We design cloud infrastructure for AI workloads — scalable compute, containerized model serving and Kubernetes-based orchestration — built and monitored with the same operational discipline as any other production system.
AWS, Google Cloud, Microsoft Azure
AI and model-serving workloads have real infrastructure requirements — compute sizing, containerization, scaling behavior and monitoring — that are distinct from a typical web application, but the underlying discipline is the same: design the environment deliberately, automate it as code, and monitor it properly.
We build the cloud infrastructure layer around AI workloads: containerized model serving on Kubernetes, scalable compute sized to actual inference or training load, and monitoring that tracks the metrics that matter for these workloads specifically — not just generic CPU and memory.
A model is deployed on whatever compute was available, with no real scaling or reliability plan.
Compute for AI workloads is provisioned without a clear cost or capacity plan.
Standard infrastructure monitoring does not capture inference latency, throughput or failure patterns.
AI workloads run outside a proper container/orchestration setup, making them hard to scale or move.
Design cloud infrastructure sized and structured for model training or inference workloads.
Provision compute that scales with actual inference or training demand.
Package model-serving applications into consistent, portable containers.
Orchestrate containerized AI workloads on Kubernetes for scaling and reliability.
Build the deployment and networking layer that serves models to applications reliably.
Track inference latency, throughput and resource usage specific to AI workloads.
Review the AI workload's compute and serving requirements.
Plan infrastructure sized to the actual workload profile.
Package the model-serving application for portability.
Orchestrate on Kubernetes with appropriate scaling.
Track inference-specific performance signals.
Tune compute allocation and cost as usage patterns emerge.
Review the AI workload's compute and serving requirements.
Plan infrastructure sized to the actual workload profile.
Package the model-serving application for portability.
Orchestrate on Kubernetes with appropriate scaling.
Track inference-specific performance signals.
Tune compute allocation and cost as usage patterns emerge.
AI workloads run with the same discipline as any other production system.
Scaling tied to actual inference or training load.
Monitoring that tracks what actually matters for AI workloads.
Model-serving workloads that are easier to move and scale.
We design secure, scalable cloud environments across AWS, Microsoft Azure, Google Cloud, DigitalOcean and Hetzner — aligned with your applications, workloads and operational requirements.
We design, deploy and improve Kubernetes and Amazon EKS environments with a focus on reliability, security, scalability, networking and operational visibility.
We help teams use AI to speed up incident investigation, log analysis and alert triage — an assistant that correlates signals and suggests next steps alongside your engineers, not a replacement for their judgment.
More on this and related topics.
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A practical guide to designing cloud infrastructure with the right balance of reliability, security, scalability and operational control.
Running Kubernetes in production requires more than creating a cluster. Explore the architecture, security, networking, scaling and observability practices that matter.
We provision GPU-backed compute where a cloud provider genuinely supports it for your target region and instance family — we will not claim GPU capabilities we have not actually set up and verified for your specific workload. We confirm this during the assessment before committing to it.
Tell us what you're building, where you're facing infrastructure challenges, and what you want to improve.
