AI cloud & infra
The cloud estate AI workloads actually need
Assessed with AI, built as code, run under FinOps. Cloud strategy, landing zones, migration, AI platform engineering and platform operations
AI cloud & infra
Assessed with AI, built as code, run under FinOps. Cloud strategy, landing zones, migration, AI platform engineering and platform operations
What is changing in cloud & infra?
of FinOps teams now manage AI spend, up from 31% two years ago
of IaaS and PaaS spend is wasted, the first increase in five years
of cloud waste comes from idle compute and over-sized instances
Sources: 1. FinOps Foundation, State of FinOps 2026. 2. Flexera, State of the Cloud 2026. 3. Flexera and Harness, The State of Cloud Waste 2026.
Use cases
An AI scan of IaC, configuration, quotas and usage produces a six-lens readiness scorecard. A cloud architect validates it.
Infra ScannerSix readiness lensesCloud strategy
A dependency scan and AI-drafted wave grouping produce a wave plan with cut-over order. The architecture board approves it.
Migration StudioDependency MapperMigration
Code translation and target-model regeneration produce translated jobs promoted via Git, with engineer review and CI.
Migration StudioCode translationMigration
AI-generated IaC modules from approved patterns, reviewed and with policy checks passed. Peer review and policy as code.
IaC GeneratorPolicy as codeCloud strategy
Gateway configuration and approved-model routing produce a model gateway with identity and budgets. The security owner approves it.
Agent StudioModel gatewayAI platform
AI right-sizing and cost anomaly detection produce sizing fixes and cost by workload. The FinOps owner approves them.
Governance StudioCost SentinelPlatform ops
The path
AI cloud & infra on 1Platform
Four service areas, one landing zone. Each area can be bought alone, and all four share the same foundation.
Cloud StrategyMigration and Modernisation
AI platformPlatform Ops
Readiness
Scan the whole cloud estate against the six lenses, then fix the worst gaps before AI workloads land.
Start from the estate being exited, whether data centre, legacy warehouse or ETL servers, and land it AI-ready.
Take one AI workload to production and build only the platform it needs, as a reusable pattern.
Migration Studio
35%+lower migration time and cost
Governance Studio
70%fewer data errors with governed data
Agent Studio
Up to 40%lower decision latency, agents act faster
BigQuery costs cut by half, with a Vertex AI model recommending query fixes that deploy across every project in a closed loop
BigQuery costs cut by half, with a Vertex AI model recommending query fixes that deploy across every project in a closed loop
CDO and CIO roundtable series
A fireside series on making agentic AI accountable, measurable and scalable, led by Saurabh Singh, our CPG Industry Head, with CIOs and CDOs running data and AI at major enterprises.
Featured experts from
The same four-step method as every Polestar Analytics assessment
Assessment agents hold read access only, inside your tenant
No agent applies changes; every change goes through review and CI
From your approved list; no training on your data
For AWS, Azure or GCP. Everything is deployed as code
Partner
Partner
Partner
FAQ
No. Assessment agents hold read access only, inside your tenant. Every change goes through a pull request, review and CI.
No. Models come from your approved list, with no training on your data.
No. Each area can be bought alone; all four share the same foundation.
Yes. It is a cloud-neutral pattern, with everything deployed as code.
Yes. We use client-run scripts and mark the affected lenses as interview-led.
Every output traces back to evidence from the estate
Senior Vice President
Assistant Vice President