Author name: Shivam Pokhriyal

Moving to AWS can give businesses greater flexibility, scalability, and control over their IT environment. But an AWS migration is rarely as simple as moving applications from an on-premises server to the cloud.  The decisions made before migration begins often have the biggest impact on cost, performance, security, and downtime.  What should you consider before an AWS migration? Start with your current infrastructure, business objectives, total cost of ownership, workload dependencies, migration strategy, security foundation, and success criteria.  This guide is for IT leaders, infrastructure teams, and business decision-makers evaluating an AWS migration and looking to understand the key decisions before committing to a migration roadmap.  In short, AWS migration starts with decisions—not AWS services.  1. Start With an AWS Migration Assessment  Before deciding what to move, understand what you already have.  An AWS migration assessment should examine:  The goal isn’t simply to create an inventory. It’s to determine what should move, what should change, and what may not need to move at all.  For example, an internal application may seem straightforward to migrate until you discover that it relies on a legacy database or an on-premises system. Moving it without understanding those dependencies could create performance or availability issues.  A proper assessment turns infrastructure data into clear migration priorities and helps shape the AWS migration roadmap.  2. Define Why You’re Migrating  “Move everything to AWS” isn’t a business objective.  Before creating a migration plan, determine what the business actually wants to achieve.  Is the goal to:  These objectives should influence which workloads you migrate first and how you migrate them.  For example, an e-commerce company preparing for seasonal traffic may prioritize scalability and resilience, while a business approaching the end of a data-center lease may prioritize migration speed and infrastructure consolidation.  Your business objective should influence your AWS migration strategy not the other way around.  3. Understand AWS Migration Cost and TCO One common mistake is assuming:  AWS migration cost = your future AWS bill.  It doesn’t.  A realistic business case should compare your current-state Total Cost of Ownership (TCO) with the cost of migration and future AWS operations.  Consider:  After migration, factors such as rightsizing, storage lifecycle management, licensing choices, and data-transfer architecture can significantly affect ongoing costs.  For a deeper look at managing AWS spending, see our AWS Cost Optimization Checklist for 2026.  Cloud cost management should be part of the migration plan from the beginning. Teams can use FinOps practices to establish visibility, ownership, and ongoing control over AWS spending as workloads move into production.  A workload that’s inefficient on-premises can remain inefficient in the cloud if its architecture, resource sizing, and operating model aren’t reconsidered.  4. Choose the Right AWS Migration Strategy  Not every application should be migrated in the same way.  AWS defines seven migration strategies, known as the 7 Rs:  Rehost, relocate, replatform, refactor, repurchase, retain, and retire.  For example:  The important part is choosing the right approach for each workload.  For example, imagine a company with 40 applications running across VMware, SQL Server, and shared file storage. Rather than moving everything at once, the team might identify eight low-dependency applications for the first migration wave, retain five temporarily, retire seven legacy systems, and create modernization plans for the remaining workloads.  That is what a practical AWS migration strategy looks like: different workloads, different decisions.  5. Prepare Your AWS Landing Zone  Your AWS environment should be ready before critical applications arrive.  An AWS landing zone establishes the foundational environment in which workloads will operate. This can include standards for:  The purpose is more than creating an AWS account. A well-planned landing zone gives migration teams a consistent environment for deploying and operating workloads as the organization grows.  It also creates an opportunity to address problems that shouldn’t simply be carried from the existing environment into AWS.  Don’t use migration to reproduce the same architectural and operational problems you already have.  If the current environment has unnecessary complexity, weak access controls, or limited visibility, address those issues as part of the migration plan.  6. Map Dependencies and Plan Migration Waves  Large environments rarely move safely in one giant migration.  Group workloads into logical migration waves based on:  A company might begin with a lower-risk internal application. That experience can help validate the AWS environment, tooling, processes, and operational readiness before moving critical customer-facing workloads.  Dependency mapping is especially important. Applications may share databases, authentication systems, file storage, or network connections that aren’t obvious from a basic infrastructure inventory.  For database-specific planning, see our guide to AWS Database Migration Service and moving on-premises databases to RDS.  Identifying those relationships early makes it easier to decide what should move together, what needs to move first, and what should wait.  7. Define What Success Looks Like  A migration isn’t successful simply because workloads are running in AWS.  Before migration begins, define measurable outcomes such as:  The right metrics depend on the reason for migrating.  If the primary objective is resilience, for example, measuring only the AWS bill won’t tell you whether the migration succeeded. If the goal is cost reduction, infrastructure performance and application availability still need to be monitored.  Your KPIs should connect directly to why you decided to migrate.  AWS Migration Readiness: 5 Questions to Ask First  Before moving forward, make sure you can answer these questions:  Question  What you should know  What are we moving?  Applications, infrastructure, and dependencies  Why are we moving?  Clear business objectives  What will it cost?  Migration expenses + ongoing AWS operating costs  How should each workload move?  The appropriate migration strategy  What could go wrong?  Risks involving security, dependencies, downtime, compliance, and operations  If you can’t answer these questions confidently, the next step probably isn’t migration.  It’s assessment.  Turn AWS Migration Planning Into a Clear Next Step  A successful AWS migration isn’t defined by how quickly workloads leave your data center. It’s defined by whether the move delivers business value without creating unnecessary cost, risk, or operational complexity.  That starts with understanding your current environment, defining clear objectives,

A higher AWS bill isn’t necessarily bad. An AWS bill you can’t explain is.  Your AWS bill went up 25% this month.  Was it because your application grew? Did a new workload go live? Did data transfer increase? Or are you paying for resources no one is using?  For many growing AWS environments, getting the total bill is easy. Understanding what is behind it is harder. [How to Read Your AWS Bill] explains how AWS billing information can be broken down into individual charges and usage types  That’s where AWS cost visibility matters. It helps engineering, finance, and leadership teams understand what is driving cloud spend and whether that spending supports the business.  What Is AWS Cost Visibility?  AWS cost visibility is the ability to understand how cloud spending is distributed across accounts, services, workloads, teams, applications, and business outcomes.  A useful strategy should answer four questions:  For example, a $5,000 increase in EC2 spending tells you that costs went up. Discovering that the increase came from a production application whose traffic grew 40% tells you why.  If revenue from that application grew at the same time, the higher cost may be entirely reasonable.  The point isn’t to react to a bigger number. It’s to understand what changed before deciding what to do about it.  Why AWS Cost Visibility Gets Harder as You Scale  AWS environments rarely stay simple.  A company might start with a few EC2 instances and an RDS database. Later, it may have separate accounts for production, development, analytics, security, and experimentation across multiple Regions.  Then come containers, data pipelines, monitoring, storage, networking, and AI workloads.  The infrastructure isn’t necessarily poorly managed. There is simply more of it, making it harder to connect an AWS charge with the application, team, or business function responsible for it.  AWS provides several ways to improve cost attribution. Account tags can associate account-level spending with structures such as business units, projects, cost centers, and environments, while resource cost allocation tags can connect spending with applications, owners, and workloads. These approaches can work together to improve cost attribution.  Your infrastructure may be organized. Your costs need the same level of organization.  A 5-Layer Approach to AWS Cost Visibility  The goal is to move from where money is spent to what that spending achieves.  1. Account: Where is the money going?  Start with your AWS accounts.  If production represents 70% of your AWS spending and development represents another 15%, you immediately know where most of the budget sits.  For multi-account environments using AWS Organizations, this provides a useful starting point for comparing environments and business units.  Account-level analysis tells you where to investigate. It doesn’t necessarily explain what is driving the cost.  2. Service: What is consuming the budget?  Now examine individual AWS services.  Consider this illustrative example:  AWS Service  Monthly Spend  Change  Amazon EC2  $18,000  +12%  Amazon RDS  $7,500  +18%  Amazon S3  $4,000  +6%  Data Transfer charges  $3,500  +35%  The largest cost isn’t always the most important one.  A 35% increase in data transfer charges could point to higher traffic, cross-Region communication, an architecture change, or another workload-specific factor.  AWS Cost Explorer helps teams group and filter cost and usage data across dimensions such as services, accounts, Regions, tags, and cost categories.  But service-level data still leaves one question:  Which workload is creating the cost?  3. Workload: What is driving the cost?  Resource-level attribution helps connect AWS spending with applications and teams.  For example:  Application = Payments  Environment = Production  Team = Platform  Once relevant cost allocation tags are activated, tagged resource costs can be analyzed through AWS cost-management tools.  Instead of:  “EC2 costs us $18,000 a month.”  you can ask:  “How much of that $18,000 supports Payments?”  That’s a much more useful starting point for engineering and FinOps teams.  The limitation is straightforward: inconsistent tagging creates inconsistent visibility. If resources aren’t tagged properly, some spending will remain difficult to attribute.  4. Business Group: Where does the spending belong?  AWS Cost Categories can group costs into business-oriented structures using defined rules and supported dimensions.  For example:  Engineering → Payments → Production  Cost Categories don’t automatically determine resource ownership. Instead, they provide a consistent way to organize spending for reporting, budgeting, and accountability.  This becomes especially useful when multiple teams share AWS infrastructure.  5. Business Outcome: What are you getting for the spend?  The final layer connects cloud costs with business performance.  Useful measures include:  Suppose AWS spending rises 25%, but transaction volume rises 40%.  The higher bill may not indicate inefficiency.  Flexera’s 2026 State of the Cloud Report found that 49% of organizations now use unit economics to connect cloud costs with business outcomes, up from 40% in 2025.  The goal isn’t to make AWS spending as small as possible. It’s to understand the cost of delivering the outcome.  A Simple End-to-End Example  Consider a production Payments application:  AWS Account → Production  Service → Amazon EC2  Workload → Payments  Business Group → Engineering  Metric → Cost per transaction  Now the team can ask:  “Is the cost of running Payments increasing faster than the transaction volume it supports?”  That’s far more useful than simply asking whether the AWS bill increased.  It gives engineering and FinOps teams a common basis for deciding whether to optimize, investigate, or accept the additional spend.  When a Higher AWS Bill May Be Justified  Consider an illustrative scenario where your AWS bill increases by 28%.  You investigate and find:  Some of that increase may simply reflect growth.  The data transfer increase, however, deserves investigation. It could come from cross-Region traffic, architectural changes, or increased data movement.  Visibility doesn’t tell you whether a cost is good or bad. It gives you the information needed to make that judgment.  How to Improve AWS Cost Visibility  You don’t need perfect visibility on day one. Start with the areas where better attribution will have the biggest impact:  You don’t need to build a perfect cost model before taking action.  Start by making your largest and fastest-growing costs explainable.  Once you know where

AWS security rarely goes wrong because a company has no security tools.  More often, it starts with something simple: an IAM role has too much access, an old access key is still active, an S3 policy was never reviewed, or a security alert went unnoticed.  That becomes harder to manage as AWS environments grow. Companies are running workloads across multiple accounts and regions, automating deployments through CI/CD, and increasingly adding AI workloads to their cloud infrastructure.  AWS security mistakes are configuration, identity, access, monitoring, and operational errors that can expose workloads or increase the impact of a security incident.  Here are seven mistakes companies should pay attention to in 2026.  1. Giving IAM Roles and Users Too Much Access  “Just give it admin access” is an easy way to solve a permissions problem.  It can also create a much bigger security problem later.  AWS recommends least-privilege access, meaning users and workloads should receive only the permissions they actually need.  For example: a developer needs to upload files to one S3 bucket. Giving them broad permissions across the AWS account solves the immediate problem, but it also increases the potential damage if those credentials are compromised.  Use narrowly scoped IAM policies and roles, temporary credentials where possible, and MFA for human access.  The goal isn’t to make access difficult. It’s to make excessive access difficult to justify.  2. Keeping Long-Lived Access Keys Around  Access keys become risky when they quietly become permanent.  A key created for a quick test can end up in a Git repository, deployment script, developer machine, or CI/CD environment—and remain active long after the original project is finished.  A deployment pipeline, for example, can assume an IAM role with temporary credentials instead of relying on a permanent administrator key.  IAM Access Analyzer can also help identify unused roles, credentials, and permissions based on access activity, giving teams a starting point for regular access reviews.  For 2026, the question shouldn’t simply be:  “Where are our AWS keys?”  It should be:  “Why does this credential need to exist at all?”  3. Treating S3 Security as a One-Time Setup  S3 security can change without anyone intentionally changing the security strategy.  A private bucket may later be accessed by a new application, AWS account, vendor, or development team.  Regularly review:  AWS recommends controls such as S3 Block Public Access and appropriate monitoring to reduce unintended exposure.  Example: an internal bucket containing customer reports is later connected to a third-party analytics application. If the bucket policy is broader than necessary, that application could access data it doesn’t need.  The issue isn’t always the original configuration.  It’s what changed afterward.  4. Collecting Logs Without Actually Monitoring Them  Having CloudTrail enabled doesn’t automatically make an AWS environment secure.  CloudTrail provides visibility, but someone still needs to act on what that visibility reveals.  CloudTrail Event History provides the most recent 90 days of management events. For ongoing auditing and longer-term retention, organizations can configure CloudTrail trails or CloudTrail Lake based on their requirements.  A practical security workflow might look like:  CloudTrail → GuardDuty → Security Hub → Investigation → Response  The important part is what happens after an alert appears.  If suspicious API activity is detected at 2 a.m., does someone know who should investigate it? Can access be revoked quickly?  Security monitoring is only useful when it leads to action.  5. Letting Unused Permissions Pile Up  AWS environments are rarely static.  People change teams. Projects end. Applications are replaced. Temporary production access becomes permanent.  That’s how security debt builds.  Imagine an engineer gets temporary production access during an incident. The incident is resolved, but nobody revisits the permissions. Six months later, that access is still there.  IAM Access Analyzer can help identify unused access based on activity, giving teams a starting point for regular permission reviews. For certain Security Hub unused-access findings, AWS uses a 90-day activity lookback.  The objective isn’t to remove permissions blindly.  It’s to make sure important permissions still have a clear reason behind them.  6. Securing Production but Ignoring the CI/CD Pipeline  Modern AWS infrastructure is increasingly created and changed through automation.  That makes the CI/CD pipeline an important security boundary.  Think about the chain:  Developer → Git repository → CI/CD pipeline → AWS  If the pipeline has excessive AWS permissions, compromising it could provide a direct path toward production.  For example: a deployment role only needs to update Lambda functions but can also modify IAM policies. That extra permission could create an opportunity for privilege escalation.  Teams should:  In 2026, cloud security doesn’t stop at the production account.  7. Managing Multiple AWS Accounts Without Strong Governance  Multiple AWS accounts can improve isolation and control—but only if security standards are consistent.  One account might have centralized logging enabled while another doesn’t. One team may follow strict IAM policies while another relies on broad permissions.  For example: a new production account is created but isn’t enrolled in the organization’s logging, monitoring, or baseline security controls.  Nothing may look wrong immediately. The problem is that the account has quietly become an exception.  AWS Organizations and AWS Control Tower can help companies establish and apply common controls across accounts.  As AWS environments scale, security governance needs to scale with them.  A Practical AWS Security Checklist for 2026  Start with the areas that can create the biggest blast radius:  You don’t need to fix everything at once. Start with the controls that protect your most sensitive data and critical workloads.  Build Security Into How You Run AWS  The biggest AWS security mistakes aren’t always sophisticated attacks.  They’re often small decisions that stay unchanged as an AWS environment grows: an IAM role with too much access, an unused credential, an overlooked S3 policy, or a new AWS account outside established security controls.  The solution isn’t simply adding more security tools.  It’s building security into how your AWS environment is designed, deployed, monitored, and maintained.  The strongest AWS security strategy isn’t the one with the most tools. It’s the one where secure decisions become the

AWS support plans range from free to tens of thousands of dollars a month, and the gap between them is significant. Choosing the wrong tier either leaves you exposed during an actual incident, or means paying for response guarantees your business does not need.  Basic Support (Free)  Included automatically with every AWS account.  Developer Support  A low-cost entry point for businesses with light production usage or ongoing development work.  Business Support  The tier most production workloads should be running on.  Enterprise Support  Built for businesses where downtime carries significant financial or reputational cost.  How to Actually Decide  What Gets Overlooked  The right support tier is less about company size and more about what an hour of unresolved downtime actually costs your business. That number should drive the decision, not which tier feels proportionate to your company’s size on paper.