AI Infrastructure Setup
Production-grade infrastructure for AI workloads. GPU provisioning, model serving, and MLOps — built to scale from prototype to production.
Running AI in production requires more than a Python script and an API key. You need GPU provisioning, model serving infrastructure, container orchestration, monitoring, autoscaling, and cost management. We handle the full infrastructure stack. We set up GPU-accelerated compute for training and inference — whether on cloud providers like AWS, GCP, and Azure, or on dedicated hardware. We deploy model serving infrastructure using frameworks like vLLM, TGI, and Triton, with proper load balancing, batching, and autoscaling. For teams running their own models, we build complete MLOps pipelines: model versioning, experiment tracking, automated evaluation, and deployment workflows. The result is infrastructure that is reliable, cost-efficient, and ready to scale as your AI usage grows. We also configure monitoring and alerting so you always know what your infrastructure is doing and what it is costing.
Use Cases
What this looks like in practice
GPU Cluster Provisioning
Set up GPU compute across cloud providers or on-premises hardware. Right-size instances for your workload — training, fine-tuning, or inference.
Model Serving & Inference
Deploy models behind production-grade inference endpoints with proper batching, load balancing, and autoscaling. Optimise for latency or throughput.
MLOps Pipeline Setup
Build end-to-end MLOps: experiment tracking, model registry, automated evaluation, CI/CD for models, and rollback capabilities.
Kubernetes for AI Workloads
Configure Kubernetes clusters optimised for AI — GPU scheduling, node pools, resource quotas, and job orchestration for training and inference.
Cost Optimisation
Implement spot instances, autoscaling policies, and resource scheduling to minimise cloud spend without sacrificing performance or availability.
Technology
Tools we work with
How It Works
Our approach
Requirements Assessment
Understand your workloads, latency requirements, budget, and compliance constraints
Architecture Design
Design the infrastructure topology — compute, networking, storage, and security
Provisioning & Configuration
Set up infrastructure with infrastructure-as-code for reproducibility and version control
Deployment & Optimisation
Deploy your models, tune performance, and configure autoscaling and cost controls
Monitoring & Handoff
Set up observability dashboards, alerting, and documentation for your team to operate
Starting from
£20K
Timeline
2-6 weeks
Ready to get started?
Book a free strategy call and we'll assess whether this service is the right fit for your business.