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AI cloud infrastructure

Infrastructure that takes AI products from prototype to production

GPU and CPU capacity, private networking, vector databases and autoscaling inference on AWS, Azure or your own servers, designed, secured and run by our cloud engineers.

Request an architecture reviewSee what you get
Enterprise-grade securityNamed engineersAWS and Azure expertise
how it worksAI Cloud Infrastructure
GPU cluster utilisation
Inference latency within target
OverviewWhat you getWho it is forHow we set you upCompareDetailsFAQs

AI cloud infrastructure and GPU hosting for enterprises

Xpecto Cloud designs and runs the infrastructure behind enterprise AI products: GPU and CPU clusters for training and fine-tuning, autoscaling inference endpoints, vector databases, data lakes and private networking, on AWS, Azure or dedicated GPU servers.

Problems we solve

Our models train slowly and GPU capacity is hard to get.
We plan GPU capacity across regions, reserved and on-demand, so teams are not blocked.
GPU spend is unpredictable and keeps growing.
Quotas, scheduling and cost guardrails keep GPU spend visible and controlled.
Inference slows down when real users arrive.
Autoscaling inference endpoints with load testing and latency monitoring.
Sensitive training data cannot leave our control.
Private networking, encryption and strict access keep data inside your boundary.

What you get with AI Cloud Infrastructure

GPU clusters

Training and fine-tuning on AWS, Azure or dedicated GPU servers.

Inference at scale

Autoscaling APIs with low latency and load testing.

Vector databases

Managed or self-hosted stores for retrieval and search.

Private networking

VPCs, private endpoints and VPN to your data centres.

Data lakes and storage

High-throughput storage for datasets and checkpoints.

Containers and Kubernetes

Portable, reproducible AI environments.

Observability

GPU, latency and cost dashboards with alerts.

Security and governance

Identity, keys, audit trails and policy control.

Who AI Cloud Infrastructure is for

Enterprises launching AI products

Teams moving from pilot to production.

Regulated organisations

Banks, healthcare and public bodies with strict data rules.

AI product companies

Teams that need to scale inference and control cost.

How we set you up

A structured path from first conversation to a secure, running platform, with your teams involved at every stage.

Workload reviewWe study your models, data volumes, latency and compliance needs so the platform is sized correctly.
Reference architectureWe design the network, GPU capacity, storage and security layout and agree it with your teams.
Build the platformInfrastructure as code creates a repeatable environment that is reviewed, versioned and easy to extend.
Test at scaleLoad tests and failure drills prove training, inference and recovery before real users arrive.
Operate and optimiseOur engineers monitor, patch and tune performance and cost as your AI products grow.

AI Cloud Infrastructure: before and after

DIY AI infrastructureAI cloud by Xpecto
CapacityGPU quotas and capacity fightsCapacity planned and reserved for you
CostIdle GPUs and surprise billsScheduling, quotas and budgets
ReliabilityModels fall over under loadAutoscaling with load tests
SecurityOpen buckets and shared keysPrivate networking and least privilege
SpeedMonths spent building a platformProven landing zones and templates

More about ai cloud infrastructure

We start with your workloads, data and compliance needs, design a reference architecture, build it as code and test it under load. Security, monitoring and cost guardrails are part of the platform from day one, not added later.

Build on top with an MLOps platform or private generative AI, protect it with cloud security and keep spend under control with FinOps.

GPU instancesKubernetesDockerTerraformVector databasesS3 and Blob storageVPC and VNetPrometheus and Grafana

AI Cloud Infrastructure FAQs

It is the compute, storage, networking and security that AI products run on, including GPU clusters, data platforms and inference endpoints.

Yes. We design GPU capacity on AWS, Azure or dedicated servers, depending on availability, cost and data requirements.

We use quotas, scheduling, auto-shutdown, right-sizing and commitment planning, with monthly cost reviews.

Yes. We use private networking, encryption and strict access controls, and can deploy fully private environments.
// ENTERPRISE ENQUIRY

Request a free AI cloud architecture review

Share your workload, timeline and cloud preference. An enterprise cloud architect will review it and come back with a recommended approach.

  • Workload and data assessment
  • Reference architecture and security approach
  • Cost and risk outline
  • No obligation

+91 997 139 2828

Request a free architecture review

Tell us about your AI or cloud project. A cloud architect will respond with next steps.

// KEEP EXPLORING

Related solutions

Most enterprise programmes combine several of these. They work well with AI Cloud Infrastructure.

MLOps and AI Platform

Pipelines, model registry and CI/CD that take AI from notebook to production.

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Generative AI and Private LLMs

Secure RAG, copilots and private LLM hosting inside your own cloud tenancy.

Explore →

AWS Cloud

AWS landing zones, GPU and managed AI services, designed, migrated and run for you.

Explore →

Planning an AI product? Let us design the cloud it runs on.

Talk to an enterprise cloud architect. We will review your workload and recommend a secure, cost-controlled path to production.

Talk to an architect +91 997 139 2828