Author name: Shivam Pokhriyal

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.

When something changes unexpectedly in an AWS account, the first question is always the same: who did this, and when? Without CloudTrail and Config enabled, that question often has no answer. With them, it takes minutes to find.  AWS CloudTrail — The Record of Every Action  CloudTrail logs every API call made in your AWS account, whether it came from the console, CLI, SDK, or another AWS service.  AWS Config — The State of Your Resources Over Time  Where CloudTrail tells you what action was taken, Config tells you what your resources actually looked like before and after — and whether they match your defined rules.  Setting Both Up Properly  What This Solves in Practice  Common Gaps  CloudTrail and Config are two of the least visible AWS services in daily operations, and two of the most important the moment something goes wrong or an audit comes due. Both are inexpensive to run and expensive to have missed enabling.

Running Kubernetes on AWS isn’t difficult. Running it efficiently in production is.  You can create an Amazon EKS cluster quickly. The harder questions come afterward: How should your VPC be designed? Should you use EC2, Fargate, or EKS Auto Mode? How should workloads scale? How do you secure AWS access? And how do you prevent Kubernetes from quietly increasing your AWS bill?  Kubernetes is now firmly established in production. CNCF reported that 82% of container users were running Kubernetes in production, while Kubernetes is also becoming increasingly important for AI inference workloads.  For organizations adopting Kubernetes on AWS, Amazon EKS combines managed Kubernetes with AWS compute, networking, security, storage, and other cloud services.  But creating a cluster is only the beginning.  What Is Kubernetes on AWS?  Kubernetes on AWS means running Kubernetes workloads on AWS infrastructure, most commonly through Amazon Elastic Kubernetes Service (Amazon EKS).  A simplified Amazon EKS architecture looks like:  Users → Load Balancer → EKS → Pods → AWS Services  EKS is a strong fit for:  But Kubernetes isn’t automatically the right answer. For a simple application, a managed service with less operational overhead may be a better choice.  The goal isn’t to use Kubernetes because it’s popular. It’s to use it when its flexibility and scalability justify the complexity.  7 Kubernetes on AWS Best Practices 1. Design Your AWS Network Before Deploying EKS  Your EKS architecture starts with your AWS VPC, not Kubernetes.  A common production pattern is:  Internet → Application Load Balancer → Private Subnets → EKS → AWS Services  Plan for:  The AWS Load Balancer Controller can connect Kubernetes services and ingress resources with AWS load-balancing infrastructure.  Networking also affects your AWS bill. NAT Gateway usage and cross-AZ data transfer can become meaningful costs as traffic grows.  Best practice: design traffic flow, security boundaries, and expected network costs before deploying production workloads.  2. Design for Multi-AZ Resilience  Running EKS across multiple Availability Zones doesn’t automatically make an application highly available.  Your workloads need to be distributed correctly too.  Use:  For example, six replicas running in one Availability Zone can still fail together during an AZ outage.  So don’t just ask:  “Is my EKS cluster highly available?”  Ask:  “Can my application continue serving customers when infrastructure fails?”  That is the difference between deploying Kubernetes and designing it for production.  3. Choose the Right EKS Compute Model  EKS gives you several ways to run workloads.  Amazon EC2  Best when you need greater infrastructure control, specialized compute, GPUs, or deeper cost optimization.  AWS Fargate  Useful when reducing server management is more important than node-level control and your workloads fit Fargate’s supported model.  EKS Auto Mode  Useful when you want Kubernetes while reducing the amount of infrastructure management your team performs. It automates aspects of compute provisioning and cluster infrastructure.  Don’t choose based on price alone.  Start with:  Workload requirements → Required control → Operational overhead → Cost  That usually leads to a better decision than simply choosing the cheapest compute option.  4. Secure AWS Access With Workload Identity  AWS secures the underlying infrastructure, but your team remains responsible for workload and Kubernetes security.  Use AWS IAM and Kubernetes RBAC together and apply least privilege.  For workloads that need AWS permissions, use EKS Pod Identity or IAM Roles for Service Accounts (IRSA) instead of distributing long-lived credentials.  A strong baseline includes:  For example, a payment service that only needs access to one secret shouldn’t receive broad permissions across the AWS account.  Give every workload only the permissions it actually needs.  5. Use Autoscaling at Multiple Levels  Kubernetes scaling isn’t simply about adding pods.  Think about three layers:  Pod scaling  Use the Horizontal Pod Autoscaler (HPA) to adjust application replicas based on appropriate metrics.  Compute scaling  Use node autoscaling solutions such as Karpenter to provision suitable compute capacity as workloads change.  Application scaling  Look beyond Kubernetes. Databases, queues, caches, and external APIs can become the real bottlenecks.  For example:  Traffic doubles → HPA adds pods → database reaches capacity → application still slows down.  Adding more pods won’t fix that problem.  Scale the bottleneck, not just the container count.  6. Build Observability Before Production  Kubernetes creates operational data across infrastructure, clusters, and applications. Without proper observability, diagnosing an outage becomes a guessing game.  Use three layers:  Metrics → Logs → Traces  Monitor:  Layer  Examples  Infrastructure  CPU, memory, network  Kubernetes  Pod restarts, scheduling failures  Application  Latency, errors, throughput  Business  Transactions, conversions  AWS services such as CloudWatch can be combined with technologies such as Prometheus, Grafana, and OpenTelemetry.  Each solves a different part of the observability problem: infrastructure monitoring, metrics, visualization, and telemetry/instrumentation.  The objective isn’t to collect more data.  It’s to answer:  What failed? When? Which workload? Who is affected?  7. Automate Deployments and Control Costs  A repeatable AWS Kubernetes deployment workflow can look like:  Git → CI/CD → Container Registry → GitOps → EKS  Use Infrastructure as Code for AWS infrastructure and GitOps for Kubernetes configuration. Tools such as Terraform, CloudFormation, and Argo CD can reduce manual changes and configuration drift.  Keeping deployment configuration in Git also creates a versioned, reviewable source of truth for production.  CNCF has reported strong adoption of GitOps practices and Argo CD within the Kubernetes ecosystem.  For cost optimization, use:  Measure → Right-size → Automate → Review  Evaluate:  For example, if an application requests 4 vCPUs but consistently uses less than 1, its resource configuration deserves review.  Kubernetes cost optimization isn’t about choosing the cheapest infrastructure. It’s about paying for capacity your workloads actually need.  A Practical Kubernetes on AWS Architecture  Consider a SaaS platform using:  Users → ALB → EKS → RDS  with:  Multi-AZ + HPA + Karpenter + Pod Identity + Secrets Manager + CloudWatch  The ALB handles incoming traffic, EKS orchestrates containers, HPA responds to application demand, Karpenter provides compute capacity, Pod Identity controls AWS access, Secrets Manager protects credentials, and CloudWatch provides operational visibility.  In a real environment, additional services such as Route 53, ElastiCache, S3, queues, or CDNs may also be part of the architecture.  The important principle

Migrating a database is not like migrating a web server — the data has to arrive intact, in the right order, with minimal or no downtime for whatever depends on it. AWS Database Migration Service (DMS) exists specifically to handle this without requiring the database to go offline for hours.  What DMS Actually Does  DMS replicates data from a source database to a target database, and can keep them in sync continuously until you are ready to cut over.  The Migration Process  Minimizing Downtime  Common Pitfalls  Why This Beats a Manual Migration  A manual database migration usually means a maintenance window, a backup, a restore, and hoping nothing changed in between. DMS keeps the source live and continuously replicates changes, which turns a high-risk, all-at-once event into a controlled, verifiable process — and that difference is exactly what makes it worth using for any production database migration.