, drift points, and alert-coverage gaps, plus your first verified operational receipt.
Weeks 3 to 4
Validate
Against live tenant workloads or burn-in suites. Deliverable: documented under-5-minute root cause, pre-threshold detections, and engineering hours returned. Your platform team's agents plug in over MCP.
Security
Where does NeuBird run, and what does it store?
SOC 2 Type II
Independently audited security and availability controls.
Zero telemetry stored
No telemetry is stored. NeuBird queries it where it lives.
Your environment or ours
Runs in your VPC, on-prem, or air-gapped, or in ours.
FAQ
Common questions
Does NeuBird replace our monitoring stack?
No. NeuBird works with the tools you already run and investigates across them.
Can NeuBird tell infrastructure issues apart from customer workload issues?
Yes. Every investigation shows the evidence behind its conclusion, whether the cause is in your infrastructure or in the customer's workload.
Where does NeuBird run?
In your environment or ours. No telemetry is stored.
For GPU-first clouds
See it on your fleet. A 30-minute working session against one of your GPU clusters.
Connect one cluster, in place. Zero telemetry storage, and nothing executes without an approval.