Cloud consulting, without the multi-cloud tax.
AWS, Azure, GCP: we pick what fits your workload, not our commission. Architecture reviews, migrations, and landing zones designed by engineers who run production — and when the bill is the problem, the cost practice starts at cloud billing & FinOps.
An InfraZen cloud consulting and migration engagement covers architecture reviews, migrations, landing zones and governance, security and compliance, disaster recovery, and FinOps across AWS, Azure, and GCP, vendor-agnostic by design. Reviews follow the provider's own well-architected framework and end in a prioritized fix list; migrations ship in waves with rollback at every step, never a big-bang weekend.
Key takeaways
- Vendor-agnostic is the whole point: the right platform for the workload, not the platform with the best margin.
- Migrations ship in waves with rollback at every step — assessment, wave plan, cutover playbooks; never a big-bang weekend.
- Architecture reviews follow the provider's own well-architected framework and end in a prioritized fix list, not a slide deck.
- Cost is its own practice: the audit-first entry point and the savings math live at cloud billing & FinOps.
Cloud, done right.
Architecture reviews
Well-Architected Framework audits across AWS, Azure, and GCP. Honest assessments that surface what's actually risky, not what's theoretically suboptimal.
- AWS Well-Architected Review (all 6 pillars)
- Azure & GCP architecture framework audits
- Prioritized remediation roadmap
- Executive summary + engineering detail docs
FinOps & cost optimization
Tagging hygiene, rightsizing, commitment strategy, waste elimination, and ongoing governance. The full cost practice — including the audit-first entry point and the honest savings math — lives at cloud billing & FinOps.
- Cost & Usage Report analysis and tagging hygiene
- Rightsizing (compute, storage, RDS, data transfer)
- Reserved Instances, Savings Plans, and Spot strategy
- Automated waste elimination (idle resources, orphaned volumes)
Cloud migration
From on-prem to cloud, or between clouds. Lift-and-shift, replatform, or refactor, all scoped realistically, executed in waves, with clean rollback at every step.
- Migration assessment and wave planning
- Lift-and-shift with minimal code change
- Replatform to managed services
- Data migration and cutover playbooks
Landing zones & governance
Multi-account architectures that enforce security, cost, and compliance guardrails from day one. AWS Control Tower, Azure Landing Zones, GCP Cloud Foundation.
- AWS Organizations and SCP design
- Azure Management Groups and Policy
- GCP Folder structure and Org Policy
- Network topology (Transit Gateway, hub-and-spoke)
Security & compliance
Hardening across identity, network, data, and runtime. Compliance-ready builds for SOC 2, ISO 27001, HIPAA, PCI-DSS, and GDPR. Security in the delivery pipeline itself is DevSecOps consulting.
- IAM hardening (least privilege, MFA, federation)
- Network segmentation and zero-trust patterns
- Encryption at rest/in transit, KMS/Key Vault key rotation
- Audit logging, CloudTrail, and SIEM integration
Disaster recovery
Tested DR plans with real RPO and RTO numbers, not just a document. Multi-region, cross-cloud, and backup strategies that survive audits and actual outages.
- RPO/RTO discovery and tiering
- Multi-region active-active/active-passive design
- Backup strategy and restore testing
- Annual DR drill facilitation
AWS, Azure or GCP: which cloud should you choose?
There is no universal winner. The decision is about fit with your workloads, your existing skills and your ecosystem, not which cloud is objectively “best”. The rough shorthand from InfraZen’s side-by-side comparison of AWS, Azure and GCP: AWS wins on breadth, maturity, ecosystem and hiring pool; Azure on Microsoft estates, hybrid and enterprise agreements; Google Cloud on data, analytics, Kubernetes and AI/ML.
On-demand list prices for equivalent compute, storage and egress sit within a few percent of each other, so the provider you choose matters far less to the bill than the FinOps discipline you practise once you are on it. The discount models differ in shape — Savings Plans and Reserved Instances on AWS, reservations and savings plans plus the Hybrid Benefit on Azure, committed use plus automatic sustained-use discounts on GCP — which is why an honest total-cost comparison models your real usage instead of reading percentages off a landing page.
For most teams the right move is one primary cloud, chosen on existing skills and workloads and committed to for the discounts, with multi-cloud treated as a deliberate exception rather than a default. InfraZen is an official reseller and partner across all three, so there is no logo to sell you.
How should you structure your cloud accounts?
A landing zone is a pre-built, multi-account cloud foundation that sets up identity, networking, security, logging and billing structure before any workload arrives. Workload teams then deploy into accounts that inherit its guardrails, usually provisioned through infrastructure as code.
Account structure is a security decision and a cost decision at the same time. AWS’s own multi-account guidance says dedicated accounts establish “clear security, access, and billing boundaries” that keep a problem in one part of the estate from spreading to the rest, and because an account is the default unit AWS allocates costs to, separate accounts per business unit and workload make spend easier to report, forecast and budget. All three clouds give you the same building blocks under different names:
| AWS | Azure | GCP | |
|---|---|---|---|
| Top of the hierarchy | Organization root, administered from the management account | Root management group | Organization node |
| Grouping layer | Organizational units (OUs) | Management groups | Folders |
| Where workloads run | Member accounts | Subscriptions | Projects |
| Guardrails that inherit downward | Service control policies | Azure Policy and role assignments | Organization policies |
The providers’ own guidance on arranging those blocks converges on a few rules:
- Keep workloads out of the top of the hierarchy. AWS recommends keeping all resources out of the management account, not least because service control policies do not apply to it. On Azure, a default management group for new subscriptions keeps them from landing directly under the root.
- Separate the platform from the products. AWS’s recommended OU structure puts security and shared infrastructure accounts in their own Security and Infrastructure OUs, apart from the Workloads OU. Azure’s equivalent is a platform management group under the root; on GCP, Google recommends separate folders for bootstrap resources and common services.
- Split production from non-production at the account boundary. Separate accounts for production and non-production keep each environment’s resources and data apart by default, so each can carry its own security and operational controls. Azure’s guidance is to do this with separate subscriptions, not separate management groups per environment.
- Give experiments a sandbox. AWS’s Sandbox OU groups the accounts used for experimentation, typically with limited access to production resources; Azure’s sandbox management group isolates experiments from development, test and production environments.
- Set guardrails high, and keep them few at the root. On AWS, a deny attached at the root or an OU applies to every account beneath it; on GCP, every descendant inherits an organization policy by default. Azure’s guidance is to limit policy assignments at the root management group, which keeps inherited policies easier to debug further down.
- Provision new accounts from code. Each new account should arrive with the landing zone’s identity, networking, security, logging and billing structure already in place, which is the Terraform side of a landing zone.
In engagement terms this is the landing-zones line in the deliverables above: AWS Organizations and SCP design, Azure management groups and Policy, GCP folder structure and Org Policy, and the network topology (Transit Gateway, hub-and-spoke) that connects the accounts.
How a migration engagement actually runs
Week one is discovery, and it is mostly reading: inventorying what runs, what talks to what, which dependencies are load-bearing and which are folklore. The output is a wave plan — workloads grouped by risk and coupling, each wave with its own cutover playbook and a rollback path that has actually been rehearsed. Stateless edges move first; the database moves last, with the most ceremony.
Then waves ship. Lift-and-shift where the workload is fine and the data center is the problem; replatform to managed services where the ops burden is the problem; refactor only where the architecture itself is the constraint — scoped honestly, because refactors are where migration budgets go to die. Every wave ends with the same question: does the runbook let your team do the next one without us?
If Kubernetes is part of the target architecture, the platform work is a separate discipline — see Kubernetes consulting and the managed vs self-hosted decision. What the estate costs once it lands is the billing practice's job.
What does a zero-downtime migration look like in practice?
State first, platform second, traffic last. That is the order in InfraZen’s e-commerce EKS migration, a representative engagement in which 47 services moved from a monolithic PHP stack on EC2 to EKS in eight waves over sixteen weeks, with zero customer-facing downtime. Two earlier attempts by another vendor had failed the same way: user sessions lived in local application state, so every cutover logged customers out mid-cart. The third attempt fixed that root cause before any traffic moved.
| Weeks | Workstream | What shipped |
|---|---|---|
| 1–4 | Remove the blocker | Session state externalised to Redis (ElastiCache) while everything still ran on EC2; the EKS platform built with Terraform, node groups sized from real traffic data |
| 3–6 | Delivery rails | Argo CD GitOps delivery, so every service’s desired state lived in Git; Istio for weighted traffic shifting; a container build pipeline with image scanning |
| 5–14 | Eight waves | Services moved in dependency order, least critical first; a load test at 3× observed peak before every wave; traffic shifted 1→10→50→100% per service, with automatic rollback triggers on error-rate and latency SLOs |
| 10–16 | Safety net & handover | The old EC2 fleet kept warm for instant DNS-level rollback until two weeks after the client’s biggest sale; runbooks, dashboards and the Argo CD workflow handed to the client’s platform team |
Rollback stays warm. A big-bang cutover stakes the business on one night going well. With small waves, the worst outcome at any point is one service’s traffic sliding back to a fleet that is still running. That costs duplicated infrastructure for a few weeks, and the plan says so up front.
Every wave is load-tested before it ships. Testing at three times observed peak caught connection-pool exhaustion, once forty-seven services each brought their own database pool, and a hot cache keyspace concentrating on one Redis shard. Both were fixed before the wave that would have exposed them.
Scope stays disciplined. The relational database stayed on RDS instead of moving onto the cluster mid-flight, and two batch workloads with no scaling problem stayed on EC2 until after the sale. The outcome: deploys went from two a week to four a day, and a 5.2× sale-day traffic peak was absorbed by autoscaling with no human intervention.
The wave structure, tooling choices and rollback discipline are how InfraZen runs a migration of this shape, published as a representative engagement while client-cleared named case studies are prepared. Read the wave-by-wave write-up → The ways a first attempt usually dies are in three Kubernetes migration mistakes.
| time to first savings | quick wins in 2–4 weeks · commitments & architecture 6–12 weeks |
|---|---|
| engagement models | strategic advisory · project delivery · managed devops & sre |
| what you get in writing | savings report scored by effort vs payoff, migration wave plans, rollback paths |
| pricing stance | fixed fees; see cloud billing & FinOps for the audit-first entry point |
What the bill looked like afterwards.
A representative engagement, anonymised at the client's request; the numbers are the ones we measured. Read the AWS bill-reduction engagement →
Common questions.
What does a cloud architecture review cover?
Workload placement, network topology, IAM boundaries, resilience posture, data-residency constraints, and the cost consequences of each. The deliverable is a written review against the provider's well-architected framework with a prioritised fix list, so you know whether the estate you have is the estate you need.
Are you locked into one cloud provider?
No. We're vendor-agnostic by design and work across AWS, Azure, and GCP. We recommend what fits your workload, compliance requirements, and team skills, not what pays us the highest partner commission.
Can you help with cloud migration from on-prem?
Yes. We run discovery, wave planning, and execute migrations in phases with clean rollback at every step. Whether it's lift-and-shift, replatform, or refactor, we scope realistically based on your timeline and risk appetite.
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