Azure vs. AWS: Migration Considerations for Enterprises

Migrating workloads between AWS and Azure isn’t a one-click move. The considerations that actually decide success are service mapping (AWS ↔ Azure equivalents and the differences that bite), identity and security (Entra ID vs IAM, different default patterns), networking (VPC vs VNet, routing, hybrid connectivity), data movement and egress (real costs and time for large datasets), people and skills (your team’s expertise, training, and runbook reality), and total cost (compute, data egress, reservations, plus people and risk). Most enterprises end up multi-cloud rather than fully migrating one way — Azure for Microsoft-aligned workloads, AWS for AWS-native workloads, with deliberate choices on what lives where.

This guide covers the considerations honestly. (Service details evolve — confirm against current vendor documentation.)

Why Enterprises Migrate AWS → Azure (and Vice Versa)

Common drivers: tighter integration with Microsoft 365 and Entra ID, US-relevant compliance posture, consolidation around the Microsoft ecosystem, AI services (Azure OpenAI), and contract or pricing leverage. Drivers the other way are similar in shape but pointed at AWS-native strengths.

Service Mapping: AWS vs Azure

Mappings are approximate — feature parity varies. Plan service-by-service rather than assuming clean equivalence.

Identity, Security, and Network Differences

Identity models differ (Entra ID is identity-first across Microsoft, IAM is AWS-native with different defaults), security service portfolios differ (Defender for Cloud, Sentinel vs GuardDuty, Security Hub), and network primitives differ in subtle ways (VNets vs VPCs, routing models, peering). Plan to re-architect rather than translate.

Data Movement and Egress

Moving large datasets between clouds takes time and costs money — egress charges and transfer time are real budget items. Azure Data Box, AWS Snowball, and online transfer tools all play a role; for very large datasets the planning, validation, and cutover phases dominate.

People, Skills, and Operating Model

Your team’s skills are usually the biggest constraint. AWS and Azure require different expertise; bridging that gap with training, hiring, and partners is part of the project. Don’t underestimate operating-model differences (deployment patterns, monitoring, incident response).

Costs — Beyond the Sticker

Real migration cost is not just compute and storage — it’s data egress, professional services, training, parallel-run periods, refactoring vs lift-and-shift trade-offs, and risk. Reservations and savings plans on each side matter. (See Azure cost management — how to control your cloud spending.) Centric delivers cloud migrations through its Azure cloud services.

Considering an AWS → Azure move? Explore Centric Azure cloud services or talk to the Centric team.

Frequently Asked Questions

What should we consider migrating from AWS to Azure?

Service mapping and feature differences, identity and security model, networking, data movement and egress costs, your team’s skills and operating model, and total cost including services and risk. Most enterprises end up multi-cloud, not fully migrated.

Can we lift-and-shift AWS to Azure?

For IaaS-heavy workloads, often yes (VMs, storage), but expect re-architecture for identity, networking, and managed services. PaaS-heavy AWS workloads typically need design work to fit Azure equivalents.

How expensive is data egress?

Real and worth planning for. Moving large datasets between clouds incurs egress charges and transfer time. For very large data, dedicated transfer tools and phased cutovers reduce both. Don’t treat egress as a footnote.

Will multi-cloud be cheaper?

Sometimes, often not. Multi-cloud has costs (operations, skills, integration, duplication). The right reason to be multi-cloud is workload fit and resilience, not price arbitrage. Adopt it deliberately.

See Centric Azure Cloud Services