Author name: Shivam Pokhriyal

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. 

A load balancer distributes incoming traffic across multiple servers so no single instance gets overwhelmed, and so a failed instance does not take the whole application down. AWS offers three types, and the right choice depends on what layer of traffic you actually need to manage.  Application Load Balancer (ALB)  Operates at Layer 7 (HTTP/HTTPS), meaning it understands the content of requests, not just the connection.  Network Load Balancer (NLB)  Operates at Layer 4 (TCP/UDP), handling raw network connections without inspecting request content.  Classic Load Balancer (CLB)  The original AWS load balancer, largely superseded by ALB and NLB.  Choosing the Right One  Common Mistakes  For nearly all standard web applications, ALB is the correct default. NLB is a deliberate choice for a specific technical requirement, not a general-purpose upgrade. 

On-Demand pricing is the most flexible way to run AWS compute, and also the most expensive. Once a workload’s usage pattern becomes predictable, there are three ways to reduce that cost significantly — and choosing the right one depends entirely on how consistent your usage actually is.  Reserved Instances  You commit to a specific instance type, in a specific region, for a 1 or 3-year term, in exchange for a discount of up to 72% off On-Demand pricing.  Savings Plans  A more flexible commitment — you commit to a dollar amount of compute usage per hour, rather than a specific instance type, for a 1 or 3-year term.  Spot Instances  Spare AWS compute capacity offered at discounts of up to 90% off On-Demand pricing, with the tradeoff that AWS can reclaim the instance with a two-minute warning.  Choosing Between Them  A Realistic Approach  Most cost-optimized AWS accounts use a mix of all three: Savings Plans covering the predictable baseline load, Spot Instances handling batch and flexible workloads, and On-Demand covering short-term spikes above the baseline. Reviewing AWS Cost Explorer’s recommendations regularly — rather than committing once and forgetting about it — is what keeps this mix aligned with how the workload actually evolves over time.

WordPress on shared hosting works fine for a small site, until traffic grows, plugins pile up, and page load times start affecting conversions. Moving to AWS gives you room to scale — but the migration needs to be handled carefully to avoid losing content or breaking the site in the process.  Why Businesses Make This Move  The Basic Architecture  Separating the database onto RDS from day one makes future scaling far easier than keeping it on the same server as the application.  Migration Steps  Reducing Downtime During Cutover  Common Mistakes  After the Move  Once WordPress is running on AWS, set up CloudWatch monitoring, automated RDS backups, and a CDN through CloudFront if it is not already in place. The real value of the migration is not just moving the same site to different infrastructure — it is unlocking the ability to scale each part of the stack independently as the site grows.

Most applications start life on a single server. It works fine until deployments get risky, scaling means manually spinning up more servers, and “it works on my machine” becomes a recurring problem. Containers solve this, and on AWS, ECS is the most direct path to running them in production.  What Docker Actually Solves  A Docker container packages your application with everything it needs to run — code, runtime, libraries, configuration — into a single unit. That unit behaves the same on a developer’s laptop, in staging, and in production.  Where ECS Fits In  Amazon ECS (Elastic Container Service) is AWS’s platform for running containers at scale. It handles scheduling, scaling, and health checks so you are not manually managing where each container runs.  For most growing businesses, ECS on Fargate removes an entire layer of operational work — no instance patching, no capacity planning for the cluster itself.  Setting Up a Basic ECS Deployment  Common Mistakes to Avoid  Is It Worth the Migration?  If your team is deploying manually, scaling by adding more identical servers, or fighting environment inconsistencies, moving to Docker and ECS pays for itself quickly in reduced deployment risk and faster releases. For simple, low-traffic applications, it can be more infrastructure than necessary — the right call depends on how much your deployment process is currently costing you in time and incidents.