AI infrastructure startup
An AI startup faced unpredictable GPU demand and noisy clusters. We re-architected their Kubernetes platform for efficient scaling, better scheduling, and cost controls aligned to growth.
The situation
Rapid model experimentation created bursty GPU usage and unpredictable costs. The platform team lacked clear guardrails for scaling and had limited visibility into workload efficiency.
Stabilized reliability while lowering cloud spend by ~20%.
How we worked
Stabilise delivery early, then build the foundation that keeps it stable once we hand it back.
Segmented workloads into dedicated node pools with tailored scaling policies and instance mixes.
Introduced GPU-aware scheduling, bin packing, and quotas to stabilize utilization.
Built FinOps dashboards and budget alerts tied to team ownership.
Added SLOs, alerting, and runbooks for GPU-intensive workloads.
Handover
Stack
Afterwards
Measured after handover, once the client's own team was running the system without us.
Tell us what you are trying to move, migrate or automate and we will reply within one business day with an honest read on the work — including the parts we think you should not do.
More engagements
