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AI Cloud Infrastructure

Cloud infrastructure built for AI workloads specifically.

AI cloud infrastructure sized and configured for real model training and inference — not a generic cloud setup that wasn't built with AI workloads in mind.

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AI Cloud Infrastructure Setup digitallyscaled
28+
AI Infrastructures Built
96%
Client Satisfaction
4–8 wks
Avg. Setup Time
24/7
Support
Overview

Why AI workloads genuinely need infrastructure designed specifically for them

Standard cloud infrastructure, built for typical web application workloads, often handles AI workloads poorly — training runs that need considerable compute in bursts, inference that needs to be genuinely fast and cost-predictable, and storage requirements shaped very differently than typical application data. Infrastructure built specifically with AI workload characteristics in mind avoids the inefficiency and unnecessary cost that comes from treating AI infrastructure needs like generic application hosting.

We work across the major cloud providers, choosing whichever genuinely fits your specific workload and existing infrastructure rather than defaulting to a single preferred vendor regardless of fit. Cost control receives genuine ongoing attention, since AI infrastructure expenses can scale unpredictably without deliberate architecture decisions around resource allocation and scaling policies. We can build infrastructure around models you've already developed, or design it in parallel with model development where the two genuinely inform each other. Both training and inference workloads get architected appropriately, since these have meaningfully different resource and latency characteristics that a one-size-fits-all setup doesn't handle efficiently.

What's Included

Everything this solution actually covers

GPU Infrastructure

Right-sized GPU compute for training and inference workloads.

Auto-Scaling Setup

Infrastructure that scales with demand instead of over-provisioning.

Data Pipeline Infrastructure

Reliable data pipelines feeding training and inference.

Cost Optimization

Infrastructure configured to avoid the runaway costs common with AI workloads.

Multi-Cloud Support

Flexibility across AWS, GCP, or Azure depending on your needs.

Ongoing Support

We stay on for updates as your AI workloads scale.

Our Process

How we get there

01

Assess

We review your AI workload requirements and current infrastructure.

02

Design

We design infrastructure sized around real training and inference needs.

03

Build

We build with cost optimization and auto-scaling in place.

04

Launch & Support

We launch and support ongoing infrastructure operation.

Tech We Use

Built on tools that scale with you

AWSGCPKubernetesTerraform
Recent Work

A few projects we’ve shipped recently

Echofield Software
SaaS

Echofield Software

An AI infrastructure rebuild that cut cloud costs while improving training speed.

View Case Study
Farrowline Financial
Fintech

Farrowline Financial

A GPU infrastructure setup built for a demanding model training schedule.

View Case Study
Greywick Retail Group
E-commerce

Greywick Retail Group

An auto-scaling inference setup that handled unpredictable demand spikes.

View Case Study
Highfield Industries
Manufacturing

Highfield Industries

A multi-cloud AI infrastructure built for redundancy and flexibility.

View Case Study
Testimonial

What clients say

“What stood out was how grounded the whole approach was, not just chasing what's trendy. +51% feature adoption within a few months, and that's held up months later.”

IQ
Ines Quinlan

Head of Data, Echofield Software

FAQ

Common questions

Which cloud providers do you work with for AI infrastructure?

Primarily AWS and GCP, with Azure available depending on your existing setup.

How do you keep AI infrastructure costs under control?

Through right-sizing, auto-scaling, and monitoring specifically tuned for the cost patterns of AI workloads.

Can you set this up around our existing models?

Yes, we build infrastructure around models and pipelines you already have.

Do you support both training and inference workloads?

Yes, infrastructure is designed for both training and production inference needs.

How long does infrastructure setup take?

Most setups take 4–8 weeks depending on scale and multi-cloud requirements.

Can you help us optimize infrastructure we've already set up ourselves?

Yes, we regularly help optimize existing AI infrastructure that's technically functional but not genuinely cost-efficient or well-architected.

Do you support hybrid setups combining cloud and on-premise resources?

Yes, hybrid architecture combining cloud and on-premise resources is within our capability, depending on your specific requirements and constraints.

Can this infrastructure scale automatically based on actual demand?

Yes, auto-scaling based on genuine real-time demand is a standard part of well-architected AI infrastructure, avoiding both under and over-provisioning.

Do you help with disaster recovery planning for AI infrastructure?

Yes, disaster recovery planning is part of a genuinely robust infrastructure setup, ensuring continuity if something goes wrong with primary systems.

Can you set up monitoring and alerting for infrastructure health?

Yes, monitoring and alerting are built in from the start, giving your team visibility into infrastructure health before issues become critical.

Do you provide documentation explaining the infrastructure architecture?

Yes, we provide clear documentation of the architecture, so your team understands what was built and why specific decisions were made.

Do you help set up infrastructure for fine-tuning existing models?

Yes, infrastructure specifically supporting fine-tuning workflows is within our scope, tailored to the specific models and data you're working with.

Can this infrastructure support multiple AI projects simultaneously?

Yes, we can architect infrastructure supporting multiple concurrent AI projects, with appropriate resource isolation and cost tracking per project.

Do you provide cost forecasting before we commit to a specific setup?

Yes, realistic cost forecasting based on your expected workload is part of the initial planning process, before you commit to a specific architecture.

Can infrastructure be set up to support both experimentation and production?

Yes, we build separate environments for experimentation and production where appropriate, avoiding the risk of experimental work affecting live systems.

Can you help us plan for future scaling as our AI usage grows?

Yes, planning for genuine future scaling is part of our approach, avoiding architecture that requires disruptive rework as usage expands.

Do you provide ongoing management after the initial setup?

Yes, ongoing infrastructure management is available for businesses wanting continued support beyond the initial architecture and setup.

Do you help select the right instance types for our specific workload?

Yes, instance selection tailored to your specific workload characteristics is part of the initial architecture planning.

Ready to explore AI Cloud Infrastructure Setup?

Let's talk about your project — no pressure, just a straightforward conversation about what you need.

Talk to an AI Expert

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