GPU provisioning, model serving, training pipelines, and inference scaling are fundamentally different problems from traditional cloud. We design infrastructure that makes your AI systems fast, reliable, and cost-efficient.
Discuss your AI infrastructure needsYour team spun up some GPU instances, ran training jobs, and the bill was terrifying. Inference latency is unpredictable. Scaling happens manually. GPU utilisation sits at 30% because nobody optimised the scheduling. Traditional cloud architectures treat AI workloads like web workloads with more compute. They're not. The cost of getting this wrong compounds every month.
We audit your current cloud setup specifically for AI readiness: GPU utilisation rates, training job efficiency, inference latency distributions, storage I/O bottlenecks, and cost allocation per model. You get a report showing exactly where you're wasting money and where performance is being throttled.
Purpose-built cloud architecture for your AI workloads. This includes GPU cluster design and instance selection, model serving infrastructure with auto-scaling, training pipelines with spot instance orchestration, data storage optimised for ML access patterns, and multi-region deployment for low-latency inference.
Moving from your current setup to AI-optimised infrastructure without disrupting existing services. We handle the migration in phases, with rollback plans at every stage. Your production systems stay live throughout.
GPU cloud costs spiral without active management. We implement spot instance strategies, reserved capacity planning, right-sizing based on actual utilisation data, automated shutdown of idle resources, and per-model cost tracking. Most clients reduce their AI cloud spend by 30-50% while improving performance.
We don't do web apps on the side. Every engineer on your project has deep AI specialisation and has deployed production ML systems before.
We don't implement the first architecture that works. We explore options, test assumptions, and design the solution that fits your specific constraints.
Our AI-augmented methodology compresses delivery timelines by 2-3x compared to traditional consulting.
Timeline
4-8 weeks for assessment + design, 4-8 additional weeks for implementation
Team
1-2 senior cloud/ML infrastructure engineers
Deliverables
Architecture design document, infrastructure-as-code templates (Terraform/Pulumi), deployment runbook, cost projection model, monitoring dashboards
After launch
Optional retainer for ongoing cost optimisation and infrastructure scaling as your AI workloads grow
A healthtech company was spending $45,000/month on cloud GPU resources for training and serving three ML models. GPU utilisation averaged 28%. Training jobs ran on on-demand instances with no scheduling. Inference scaled manually by an engineer watching dashboards. We redesigned their infrastructure: migrated training to spot instances with checkpointing, implemented auto-scaling for inference based on request queues, right-sized GPU instances based on actual model requirements, and set up automated shutdown for idle dev environments. Monthly cloud spend dropped to $18,000 with faster training times and more consistent inference latency.
Representative of a typical engagement.
We work across AWS, GCP, and Azure. We'll recommend the best fit based on your existing setup, AI-specific service offerings, GPU availability in your regions, and pricing. Many clients are multi-cloud. We design for that too.
Absolutely. We typically work alongside your team, designing the architecture and implementing it together so knowledge transfers naturally. When we leave, your team operates the infrastructure independently.
We design on-prem AI infrastructure too. NVIDIA DGX clusters, custom GPU servers, and hybrid setups where training runs on-prem and inference runs in the cloud. On-prem makes sense when data residency requirements exist or when training volume justifies the capital investment.