GPU & training platforms
Right-sized GPU nodes on managed Kubernetes, spot where jobs tolerate it, quotas per team, and checkpointing so failures cost minutes, not days.
AI & ML Infrastructure
AI & ML infrastructure without the hype.
GPU clusters that actually get used, model serving that doesn't scare anyone, and an MLOps path your team can operate. Built on cloud-native platforms, sized for you, not for a keynote.
What we help with
The AI infrastructure stack.
From the first GPU test run to a serving path you can defend in an incident review.
Model serving & inference
vLLM, TGI, Triton, or KServe depending on the workload, autoscaling that follows queue depth, and a rollback path that's actually rehearsed.
MLOps pipelines
Reproducible training runs, model registries, CI/CD for models, experiment tracking, and evaluation gates before anything reaches production.
Security & data access
Least-privilege identity for training data, controlled egress for model weights, and audits that make the compliance conversation short.
AI cost discipline
GPU spend with an owner, budgets per experiment, idle capacity detection, and commitment strategy so capacity you bought actually gets used.
Cloud-native adoption
Adopting cloud-native for AI, deliberately.
Running AI workloads on Kubernetes, GitOps, and open-source tooling is a decision, not an event. We scope the beachhead, productize the serving path, and hand over a platform your engineers can run without a dedicated ML team.
- Start with one workload. A single well-understood model or training job proves the platform before anything expands.
- Standardize the serving path. One route, one registry, one runbook. Nobody builds a bespoke ML platform on day one.
- Share the same platform. The Kubernetes you run AI on is the Kubernetes your applications run on. One operating model, not two.
- Know when not to adopt it. If managed APIs or a single GPU server honestly suffice, that's what we'll recommend.
Cloud-native adoption beyond AI: Kubernetes & platform engineering and cloud migration & modernization cover the wider move.
When AI infrastructure is worth it, and when it isn't.
Managed model APIs are often the right answer for years. We get into self-hosting and cloud-native AI when the economics, data controls, or latency genuinely demand it, and we'll tell you plainly when yours doesn't.
Common questions
AI infrastructure questions we hear often.
Do we need Kubernetes to run ML workloads?
Often not at first. A single GPU server or a managed notebook service covers many early workloads. Kubernetes earns its place when you need to serve several models, share GPUs across teams, or want reproducible deployments. We'll tell you which stage you're at.
How do you keep GPU and training costs under control?
The same way we handle compute spend everywhere: quotas and budgets per team, spot capacity where jobs tolerate interruption, idle detection for GPU nodes and checkpoints, and reserving capacity only for steady-state training loads. GPU cost should be a visible line item with an owner.
Can we self-host models without a dedicated platform team?
Yes, if the serving path is small and standardized. We design for the team you have: one clean serving route (for example vLLM behind a standard ingress), model versioning in a registry, and runbooks that make a deployment boring. Most teams never need a bespoke ML platform.
We're already using managed model APIs. When should we move off them?
When three things are simultaneously true: your spend at scale materially exceeds self-hosting, your workload has predictable shapes, and you have the operational appetite to own the path. Until then, managed APIs are often the right answer, and we'll say so.
Tell us about your AI workloads.
Tell us what you're training, serving, or planning. You'll get a straight answer and a practical next step.