Cloud native for AI workloads:
what’s actually different.
Cloud-native architecture for teams running AI and ML workloads in production — where the infrastructure is the product and getting it wrong costs GPU-hours rather than CPU-hours.
The AI platform is not a side project
Most AI platforms were assembled by a data science team that needed somewhere to run a notebook and grew from there. The result is a collection of services that were never designed as a system: a training cluster that takes two days to provision, a model registry nobody enforces, serving infrastructure that cannot scale below one expensive GPU, and a monitoring gap between the MLOps dashboard and the SRE dashboard that nobody owns.
Cloud-native patterns — containerisation, declarative infrastructure, service mesh, GitOps — solve the operational problems, but only if they’re adapted for workloads that behave differently from web services. Model serving has a different scaling profile. Training jobs have a different failure mode. GPU resources have a different cost structure. Applying the Kubernetes playbook from your web tier without adjusting for these differences produces an expensive, complicated way to run the same disorganised platform.
What we do
- AI platform architecture. GPU and accelerator capacity strategy, model-serving infrastructure, vector and feature stores, and a data plane that does not require a ticket to read from.
- Cloud-native design for ML workloads. Containerised training and serving, autoscaling tied to inference load, spot/preemptible scheduling for batch, and cost controls that prevent a forgotten training job from running up a five-figure bill overnight.
- Kubernetes for AI. Cluster topology, GPU scheduling, node affinity, resource quotas, and the operational playbook for running GPU workloads on Kubernetes without turning every incident into a platform team escalation.
- MLOps and model lifecycle. Model registry, promotion pipelines, A/B and shadow deployment, rollback, and the monitoring that tells you the model has degraded before the business metrics do.
- FinOps for compute-heavy workloads. GPU utilisation tracking, commitment strategy, workload scheduling, and the financial model that tells you when self-hosted training is cheaper than API calls.
Tell us what’s stuck.
First conversation is a working session with an engineer, not a pitch.