Cloud for AI is a balancing act — training throughput, inference latency, security posture, and cost, held in one foundation.
How it's built
A cloud foundation sized for AI workloads and cost control.
- FOUNDLanding zone
Multi-account architecture, network segmentation, and IAM as code.
- ENABLEMLOps platform
Training, experiment tracking, artifact registry, and deployment pipelines.
- OBSERVEObservability
Distributed tracing, log aggregation, and alerting across the stack.
- CONTROLFinOps
Cost tagging, budgets, and spend anomaly detection.
The delivery timeline, sized by duration
Four phases from posture assessment to observability and FinOps — sized by their stated duration.
Segment width follows each phase's stated duration range.
What's built into every cloud foundation
≤4 hrs
RTO target on fully designed cloud disaster recovery architecture
< 200ms
TTFB target on CDN-accelerated application infrastructure
3
Governance guarantees built into every landing zone — infrastructure as code, governed cost, resilience by design
Everything as code, observed, and cost-governed
A reproducible foundation — no console drift, full observability, and FinOps controls from day one.
Compute & runtime
IaC & delivery
Observability
Security & FinOps
Built to be trusted in production
Governance is part of the delivery method, not a layer added after launch.
Everything as code
The landing zone is defined in Terraform with security baselines — no undocumented console drift.
Cost is governed
FinOps tagging and budget controls make cloud spend visible and bounded from the start.
Resilience by design
Disaster-recovery architecture with explicit RTO targets is part of the foundation, not an afterthought.
The right engagement profile
CTO / Engineering Leads
Migrating to cloud or re-architecting for AI scale and production reliability
AI Programme Teams
Needing MLOps infrastructure for model training, deployment, and lifecycle management
CISO / Security Teams
Requiring a cloud posture that satisfies enterprise security and compliance requirements
Software behind this service
Where this service has the most impact
Questions buyers ask first
Do you work with our existing cloud, or start over?
Both. The posture assessment audits your current environment and returns a remediation plan — we migrate or re-architect based on what serves you, not a rip-and-replace default.
How do you keep cloud costs under control?
FinOps tagging, budget controls, and spend anomaly detection are built into the foundation from day one, so spend is visible and bounded rather than discovered after the fact.
Is the infrastructure reproducible?
Yes — the landing zone is defined in Terraform with security baselines, so it is versioned, reviewable, and free of console drift.
Can you support model training workloads?
Yes. The MLOps platform covers training environments, experiment tracking, artifact registry, and deployment pipelines.
NOT SURE THIS IS YOUR GAP?
The AI Readiness Assessment scores infrastructure readiness alongside the three other layers a programme depends on, and tells you which one is holding the rest back. About five minutes, nothing is sent anywhere.
Score your readinessBuild a cloud foundation sized for AI
Book a 40-minute discovery call for a cloud posture assessment of your current environment. No commitment.