TL;DR: An AWS to Azure migration runs as a five-stage pipeline, discover, assess, replicate, test, and cut over, with Azure Migrate handling servers, Database Migration Service handling databases, and AzCopy or Azure Data Factory moving S3 data into Blob Storage. Map every AWS service to its Azure counterpart before anything moves, build the landing zone first, and fund the project with Azure Hybrid Benefit, reservations, and your existing Microsoft agreement.
Working through an adjacent platform decision? See also Data Engineering Trends 2026 · Databricks Metastore Setup and Hive Migration .
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Why Enterprises Are Moving Workloads From AWS to Azure The center of gravity in enterprise IT has shifted. Companies that run Microsoft 365, license Windows Server and SQL Server, and now want Copilot and Azure OpenAI in production are asking why their infrastructure spend still sits with a second vendor.
An AWS to Azure migration turns that scattered spend into one negotiating position on an Enterprise Agreement, activates licenses the company already owns, and puts workloads next to the Microsoft AI and analytics stack. The move itself, though, is a serious engineering project with well-documented failure modes, and it deserves a better plan than a cloud transformation strategy slide.
This guide is that plan. In this article, we’ll cover why teams migrate, how to assess an AWS estate, the full AWS to Azure service mapping, the Azure Migrate toolchain, phased execution, data migration approaches, networking and identity gotchas, cost levers, security mapping, and the pitfalls that wreck timelines.
Key Takeaways An AWS to Azure migration succeeds or fails in assessment, where you inventory workloads, map dependencies, and assign each application a rehost, replatform, or refactor treatment. Every core AWS service has an Azure counterpart. EC2 maps to Virtual Machines, S3 to Blob Storage, Lambda to Functions, RDS to Azure SQL, and IAM to Microsoft Entra ID. Azure Migrate treats AWS EC2 instances as physical servers and replicates them with an installed agent, and test migrations rehearse cutover without touching production. A landing zone with identity, networking, governance, and cost controls must exist before the first wave moves, or every later wave inherits the shortcuts. Wave-based execution, a low-risk pilot first and the hardest dependency clusters last, keeps risk contained and builds a delivery rhythm that compounds. Azure Hybrid Benefit, reservations, and consumption commitments cut the landed cost substantially, but AWS egress fees and double-running both clouds belong in the business case up front. What to Assess Before You Move a Single Workload Most migration horror stories trace back to a skipped assessment. Teams that discover an undocumented dependency during cutover weekend did not have a tooling problem, they had an inventory problem that stayed invisible until the worst possible moment.
Start with a full inventory of the AWS estate. That means every EC2 instance with its size and utilization history, every S3 bucket with its access patterns, every RDS and DynamoDB database, every Lambda function, and the VPC topology, load balancers, and DNS zones that tie them together. A structured data migration checklist keeps this phase honest.
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Then map dependencies. Applications rarely fail because a VM did not boot, they fail because a hardcoded IP, an IAM role assumption, or a message queue no one documented broke the chain. Dependency mapping decides which workloads must move together as a group.
Finally, assign each workload one of five treatments. This decision drives everything downstream, from the type of data migration you run to the skills each wave needs.
Rehost. Lift the VM as-is onto Azure Virtual Machines. Fastest path, no code changes, and the default for stable workloads under time pressure.Replatform. Swap components for managed equivalents during the move, such as RDS to Azure SQL Database, without rewriting application logic.Refactor. Rework the application for Azure-native services such as Functions, Container Apps, or AKS. Reserve this for systems where the payoff is clear.Retire. A migration is the best audit you will ever run. Ten to twenty percent of most estates turns out to be abandoned and can simply be switched off.Retain. Some workloads stay on AWS for contractual, latency, or dependency reasons. Plan the coexistence rather than pretending it away.Weigh each treatment against business risk, not engineering preference. The risks in data migration concentrate in systems with heavy data gravity and thick integration webs, so score those explicitly before committing dates to a steering committee.
The AWS to Azure Service Mapping Table Service mapping is the reference document your architects will open every day of the project. The two platforms cover the same ground, but the resources are organized differently, and a few mappings hide behavior changes that matter at cutover.
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Explore Migration Services The table below covers the mappings that decide most enterprise migrations. Microsoft maintains the exhaustive version in its Azure for AWS professionals documentation, which is worth bookmarking for the long tail.
Category AWS service Azure counterpart Compute Amazon EC2 Azure Virtual Machines Compute AWS Lambda Azure Functions Compute Amazon EKS Azure Kubernetes Service (AKS) Compute Amazon ECS / Fargate Azure Container Apps Compute AWS Elastic Beanstalk Azure App Service Storage Amazon S3 Azure Blob Storage Storage Amazon EBS Azure managed disks Storage Amazon EFS Azure Files Database Amazon RDS Azure SQL Database / Azure Database for PostgreSQL and MySQL Database Amazon DynamoDB Azure Cosmos DB Database Amazon ElastiCache Azure Cache for Redis Analytics Amazon Redshift Azure Synapse Analytics / Microsoft Fabric Analytics Amazon Kinesis Azure Event Hubs Messaging Amazon SQS / SNS Azure Service Bus / Event Grid Networking Amazon VPC Azure Virtual Network (VNet) Networking Elastic Load Balancing Azure Load Balancer / Application Gateway Networking Amazon Route 53 Azure DNS + Traffic Manager Networking AWS Direct Connect Azure ExpressRoute Networking Amazon CloudFront Azure Front Door Networking Amazon API Gateway Azure API Management Identity AWS IAM Microsoft Entra ID + Azure RBAC Security AWS KMS Azure Key Vault Security Amazon GuardDuty Microsoft Sentinel Governance AWS Config / CloudTrail Azure Policy / activity log Monitoring Amazon CloudWatch + X-Ray Azure Monitor + Application Insights IaC and DevOps AWS CloudFormation / CodePipeline ARM templates, Bicep / Azure DevOps, GitHub Actions
Treat the table as a starting bid, because parity is rarely exact. DynamoDB and Azure Cosmos DB differ in consistency models and pricing math, and Redshift to Synapse or Fabric is a re-architecture decision rather than a swap. Organizational structure differs too, since AWS accounts become Azure subscriptions under management groups.
Tooling deserves the same scrutiny as services. Our comparison of the leading Azure migration tools breaks down when Azure Migrate alone is enough and when you need third-party help for a mixed estate.
Choose the Right Migration Path: Rehost, Replatform, Refactor, or Replace The service mapping table earlier in this guide answers “what is the Azure equivalent of this AWS service.” A separate, equally important question is “how much should this workload change on the way over.” Four common paths, in order of increasing effort and increasing long-term payoff:
Rehost (lift-and-shift). Move the workload to Azure with minimal changes, typically EC2 to Azure VMs with the same OS and application stack. Fastest path, lowest risk, but carries forward any inefficiency or technical debt the workload already had. Right for workloads under time pressure to exit AWS, or ones nearing end-of-life where further investment is not justified.Replatform. Move to Azure with targeted optimizations that do not require rearchitecting the application, such as moving a self-managed database on EC2 to Azure Database for PostgreSQL, or a containerized app on self-managed Kubernetes to Azure Kubernetes Service. Captures meaningful operational savings without a full rebuild.Refactor. Rearchitect the application to take advantage of Azure-native services, such as decomposing a monolith into microservices on AKS or moving batch processing to Azure Functions. Higher effort and longer timeline, but the workload gained genuinely new capabilities rather than just changed its hosting location.Replace. Retire the workload entirely in favor of a SaaS or Azure-native equivalent, common for undifferentiated capabilities like internal ticketing, email, or generic reporting tools where a managed service costs less to run than a custom migration.Most enterprise migrations use all four paths simultaneously across different parts of the estate: rehost the workloads under time pressure, replatform the ones with clear operational wins, refactor the strategic differentiators, and replace the commodity capabilities. A single-path strategy applied to an entire portfolio almost always under- or over-invests in at least some of the workloads.
Build the Azure Landing Zone Before Wave One Migrating into an empty subscription is how shadow architecture gets born. Before any workload moves, stand up a landing zone, the pre-configured Azure environment that carries identity, network topology, governance, and cost controls as code.
Microsoft’s Cloud Adoption Framework landing zone guidance defines the reference design. In practice, five pieces must exist on day one for the migration to land cleanly.
First, an identity foundation in Microsoft Entra ID with role assignments that mirror the duties your AWS IAM roles carried. Second, a hub-and-spoke network where shared services such as firewalls and DNS live in the hub and each application team gets a spoke VNet. Sound cloud architecture at this layer prevents years of rework.
Third, Azure Policy guardrails that enforce tagging, allowed regions, and encryption defaults automatically. Fourth, budgets and cost alerts wired into Microsoft Cost Management before spend starts. Fifth, a subscription layout that separates production from non-production, which also settles the cloud-first versus cloud-native question per environment rather than in the abstract.
Teams that skip this step pay for it in wave three, when inconsistent tagging, flat networks, and unmanaged spend surface at the same time. The landing zone is boring work, and it is the highest-return week of the whole program.
How Azure Migrate Handles AWS Workloads Azure Migrate is Microsoft’s hub for discovery, assessment, and server replication, and it works against AWS with one conceptual trick. It treats EC2 instances as physical servers, so migration runs agent-based rather than through hypervisor snapshots.
The flow starts with a new Azure Migrate project in your subscription. For discovery, you either deploy the appliance to enumerate your estate or import an inventory export, and each discovered server gets a readiness verdict, a right-sized Azure VM recommendation, and a monthly cost estimate. The assessment output is where utilization data pays off, since most teams find their AWS instances oversized against actual load.
Replication then runs through the Migration and modernization tool. A replication appliance coordinates traffic, a mobility service agent installs on each EC2 instance, and block-level replication streams the servers into Azure over a secure channel while production keeps running. Microsoft documents the exact sequence in its AWS VM migration tutorial .
The step teams skip at their peril is the test migration. Azure Migrate spins the replicated server up in an isolated VNet so you can boot it, run smoke tests, and throw it away without touching production or interrupting replication. Cutover only happens after a clean test, and the same discipline applies to Azure data migration workstreams running alongside the servers.
Databases follow a parallel track through Azure Database Migration Service , which handles schema and data movement from RDS engines into their Azure equivalents with minimal-downtime options. Round out the toolbox with our guide to data migration tools for the cases neither service covers.
A Phased Execution Plan From Pilot to Cutover Big-bang cloud migrations fail predictably, so enterprise moves run in waves. Each wave is a dependency-complete group of workloads that moves as a unit, and the sequence builds skill before it takes risk.
Step 1. Prove the pipeline with a pilot. Pick one low-risk, internally-facing application with a real database and real users. The pilot’s job is to exercise every stage, replication, testing, cutover, and rollback, while the blast radius is small.Step 2. Group the estate into waves. Use the dependency map to cluster workloads that must move together. Sequence waves from lowest risk to highest, and keep each wave small enough to cut over inside one maintenance window.Step 3. Replicate and validate continuously. Start replication for a wave one to two weeks before its window so initial sync completes early. Track replication health daily and fix lagging servers before, never during, the cutover weekend.Step 4. Run a test cutover for every wave. Boot the wave in an isolated VNet, run functional and performance smoke tests, and rehearse the runbook. Rigorous data migration testing here is what makes the production window boring.Step 5. Execute the production cutover. Freeze changes, complete a final delta sync, fail over, repoint DNS with pre-lowered TTLs, and run the validation checklist. Keep the AWS side intact but idle as the rollback path.Step 6. Stabilize, optimize, then decommission. Hold a one-to-two-week hypercare window per wave, right-size against observed load, then shut down and clean up the AWS resources so the double-run cost stops.Run the whole cadence inside a written data migration framework with named owners for each step. The teams that publish a wave calendar and hold a short retro after each cutover get measurably faster by wave three, because every fix feeds forward.
Data Migration Approaches for Storage, Databases, and Warehouses Servers are the easy part of an AWS exit. Data carries gravity, cost, and downtime constraints, so each data class needs its own movement strategy rather than one blanket approach.
For object storage, AzCopy copies directly from S3 to Blob Storage using S3 APIs on the source side, and Microsoft documents the pattern in its AzCopy S3 guide . For very large or ongoing transfers, Azure Data Factory’s S3 migration guidance covers partitioned, resumable copies at petabyte scale, with checkpointing that a hand-rolled script will not give you.
Relational databases move through Database Migration Service, with offline migrations for systems that tolerate a window and online, continuous-sync migrations for those that do not. Plan a schema compatibility pass first, since engine versions, collations, and extensions diverge between RDS and Azure’s managed engines, and validate row counts and checksums as part of data quality during migration .
NoSQL and caching layers usually replatform rather than lift. DynamoDB workloads map to Cosmos DB but need partition key and consistency model review, while ElastiCache moves to Azure Cache for Redis with little friction. Streaming pipelines built on Kinesis re-land on Event Hubs, which most teams fold into a broader cloud data integration redesign.
The analytics estate is the strategic decision. Moving Redshift means choosing between Synapse and Microsoft Fabric as the target cloud data warehouse , and the right answer depends on how much of your BI stack already lives in the Microsoft ecosystem. Our Redshift versus Synapse comparison goes deep on that call, and pipeline conversion itself is a classic ETL migration exercise where automation beats manual rewrites.
Whatever the data class, sequence data movement before application cutover, never during it. A legacy data migration that shares a weekend with an application cutover is two risky projects stacked on one clock.
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Schedule a Demo → Networking and Identity Gotchas That Derail Cutovers Most failed cutover weekends are network or identity stories. The services map cleanly on paper, but the two platforms behave differently in ways that only surface under production traffic.
On networking, VPCs and VNets look alike until the details bite. AWS security groups attach to instances while Azure NSGs attach to subnets and NICs, so translated rules need re-testing rather than re-typing. DNS deserves its own runbook line, because cutover speed is bounded by record TTLs that must be lowered days in advance.
Hybrid connectivity is a coexistence problem. Most enterprises run Direct Connect and ExpressRoute side by side during the transition, with a site-to-site VPN bridging AWS and Azure so waves can talk to workloads that have not moved yet. Size that inter-cloud link for real traffic, since chatty applications split across clouds will find its limits quickly, and route encrypted paths carefully to keep data migration secure in transit.
Identity is the deeper conceptual shift. AWS IAM attaches JSON policies to users, roles, and resources, while Azure layers Entra ID identities over RBAC roles scoped to management groups, subscriptions, and resource groups. Role assumptions and instance profiles become managed identities, and every service-to-service trust needs an explicit redesign, not a mechanical translation.
Secrets and keys carry a hard dependency. Data encrypted with KMS keys cannot simply point at Key Vault, it must be re-encrypted or re-keyed during migration, so inventory every KMS usage early. Miss one and the failure appears as a cryptic access error at three in the morning on cutover night.
What the Migration Costs and Where the Savings Hide An honest business case counts the exit costs, the double-run period, and the levers that make Azure cheaper at steady state. Skipping any of the three produces a number the CFO will eventually dispute.
The exit costs start with data transfer. AWS charges for data leaving its network, roughly nine cents per gigabyte at the first published tier per AWS’s data transfer pricing , which turns a 200 TB estate into a real line item. Add the weeks or months of paying both clouds during waves, and disciplined cloud cost management becomes part of the migration plan rather than an afterthought.
The savings side is where Microsoft-heavy shops win. The table below lists the levers that consistently move the number.
Cost lever What it does Documented impact Azure Hybrid Benefit Applies existing Windows Server and SQL Server licenses with Software Assurance to Azure VMs and databases Up to 85% off pay-as-you-go when stacked with reservations, per Microsoft Reservations and savings plans Commits steady-state compute for one or three years Up to 72% off pay-as-you-go rates for reserved VM instances Consumption commitment (MACC) Folds Azure and eligible Marketplace spend into one negotiated Microsoft commitment Commitment discounts plus partner funding programs on qualifying projects Right-sizing from assessment Uses Azure Migrate utilization data instead of matching AWS instance sizes one for one Cuts oversized compute that lift-and-shift would have carried across Dev/test pricing and spot VMs Moves non-production and interruptible workloads onto discounted rates Deep discounts on environments that never needed production pricing
Azure Hybrid Benefit deserves a licensing review before you model anything, and Microsoft’s Azure Hybrid Benefit documentation spells out eligibility. If your organization signs a Microsoft Azure Consumption Commitment , migrated workloads burn down that commitment, which often changes the deal math more than any per-VM discount.
After cutover, treat cost as an operating discipline. A standing Azure cost optimization practice, reviewing Advisor recommendations, reservation coverage, and orphaned resources monthly, is what keeps the projected savings real a year later.
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Watch the Webinar → Mapping Security and Compliance Controls to Azure Security teams need a control-by-control map, not reassurance. Every detective and preventive control running in AWS must have a named Azure successor before the first production wave, or the estate develops monitoring gaps exactly when change velocity peaks.
The core translations are direct. GuardDuty findings move to Microsoft Sentinel, Inspector’s vulnerability scanning lands in Defender for Cloud, Config rules become Azure Policy assignments, and CloudTrail auditing continues in the Azure activity log with export to a Log Analytics workspace for retention.
Compliance needs re-attestation rather than assumption. Certifications such as SOC 2, HIPAA, or PCI DSS apply to your architecture, so scoped controls, evidence collection, and auditor walkthroughs must be rebuilt against Azure services even though both platforms hold equivalent platform certifications.
Wire observability into the same pass. Standing up Azure monitoring tools per wave, with alert parity against what CloudWatch was watching, means a migrated workload is never invisible between cutover and stabilization.
Rollback Planning and the Hybrid Coexistence Period Every migration plan should assume the first cutover attempt for a given workload might not go cleanly, and build in a way back that does not require a full re-migration from scratch.
Rollback strategy by workload type. For stateless application tiers, rollback is straightforward: keep the AWS deployment warm (not terminated) for a defined window — typically two to four weeks post-cutover — and redirect DNS or load balancer traffic back if Azure-side issues surface. For stateful workloads (databases, data warehouses), rollback is harder because data written post-cutover on Azure needs to sync back to AWS or be reconciled manually. The safest pattern is a dual-write period: the application writes to both AWS and Azure data stores for a defined validation window before AWS becomes read-only and is eventually decommissioned.
The hybrid coexistence period is longer than most plans budget for. Enterprises migrating in phases (recommended over big-bang cutovers for anything beyond a handful of services) typically run AWS and Azure side by side for three to nine months, not weeks. This means budgeting for both cloud bills simultaneously, maintaining two sets of monitoring and alerting, and — the part teams most often underestimate — maintaining network connectivity (VPN or ExpressRoute/Direct Connect) between the two environments for services that have not yet migrated but need to talk to services that have.
Identity is the trickiest coexistence problem. If AWS IAM and Azure AD/Entra ID both need to authenticate users and services during the transition, plan the federation model (which directory is authoritative, how service principals map across clouds) before migration begins — not as an afterthought once services are already split across both environments. Retrofitting identity federation mid-migration has caused more schedule slips in Kanerika’s migration engagements than any single technical mapping issue in the service comparison table above.
Seven Pitfalls That Sink AWS to Azure Migrations Patterns repeat across failed and stalled migrations. These seven account for most of the data migration challenges we see teams hit in cloud-to-cloud moves.
Skipping the dependency map. The undocumented integration always surfaces during cutover. Map before you group waves, not after.Matching instance sizes one for one. Copying AWS sizes into Azure carries years of overprovisioning across. Size from utilization data instead.Translating IAM mechanically. Identity is a redesign, and policy-to-role translation without scope review either breaks applications or over-grants access.Underestimating egress and double-run costs. Data transfer fees and months of parallel billing belong in the approved business case, not in a surprise report.Cutting over without a test migration. The isolated-VNet rehearsal exists so the production window is a repeat, never a first attempt.Forgetting DNS TTLs and rollback paths. Lower TTLs days ahead, script the failback, and keep the AWS side runnable until hypercare ends.Treating the migration as pure infrastructure. Skills, runbooks, and on-call rotations must move too, or the new platform runs with old muscle memory.None of these requires exotic tooling to avoid. They require the assessment, the rehearsal discipline, and the cost honesty described above, applied consistently wave after wave.
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Modernizing After the Move: AKS, Azure Functions, and PaaS The migration does not have to end at rehost. Once a workload is stable on Azure, a second-wave modernization pass often captures value the initial migration left on the table. Common patterns: moving containerized workloads from self-managed Kubernetes or ECS onto Azure Kubernetes Service to reduce cluster management overhead; converting scheduled batch jobs or event-driven processing from EC2-based cron jobs to Azure Functions, which eliminates idle compute cost entirely for spiky workloads; and moving self-managed application servers to Azure App Service or other PaaS offerings where the operational burden of patching and scaling shifts to Microsoft.
The right sequencing is migrate first, modernize second. Attempting both simultaneously multiplies risk and makes it hard to isolate whether a post-migration issue came from the platform change or the architecture change. Give the rehosted workload a stable period in production, then plan modernization as a separate, deliberate initiative with its own success criteria.
How Kanerika De-Risks AWS to Azure Migrations Kanerika works these projects as a Microsoft Solutions Partner for Data and AI that holds the Microsoft Advanced Specialization in Data Warehouse Migration to Azure, an audited credential earned in December 2025. The team has spent 10+ years running migration engagements for 100+ enterprise clients, with a 98% client retention rate that reflects how those projects end rather than how they are pitched.
Engagements follow the same arc this guide describes. A short assessment maps the AWS estate, dependencies, and licensing position, then the team designs the landing zone, sequences waves, and runs pilot through cutover with test migrations rehearsed per wave. Post-cutover, right-sizing and reservation planning through its Azure cloud solutions practice keep the steady-state bill aligned with the business case that justified the move.
For the data platform side of an AWS exit, Kanerika brings FLIP, its migration accelerator for pipelines and reporting workloads. FLIP automates the conversion work that consumes most migration hours, and its documented results include an 80% faster migration timeline, 50% lower migration costs, and 65% fewer resources required, which matters when Redshift pipelines are being rebuilt for Synapse or Fabric. Teams migrating Databricks workloads simultaneously should plan their Databricks metastore migration, since workspace attachments and catalog references must move to Unity Catalog naming in the same release window.
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Read the Case Study → The delivery record is third-party verifiable. In a Microsoft-published customer story , Kanerika unified six operational systems for FoodPharma on Microsoft Fabric in seven weeks, consolidating around 1 TB of historical data and cutting cross-functional reporting from two business days to 90 minutes. The same engineering discipline, deterministic conversion, validation gates, and staged cutover, is what an AWS to Azure program needs at enterprise scale.
Practitioner guidance shapes the details clients do not see coming. Kanerika’s migration teams watch for the KMS re-encryption dependency, the security-group-to-NSG behavior drift, and the wave that stalls because one COTS vendor will not certify Azure, because those are the issues that appear in month three, long after the kickoff deck. That experience, plus Microsoft funding programs such as Azure Accelerate that Kanerika can bring to qualifying projects, is the difference between a migration that lands and one that lingers.
License Optimization: The Cost Lever Most Migration Plans Miss The cost comparison in the earlier section covers compute and storage pricing, but the single biggest lever most enterprises leave on the table is license optimization through the Azure Hybrid Benefit. If your organization already owns Windows Server or SQL Server licenses with active Software Assurance, or qualifying subscription licenses, Azure Hybrid Benefit lets you apply those existing licenses to Azure virtual machines and SQL databases instead of paying for a new license bundled into the Azure rate.
For a migration involving a meaningful footprint of Windows or SQL Server workloads, this is not a marginal saving. Depending on the workload mix, Azure Hybrid Benefit combined with Reserved Capacity (a one- or three-year compute commitment in exchange for a discounted rate, similar in concept to AWS Reserved Instances) commonly reduces the compute portion of a migration’s ongoing cost by 30 to 40 percent compared to pay-as-you-go pricing with a fresh license.
Three actions to take before finalizing a migration cost model:
Audit existing license entitlements before scoping the target Azure environment. Many enterprises do not have a clean inventory of which Software Assurance-eligible licenses they hold, which understates the savings available and can lead teams to under-negotiate their committed-use discount.Model Reserved Capacity commitments against your phased migration timeline , not against day-one capacity. Committing to a three-year reservation before wave one workloads are validated risks over-committing to capacity you resize down later. Stagger reservation purchases to align with each migration wave’s validated steady-state usage.Separate license savings from compute savings in your business case. Presenting a single blended savings number to finance makes it hard to audit later. Break out Hybrid Benefit savings, Reserved Capacity savings, and right-sizing savings as distinct line items so the post-migration cost review can validate each assumption independently.For workloads that were candidates for modernization rather than pure lift-and-shift, weigh this against the alternative: refactoring a Windows-licensed VM workload to a PaaS or containerized service can eliminate the license cost question entirely, at the expense of a heavier migration lift. This is a case-by-case tradeoff, not a blanket rule.
The Bottom Line on Moving From AWS to Azure An AWS to Azure migration rewards teams that treat it as an engineering program, assess honestly, map services deliberately, build the landing zone first, and move in rehearsed waves. The tooling is mature, the cost levers are real for Microsoft-licensed shops, and the pitfalls are known and avoidable. What separates smooth migrations from stalled ones is discipline and experience with the gotchas, not secret technology. If your team wants senior migration engineers who have run this play before, talk to Kanerika and pressure-test your plan before wave one.
Frequently Asked Questions How do I migrate from AWS to Azure? Run it as a five-stage pipeline. Inventory and assess the AWS estate, map every service to its Azure counterpart, and build a landing zone first. Then replicate servers with Azure Migrate and databases with Database Migration Service, and rehearse each wave with a test migration in an isolated VNet before cutting production traffic over.
What is the Azure equivalent of AWS EC2 and S3? Amazon EC2 maps to Azure Virtual Machines and Amazon S3 maps to Azure Blob Storage. Other common pairs include Lambda to Azure Functions, RDS to Azure SQL Database, DynamoDB to Cosmos DB, VPC to Virtual Network, IAM to Microsoft Entra ID, and CloudWatch to Azure Monitor. Most mappings are close but not feature-identical.
Does Azure Migrate work with AWS EC2 instances? Yes. Azure Migrate treats EC2 instances as physical servers. A replication appliance coordinates the move, a mobility service agent installs on each instance, and block-level replication streams data into Azure while workloads keep running. You can run a test migration in an isolated network before the final production cutover.
How long does an AWS to Azure migration take? Timelines depend on estate size, dependency complexity, and data volume. A single application can move in a few weeks, while enterprise estates typically run three to twelve months across multiple waves. Assessment and landing zone work usually occupy the first month, and data-heavy or compliance-bound workloads extend the tail.
Is Azure cheaper than AWS? Neither platform is universally cheaper. Azure usually wins for Microsoft-licensed organizations because Azure Hybrid Benefit applies existing Windows Server and SQL Server licenses, and reservations cut compute rates by up to 72 percent. Model your actual workloads with utilization data, including egress and double-run costs, before committing to a business case.
What are the biggest risks in an AWS to Azure migration? The recurring failures are undocumented dependencies discovered at cutover, mechanical IAM-to-Entra translations that break access, unplanned egress and double-run costs, and skipped test migrations. High DNS TTLs also slow rollback when something goes wrong. Each risk is avoidable with dependency mapping, an identity redesign, honest cost modeling, and rehearsed runbooks.
Do I have to move everything from AWS at once? No, and you should not. Wave-based execution moves dependency-complete groups over weeks or months, with a site-to-site VPN or ExpressRoute alongside Direct Connect keeping both clouds connected during the transition. Some workloads legitimately stay on AWS, so plan for coexistence where contracts, latency, or dependencies demand it.
What tools does Microsoft provide for AWS to Azure migration? Azure Migrate covers discovery, assessment, and server replication. Database Migration Service moves relational databases, AzCopy and Azure Data Factory transfer S3 data into Blob Storage, and Azure Site Recovery supports replication scenarios. Cost tooling includes the pricing calculator, the TCO calculator, and Azure Migrate’s built-in per-server cost estimates.
