Cloud-Native Systems & DevOps

Modern AI workloads put real pressure on cloud infrastructure — they need GPUs, they are bursty, they are expensive when run inefficiently, and they require operational discipline that traditional web applications never demanded. Cloud-native engineering is what separates AI initiatives that scale economically from those that consume budget faster than they deliver value.

Kubernetes and container orchestration. Kubernetes is the practical standard for orchestrating production workloads at scale, including AI inference and training. We design Kubernetes deployments around the realities of your applications — autoscaling policies tuned for actual traffic patterns, GPU node pools sized to the workload, service mesh where it adds value (and only there), and observability built in from day one. Done right, Kubernetes lets you ship faster with fewer surprises. Done wrong, it becomes its own operational burden.

DevOps and CI/CD. The goal of DevOps is to make production deployments boring — predictable, reversible, and frequent. We build CI/CD pipelines that run tests, security scans, and deployment automation on every change, with proper environment promotion (dev → staging → production) and clear rollback paths. We use infrastructure as code (Terraform, Pulumi) so your entire environment is reproducible and auditable.

GPU and AI infrastructure. AI workloads have specific infrastructure needs: GPU scheduling, model caching, inference autoscaling, and cost-conscious use of spot instances and reserved capacity. We design infrastructure that gets the most out of each GPU hour, separates training from inference workloads, and uses techniques like model quantization and batching where appropriate to drive down per-request cost.

Cloud migration. We help organizations move from on-premise or legacy cloud setups to modern architectures with minimal disruption. The right migration approach varies — sometimes it is lift-and-shift to buy time, sometimes it is a clean rebuild, often it is a phased path that retires legacy systems incrementally as new ones come online. We help teams choose, plan, and execute the migration with the least operational risk.