Training jobs, inference endpoints, and GPU clusters have their own failure modes and their own bills. Obsium helps AI and ML teams run reliable, observable, cost-controlled infrastructure on Kubernetes and AWS, Azure, and GCP, so your researchers ship models instead of fighting infrastructure.
Moving from a notebook to production AI changes the failure modes and the economics. Here is what teams usually bring to us.
Idle GPUs, oversized instances, and training jobs that overrun quietly turn into a cloud bill nobody can explain.
Workloads that ran fine on one node fall over on a cluster, or queue for GPUs that aren't there when you need them.
A model that's accurate is no use if the endpoint is slow or down when real users hit it.
When a training run fails at hour nine or latency creeps up in production, you find out late because the pipeline isn't instrumented.
Your team ships models. Getting them onto reliable, reproducible infrastructure is a separate job that slows everyone down.
Most AI teams arrive with two or three of these at once. We help close them without pulling your researchers into infrastructure work.
We map our services to the parts of AI infrastructure that carry the most cost and risk.
Metrics, logs, and traces across your stack, so you see GPU utilization, failing training runs, and inference latency before they cost you a run or a customer.
Explore observabilityError budgets, incident response, and on-call practices that keep inference endpoints and long training jobs running through load and node loss.
Explore SRERun GPU training and serving on Kubernetes with the autoscaling, scheduling, and isolation that AI workloads need.
Explore KubernetesInfrastructure on AWS, Azure, and GCP for AI, with GPU capacity and spend kept under control instead of climbing unchecked.
Explore cloudCI/CD pipelines and infrastructure as code, so model builds and deploys are repeatable rather than bespoke.
Explore DevOpsInternal developer platforms and golden paths so data scientists launch training and ship models without waiting on ops.
Explore platform engineeringYour models, weights, and training data are the crown jewels, and they often run on shared GPU clusters with third-party tooling. We build infrastructure that keeps them isolated and controlled: private networking, least-privilege access, secrets management, audit logging, and tenant separation enforced at the infrastructure layer. For AI SaaS, that also covers the engineering side of SOC 2.
// Infrastructure aligned to the controls above. We support the engineering side of security, not the certification itself.
We worked closely with Obsium on an application modernization project for a US-based healthcare customer. Their team successfully migrated the platform to AWS, implemented Kubernetes, and deployed a robust observability stack.
Obsium demonstrated deep expertise in cloud-native technologies and delivered the engagement with professionalism and technical excellence.
We highly recommend Obsium for organizations seeking modern cloud, Kubernetes, and observability solutions.
We start by making your system visible, so GPU waste, failing runs, and latency surface early instead of after they burn compute or customers.
We run large-scale, multi-tenant Kubernetes, the substrate AI training and serving workloads depend on, so scaling GPU work on clusters is familiar ground.
The person advising you runs the implementation. Nothing gets lost in a handoff to a junior team.
A shared Slack channel, regular syncs, flexible hours, and no long lock-in. You add capacity without adding headcount.
A short, no-pressure call to understand your setup, your goals, and where things are getting in the way.
You get a clear scope: the approach, trade-offs, and first steps, shaped around your priorities and how you like to work.
We match you with the senior engineer right for the job, and you confirm the fit before any work begins.
Your engineer plugs into your team through a shared channel and regular syncs, does the hands-on work, and keeps you in the loop.
Yes. We implemented an MLOps platform for a Fortune 500 customer in the US, covering the cloud, DevOps, and MLOps setup around their models.
Yes. We build resilient cloud and Kubernetes architectures that scale smoothly and recover fast under real production load, backed by 24/7 managed support and incident response.
We work across AWS, Azure, and GCP, and design cloud-agnostic, portable architectures. We also handle hybrid setups that connect on-premises and cloud.
We build governance and compliance controls aligned with recognised standards, with visibility and audit readiness, and bake security into the architecture from the start. Sensitive workloads can run behind private networks with no public exposure. Models, data, and workloads can be isolated on private networks.
Yes. We integrate with the tools and workflows you already use and improve them, without unnecessary replacements. We can also embed engineers and dedicated SRE support to work alongside your team.
Start with a conversation. Request a demo or get in touch at obsium.io/contact-us, and we will scope the work to your needs and share a quote.
Tell us where your AI infrastructure is straining, whether that's GPU spend, a training pipeline that keeps breaking, or inference that buckles under load. The first conversation is free, and no hour is billed before you have seen the plan.
Book a free consultation