Cloud engineering and consulting in Australia
Cloud consulting in Australia from the engineers who then build and run the platform. We structure AWS accounts, networks, pipelines and monitoring so the software on top ships on time, survives a bad deploy and costs what you expected.
- Infrastructure as code in your own AWS account
- A small senior team that designs, builds and operates the platform itself
- Each stage quoted in writing; leave at any boundary
For teams whose platform has become the bottleneck
The product works; getting a change into production is the hard part. One person knows how the account is wired, and outages are reported by customers. That is a platform engineering problem.
We are AWS-first. Our AWS consulting covers the choice of services; this page covers the discipline around them. Where AI workloads are involved, our AI engineering team designs the model layer on top.
A good fit if…
- Your software runs on AWS and deploys depend on one person and one laptop
- Resources were created by hand and nobody knows what breaks if they change
- An AI feature needs somewhere to run that security will approve
Three platform problems we fix
The hand-built account
Resources clicked together in the console, with no code describing them. We import them into Terraform or CDK, reconcile the drift, and make a pull request the only way infrastructure changes.
Release-day dread
Deploys happen after hours because nobody trusts the rollback. We build pipelines that test, scan and promote one artefact through each environment, with a rollback path rehearsed in advance.
Customers as the monitoring system
Logs exist but nothing pages anyone. We define what healthy looks like, alarm on the symptoms users feel, and route each alarm to a person with a runbook.
What our cloud engineering and platform engineering work covers
Account and landing-zone structure
One account per environment and workload, single sign-on with short-lived credentials, guardrails against dangerous actions, central audit logging.
Infrastructure as code
Terraform or CDK, shared modules, locked remote state, plans reviewed in pull requests, scheduled drift detection. Existing resources imported, not rebuilt.
CI/CD, environments and releases
Build once, promote the same artefact through development, staging and production. Preview environments where they earn their cost, gated promotion, rehearsed rollback.
Networking, security and secrets
Private subnets by default, security groups in code, CloudFront and API Gateway at the edge, one least-privilege IAM role per service, secrets in a managed store with rotation.
Observability, reliability and recovery
Structured logs, metrics and traces in CloudWatch, symptom-based alarms with a runbook each, agreed service level objectives, multi-AZ where it matters, backups proven by restoring them.
Cost management
Tagging enforced in code, budgets that alert per account, right-sizing from measured use, commitment discounts once usage is stable, a monthly review your team owns.
The platform baseline we build to
Six layers, each with an owner, a code definition and a way to observe it. A small workload may skip a box, never a layer.
- Accounts and IAMwho may do whatOne account per environmentSingle sign-onShort-lived credentialsLeast-privilege rolesCentral audit log
- Networkwhere traffic may goVPC per accountPrivate subnetsCloudFrontAPI GatewayTLS everywhere
- Computewhere code runsLambdaStep FunctionsECS/FargateEventBridgeSQSGPU inference where justified
- Datawhere state livesRDS/PostgrespgvectorDynamoDBS3 with lifecycle rulesManaged keys per environmentRestore-tested backups
- CI/CD and IaChow change reaches productionTerraform/CDKPlan on pull requestBuild once, promotePreview environmentsRollbackDrift detection
- Observabilityhow you knowStructured logsMetrics and tracesCloudWatch dashboardsSymptom-based alarmsRunbooksCost per workload
This is an AWS baseline because AWS is where we build and operate. We do not run Azure or Google Cloud practices, and we say so up front.
Infrastructure for AI systems, decided case by case
Most AI features start on a managed model API through Bedrock, OpenAI, Anthropic or Google, and that is usually right: no GPUs to schedule, no model to patch. Self-hosted inference earns its place when an open-weight model fits, data cannot leave your account, or volume favours dedicated capacity.
Around the model sits ordinary platform work: Postgres with pgvector until scale says otherwise, SQS and Fargate pipelines for ingestion and embedding, token and GPU spend reported per feature. Our production AI engineering practice designs that layer.
Decisions we make with you
- Managed model API or self-hosted inference endpoint
- Postgres with pgvector or a managed vector database
- Which region each component runs in and what may leave it
How a platform engagement runs
Commercially it follows the five-stage model in how we work. Technically, it moves through five phases.
- AssessRead-only access, an inventory of what exists, and what would fail an audit or an outage.
- DesignTarget accounts, network, compute, pipelines and recovery objectives, agreed with your team.
- Build the platformLanding zone, IaC modules, pipelines, secrets and alarms, in slices usable as they land.
- Migrate or launchWorkloads moved in with a cutover plan and rollback path, or a new product's first deploy.
- Operate and improveRunbooks, cost review and shared on-call, then a handover that leaves you running it.
Where platform work begins
Landing zone for a growing SaaS product
One account holding everything becomes several, with tenant data boundaries, per-environment pipelines and the cost split a board asks for. Often paired with our SaaS development.
Modernising a monolith on a single server
Containerise, move the database to RDS with backups, add a pipeline, then cut over with a rehearsed rollback. Often the start of wider software engineering consulting on the application.
Platform ready before the first feature
A new product from our custom software development team gets accounts, pipeline and alarms in its first weeks, so nothing is retrofitted.
Somewhere approved for an AI feature to run
A model API or inference endpoint, a vector store and an embedding pipeline inside your own account, with the network and IAM boundaries security asked for. The platform half of AI feature development.
Serverless, containers or managed services: when each fits
Most estates use all three. The mistake is picking one for everything.
| Consideration | Serverless (Lambda, Step Functions) | Containers (ECS/Fargate) | Managed services |
|---|---|---|---|
| Suits | Event-driven, bursty, short-lived work: APIs, webhooks, orchestration, agent steps | Long-running processes, custom runtimes, steady throughput | What you should not build: databases, identity, queues, model hosting |
| Cost shape | Per invocation; near zero idle, climbs at sustained volume | Per running task; predictable at steady load, wasteful when idle | Per usage or instance; you pay instead of staffing it |
| Our default | APIs, integrations and workflow orchestration | Ingestion, batch jobs, self-hosted inference | Postgres, identity, queues, edge delivery, model APIs |
A software company doing the cloud work, not a reseller
We operate what we design
The platforms we design carry systems we build and operate in production. That changes how carefully the alarms get written.
Your account, your code
Terraform, pipelines and runbooks live in your repositories and AWS organisation from the first paid stage. No retainers; take it in-house when you choose.
Quoted per stage
Nothing is billed until Discovery. From there each stage, and each 12-week Implementation block, carries a written price before work starts, and every boundary is an exit.
Platforms we have shipped
A custom SaaS platform built for Modedu; secure patient-data storage built for DisabilityAssessments by the same team. Client-reported outcomes are on our case studies page.
Where this work usually leads next
AWS consulting
Choosing and configuring the AWS services themselves.
AWS consulting in AustraliaAI engineering
Retrieval, model routing and evaluation on top of the platform.
Production AI engineeringCustom software
Applications built to run on infrastructure described in code.
Custom software developmentSoftware engineering consulting
Architecture review and delivery practice for an existing codebase.
Architecture reviewSaaS development
Multi-tenant products that need account, data and cost boundaries.
Custom SaaS platformsAI automation
Agentic workflows that run on queues, schedules and approval steps.
Agentic workflow automationQuestions we get about cloud and DevOps consulting
Anything not covered below is a fair question for the intro call, which costs nothing.
Book a free intro callDo we need Kubernetes?
Usually not. Lambda for event-driven work and ECS/Fargate for containers serve most teams we work with, and neither leaves you a cluster to operate. Kubernetes makes sense when you already run it well or a workload needs its scheduling model.
How do you hand over?
Throughout, not at the end. Infrastructure as code lives in your repositories from the start, every alarm has a runbook, and your engineers review and merge pull requests with us. The goal by the end of a block is a production deploy your team runs without us in the room.
Can you work with our existing engineering team?
Yes, and it works better that way. We join your repositories, review process and on-call rotation rather than run a parallel one, and train the people who will own the platform.
What does "operate" include?
Shared on-call with your team, alarms answered with the runbooks we wrote, the monthly cost review, patches and dependency updates, and recovery rehearsals. It is priced in writing like every Implementation block and ends when you decide; no retainer is needed.
What does cloud consulting cost?
The first two conversations are free. After that an assessment runs as Discovery from a fixed fee, design as Strategy priced up front, and the build as 12-week Implementation blocks, each quoted in writing with an exit at every boundary. The IP is yours from the first paid stage.
Tell us how a change reaches production today
That one answer usually shows where to start. Two free conversations first; after that, every stage is quoted in writing and you can stop at any boundary.