About CloudQ

Built by engineers,
for engineers.

A boutique multi-cloud consultancy based in Sydney — with over 15 years of expertise across AWS and Azure.

Our story

15 years of craft,
applied with purpose.

CloudQ was founded on a simple belief: that great cloud outcomes come from experienced engineers who are genuinely invested in the result. With over 15 years of collective expertise across AWS and Azure, we bring deep technical capability and a commitment to quality that runs through every engagement.

We only work with a handful of clients at a time. That's intentional. It means every client gets senior engineers from day one — the people in the proposal are the people doing the work.

Based in Sydney, we operate across APAC. Clients range from funded startups scaling their first production environment to ASX-listed enterprises modernising complex platforms — on AWS, Azure, or both.

Over the past few years, AI/ML deployment has grown into a significant part of our practice. We help organisations deploy and operate AI services in a secure, governed, and repeatable way — applying the same SDLC rigour to model pipelines that we apply to every other part of the stack.

Work with us
Certifications & partnerships
AWS Advanced Consulting PartnerCloud Architecture, Migration & ML competencies
AWS Security CompetencyValidated cloud security practices
Microsoft Azure Solutions PartnerInfrastructure, DevOps & AI/ML specialisations
Microsoft AI Cloud PartnerAzure AI platform implementations
HashiCorp Technology PartnerTerraform delivery specialists
CNCF MemberKubernetes & cloud-native expertise
How we work

Our principles

01

Seniors only

No bait-and-switch. The people you meet in the proposal are the people doing the work. Always.

02

SDLC-first

Every deployment — infrastructure or ML model — is secure, repeatable, and auditable. Governance is built in, not bolted on.

03

Genuinely multi-cloud

We work across AWS and Azure with equal depth. We recommend what's right for your workload, not what we're most comfortable with.

04

No lock-in

Everything is documented and owned by you. We build things designed to outlast the engagement — and leave your team more capable than we found it.

Our approach

How we think about the work

01

AI/ML belongs in the SDLC

Too many teams treat model deployment as a separate discipline from software delivery. We don't. AI model pipelines go through the same branch protection, review gates, staging environments, and rollback capabilities as any production service — because a broken model in production is just as serious as a broken API.

02

Multi-cloud isn't a compromise

AWS and Azure each have genuine strengths. SageMaker is best-in-class for ML pipelines. Azure AI Gateway and MCP server support give Azure an edge for governed LLM exposure. We help clients use both strategically rather than forcing everything into one provider.

03

Security is a pipeline concern

Shift-left security means SAST, secret scanning, container image scanning, and policy-as-code checks run in CI — before anything reaches staging. By the time a change hits production, it's been through multiple automated gates and human review. This is how we build repeatably.

04

AIOps changes how you operate

Traditional monitoring tells you something broke. AIOps tells you it's about to break — and often fixes it before you're paged. We've seen teams go from 300 alerts per week to 40 meaningful ones. That's the difference between a team that's reactive and one that's engineering.