Also we're purpose built to monitor GPUs, so we have things like drilling down from a Kube cluster, down to GPU nodes, down to a GPU card.
Currently, our free version is self-hosted and monitors clusters with up to 64 GPUs. We feel this will work for many use cases, especially just to try it out. Monitoring GPUs typically requires you to deploy something where your GPUs live. Since you’re already installing software on your cluster, you might as well keep your data there too.
Then on your GPU cluster w/o disk, you just need to install the Neurox Workload agent. In the Web Portal UI, click on Clusters > New Cluster and copy/paste the snippet there.
Given you decided to start self-hosted, are you planning on a cloud version in the next while too?
I'm curious also who you think is the right fit for this right now in terms of initial users
Our solution is self-hosted and your data remains on your servers. And I think we do provide a fairly generous free limit of 64 GPUs.
The main 3 are:
- GPU runtime stats from NVIDIA smi
- Running pods from Kube state
- Node data & events from Kube state
We have several screens with similar information intended for different roles. For example, the Workloads screen is mainly for researchers to monitor their workloads from creation to completion. The Reports screen shows mainly cost data grouped by team/project, etc.
And yes, it's nowhere near 100% but an overwhelming majority was running Kubes for GPU workloads...mainly cuz so they'd have a unifying layer that wasn't managing each cloud separately and being proficient with their respective tooling.
Are you using something else? Slurm, docker, etc?
https://awsdocs-neuron.readthedocs-hosted.com/en/latest/neur...
leeab•9mo ago
When I co-founded Mezmo (a Series D observability platform), we obsessed over logs, metrics, and traces. I learned firsthand how critical app-level observability is for DevOps, cutting through logging noise and finding the needle in the haystack is everything.
But after diving into AI infra, I noticed a huge gap: GPU monitoring in multi-cloud environments is woefully insufficient.
Despite companies throwing billions at GPUs, there's no easy way to answer basic questions:
- What's happening with my GPUs?
- Who's using them?
- How much is this project costing me?
What's happening: Metrics (like DCGM_FI_DEV_GPU_UTIL) told us what was happening, but not why. Underutilized GPUs? Maybe the pod is crashlooping, stuck pulling an image, or misconfigured, or the application is simply not using the GPU.
Who's using the compute: Kubernetes metadata such as namespace or podname gave us the missing link. We even traced issues like failed pod states, incorrect scheduling, and even PyTorch jobs silently falling back to CPU.
How much is this gonna cost: Calculating cost isn't easy either. If you're renting, you need GPU-time per pod and cloud billing data. If you're on-prem, you'll want power usage + rate cards. Neither comes from a metrics dashboard.
---
Most teams are duct-taping scripts to Prometheus, Grafana, and kubectl.
So we built Neurox - A purpose-built GPU observability platform for Kubernetes-native, multi-cloud AI infrastructure. Think:
1. Real-time GPU utilization and alerts for idle GPUs
2. Cost breakdowns per app/team/project and finops integration
3. Unified view across AWS, GCP, Azure, and on-prem
4. Kubernetes-aware: connect node metrics to running pods, jobs, and owners
5. GPU health checks
Everyone we talked to runs their compute in multi-cloud and uses Kubes as the unifier across all environments. Metrics alone aren't good enough. You gotta combine metrics with Kube state and financial data to see the whole picture.
Check us out, let us know what we're missing. Curious to hear from folks who've rolled their own, what did you do?
Lee @ Neurox
nickysielicki•9mo ago
I categorically support any company willing to take a strong stance on the total irrelevance of slurm.
dharmab•9mo ago
I took a serious look at SLURM for my problem space and among my conclusions were:
- Hiring people who know Kubernetes is going to be far cheaper
- Kubernetes is gonna be way more compatible with popular o11y tooling
- SLURM's accounting is great if your billing model includes multiple government departments and universities each with their own grants and strict budgets, but is far more complex than needed by the typical tech company
- Writing a custom scheduler that outperforms kube-scheduler is far easier than dealing with SLURM in general
leeab•9mo ago
Our focus is definitely more on container-first, cloud-native Kubernetes environments like EKS, GKE, AKS. Also we're way more health monitoring of the actual GPU hardware rather than just scheduling jobs.