Cloud Migration
Use a phased migration strategy, not a “big bang” approach.
A large retailer decided to migrate its entire e-commerce platform to the cloud over a single weekend. They called it the “big bang.” The migration failed spectacularly. The website was down for two days, costing them millions in lost sales. A rival company took a phased approach. They started by migrating a single, low-risk application, like their internal blog. They learned from the process, refined their strategy, and then moved progressively larger and more critical applications over several months. Their migration was smooth, predictable, and had zero downtime.
Stop doing a “lift and shift” of your legacy applications. Do refactor for the cloud instead.
A company took their old, monolithic application running on a single massive server and simply moved the virtual machine to the cloud. This “lift and shift” gave them a shocking cloud bill and none of the promised benefits; it was just someone else’s expensive server. A smarter company refactored their application for the cloud. They broke it down into smaller services, used serverless functions for specific tasks, and leveraged managed databases. This cloud-native approach made their application more resilient, scalable, and significantly cheaper to run.
The #1 secret for a successful cloud migration that cloud providers won’t tell you.
The secret isn’t a tool or a service; it’s a relentless focus on training your people. Cloud providers will sell you on the power of their technology, but a migration at a financial services firm succeeded because they invested heavily in their team first. Before moving a single server, they ran months of intensive training, ensuring their operations and development teams understood cloud architecture, security, and cost management. The technology was the easy part; the success came from having a skilled team that knew how to use it properly.
The biggest lie you’ve been told about the cost savings of moving to the cloud.
The lie is that moving to the cloud will automatically save you money. A company migrated their entire data center to the cloud, expecting their IT costs to drop. A year later, their bill was 30% higher than before. They had treated the cloud like their old data center, leaving servers running 24/7 and over-provisioning resources. True cloud cost savings don’t come from just moving; they come from adopting a new operational model—turning off resources when not in use, leveraging autoscaling, and continuously optimizing your architecture.
I wish I knew this about application dependency mapping before starting a cloud migration.
We thought we were ready to migrate our inventory management system. We moved the main application server, and then all hell broke loose. We didn’t realize it had hidden dependencies on a dozen other internal services—an old authentication server, a legacy reporting database, a file share. The application was broken for a week while we frantically tried to identify and move all the interconnected pieces. I wish I had known that creating a detailed dependency map before you move anything is the most critical, non-negotiable first step.
I’m just going to say it: Not every application belongs in the cloud.
A manufacturing company had a specialized, performance-critical application that controlled the machinery on their factory floor. The latency requirements were measured in microseconds. Their new CIO, a “cloud-first” evangelist, insisted they migrate it to the cloud. The project was a disaster. The network latency between the cloud and the factory floor made the application unusable. Some systems, especially those requiring ultra-low latency or interfacing with specialized on-premise hardware, are simply better suited to remain in the data center.
99% of companies make this one mistake during their cloud migration.
The most common mistake is underestimating the cultural shift required. A company successfully migrated its technology to the cloud but failed to change its processes. The development teams still operated in silos, and the finance department was shocked by the new variable spending model. The migration’s true value wasn’t realized. A cloud migration isn’t just a technology project; it’s a business transformation. It requires new ways of thinking about budgeting (FinOps), security, and collaboration. Ignoring the cultural change is setting yourself up for failure.
This one small action of conducting a thorough cloud readiness assessment will change the outcome of your migration forever.
A company was excited to move to the cloud. They jumped right in, trying to migrate their most complex application first. They failed within a month. They took a step back and performed a thorough cloud readiness assessment. They analyzed their applications, their team’s skills, and their operational processes. The assessment revealed that they should start with a different, simpler application and that their team needed significant training. This one action of pausing to assess the situation turned their second migration attempt into a resounding success.
The reason your cloud migration is failing is due to a lack of a clear business case.
A company’s IT department started a cloud migration because it was the trendy thing to do. They had no clear goals. When asked why they were migrating, the answer was a vague “to be more agile.” The project meandered without support and eventually fizzled out. A successful migration starts with a clear business case. Are you migrating to reduce costs, improve scalability to handle seasonal peaks, or accelerate product development? A clear “why” provides the focus and executive support needed to navigate the complexities of a migration.
If you’re still managing your own data centers, you’re losing agility.
Two retail companies were heading into the holiday shopping season. Company A, running its own data center, had to spend months procuring and provisioning extra servers to handle the anticipated traffic spike. Company B, running in the cloud, simply configured their systems to automatically scale up to meet the demand and then scale back down in January. Company B could react to market conditions in minutes, not months, giving them a massive competitive advantage. Managing your own hardware is a slow, capital-intensive process that kills business agility.
Serverless Computing
Use serverless for event-driven applications, not for long-running processes.
A developer was tasked with processing uploaded videos. He tried to do it in a single serverless function. The function would frequently time out on large videos, as serverless platforms have maximum execution limits. He re-architected the solution. Now, an initial serverless function would trigger when a video was uploaded, break it into smaller chunks, and then invoke a separate function for each chunk to process in parallel. This event-driven, distributed approach was a perfect fit for serverless and handled videos of any size with ease.
Stop doing traditional server provisioning. Do embrace a functions-as-a-service (FaaS) model instead.
A team was responsible for an API that received infrequent, spiky traffic. They maintained a cluster of virtual servers running 24/7 to handle the load, most of which sat idle 95% of the time, costing the company money. They switched to a Functions-as-a-Service (FaaS) platform like AWS Lambda. Now, the code only runs when an API request comes in, and they pay only for the compute time they actually consume. They completely eliminated the cost of idle servers and the operational burden of managing them.
The #1 hack for debugging serverless functions that will save you hours.
The secret isn’t just looking at logs; it’s using a tool for live, local debugging. A developer was struggling to debug a complex serverless function by repeatedly deploying it to the cloud and checking the logs. The feedback loop was painfully slow. He discovered a framework that allowed him to invoke his cloud function on his local machine, simulating the cloud environment. He could now use his IDE’s debugger, set breakpoints, and step through his code line-by-line, turning a process that took hours into one that took minutes.
The biggest lie you’ve been told about serverless being “cheaper”.
The lie is that serverless is always cheaper. For applications with spiky, unpredictable traffic, it’s often a huge cost-saver. But a company migrated an application with steady, high-volume, 24/7 traffic to a serverless model. Their bill skyrocketed. At a certain scale of constant, predictable workload, paying per-invocation and per-millisecond can be more expensive than running a continuously-utilized, provisioned server. “Cheaper” depends entirely on the workload’s traffic pattern.
I wish I knew this about the cold start problem when I built my first serverless application.
I built my first serverless API and was so proud. It worked perfectly. But then I noticed the first request after a few minutes of inactivity was incredibly slow—sometimes taking several seconds. This was the “cold start”: the cloud provider had to create a new container for my function to run in. I wish I had known that for latency-sensitive applications, you need to plan for cold starts, either by using provisioned concurrency to keep instances warm or by designing your application to tolerate that initial delay.
I’m just going to say it: Serverless architecture can lead to complex and hard-to-manage systems.
A team enthusiastically adopted a serverless-first approach. Their application, which could have been a simple monolith, was broken down into 50 different serverless functions, each triggering another. They called it a “serverless spaghetti” architecture. While each function was simple, understanding the overall flow of the system and debugging a transaction that spanned multiple functions became a distributed nightmare. Without careful design and excellent monitoring tools, serverless can trade server management complexity for architectural complexity.
99% of developers make this one mistake when designing their serverless applications.
The most common mistake is writing “fat” functions that do too many things. A developer wrote a single serverless function that would receive a user’s data, validate it, process it, write it to three different database tables, and then send an email. This function was slow, hard to test, and had too many dependencies. A better, serverless-native design follows the single responsibility principle. Each function should do one thing well. This makes them faster, more scalable, and easier to maintain.
This one small action of setting up distributed tracing will change the way you monitor your serverless architecture forever.
A user’s request in a serverless application was failing, but it was impossible to see why. The request would trigger one function, which called another, which wrote to a queue, which triggered a third. The logs were scattered across multiple services. The team then implemented distributed tracing. Now, each request was assigned a unique trace ID that was passed between all the functions. They could visualize the entire lifecycle of a request as a single, unified trace, pinpointing the exact function and line of code where the failure occurred.
The reason your serverless application is slow is due to inefficient function design.
A developer’s serverless function was performing poorly. The reason? Inside the function’s code, he was initializing a database connection and loading a large configuration file with every single invocation. This added significant overhead to each run. He refactored his code to initialize the database connection and load the configuration outside the main function handler. Now, these expensive objects were created only once during a cold start and were reused for subsequent warm invocations, dramatically improving the function’s performance.
If you’re still managing servers for your APIs, you’re losing scalability.
A company’s API would crash every time a marketing campaign went viral and sent a huge spike of traffic. The team would scramble to manually add more servers to handle the load. A competitor built their API using a serverless platform. When their campaigns went viral, the platform automatically scaled to handle hundreds of thousands of concurrent requests without any manual intervention. They never had to worry about provisioning or scaling their servers again, allowing them to focus on their business, not their infrastructure.
Containers & Orchestration
Use Kubernetes for container orchestration at scale, not just Docker Compose.
A small team started their project using Docker Compose to run their multi-container application on a single server. It was simple and worked well. But as their application grew and they needed to run it across a cluster of multiple servers for high availability, Docker Compose was no longer sufficient. They migrated to Kubernetes. While more complex, Kubernetes provided the robust, production-grade orchestration they needed, automatically handling service discovery, load balancing, and self-healing across their entire cluster.
Stop doing manual container deployments. Do use a CI/CD pipeline for your containers instead.
A developer would build a new Docker image on his laptop, push it to a registry, and then manually SSH into the production server to pull and run the new container. The process was error-prone and slow. A CI/CD pipeline changed everything. Now, when he merges his code, a pipeline automatically builds the Docker image, runs tests against it, pushes it to the registry, and then triggers a rolling update in the Kubernetes cluster. The entire process is automated, reliable, and takes just a few minutes.
The #1 secret for optimizing your Docker image size that will speed up your deployments.
The secret is using multi-stage builds. A developer’s Dockerfile first installed a bunch of build-time dependencies and compilers to build their application, resulting in a massive 2GB image. He then switched to a multi-stage build. In the first stage, he used a large “builder” image to compile his application. In the second, final stage, he used a minimal, lightweight base image and copied only the compiled application binary from the first stage. His final image size dropped from 2GB to 20MB, making deployments incredibly fast.
The biggest lie you’ve been told about Kubernetes being easy to manage.
The lie, often perpetuated by hype, is that Kubernetes is a simple, plug-and-play solution. A small company with a simple web application decided to adopt Kubernetes because everyone else was. They quickly found themselves drowning in complexity. They spent more time learning about YAML manifests, networking plugins, and ingress controllers than they did writing their actual application. Kubernetes is an incredibly powerful system, but it’s also incredibly complex. It’s a tool designed to solve problems of scale that most small applications simply don’t have.
I wish I knew this about the complexity of networking in Kubernetes when I started.
When I first started with Kubernetes, I thought networking would just work. I deployed my application and couldn’t figure out why my front-end container couldn’t talk to my back-end container. I spent a week learning about pods, services, cluster IPs, network policies, and ingress controllers. I wish I had known that networking is one of the most complex and critical aspects of Kubernetes. Understanding how pods communicate with each other and how traffic gets into the cluster from the outside world is a fundamental skill that you need to learn upfront.
I’m just going to say it: For many applications, a simpler container orchestrator is a better choice than Kubernetes.
A team with a straightforward, stateless web application spent months setting up and managing a complex Kubernetes cluster. They were constantly battling with its complexity. A similar team with a similar application chose a simpler solution like AWS Fargate or Docker Swarm. They were up and running in a day and spent almost no time on infrastructure management. While Kubernetes is the undisputed king of large-scale container orchestration, for many common use cases, its complexity is overkill, and a simpler tool is a much more productive choice.
99% of teams make this one mistake when adopting Kubernetes.
The most common mistake is giving their developers direct access to the production Kubernetes cluster with broad permissions. A developer, trying to debug an issue, accidentally deleted the wrong deployment, taking down the entire application. A better approach is to use a GitOps workflow. Developers don’t interact with the cluster directly. Instead, they make changes to the application’s configuration in a Git repository. An automated tool then syncs those changes to the cluster. This provides a clear audit trail and prevents costly human errors.
This one small action of implementing readiness and liveness probes will change the reliability of your containerized applications forever.
An application running in a container would sometimes get into a bad state where it was running but no longer serving traffic. Kubernetes, thinking the container was healthy, would keep sending traffic to it, resulting in errors for users. The team implemented two probes. A “liveness probe” would check if the application was still running correctly; if not, Kubernetes would restart the container. A “readiness probe” would check if the application was ready to accept traffic; if not, Kubernetes would temporarily stop sending it traffic. These two small configurations made their application self-healing.
The reason your Kubernetes cluster is so expensive is due to a lack of resource requests and limits.
A company’s Kubernetes bill was surprisingly high. The reason? Their developers weren’t specifying resource requests and limits for their containers. Kubernetes didn’t know how much CPU or memory each container needed, so it couldn’t pack them efficiently onto the cluster’s nodes. This resulted in wasted resources and underutilized servers. By setting appropriate requests (what the container needs to start) and limits (the maximum it can use), they allowed Kubernetes to schedule their workloads much more efficiently, significantly reducing the size and cost of their cluster.
If you’re still deploying applications on bare metal servers, you’re losing portability.
A company deployed their application directly onto a fleet of servers running a specific version of Ubuntu. When they wanted to move to a different cloud provider that used a different operating system, they had to spend months re-configuring their application. A company that deployed their application in containers could run it anywhere—on-premise, on AWS, on Google Cloud—without changing a single line of code. Containers provide a consistent, portable environment that decouples your application from the underlying infrastructure.
Cloud Storage
Use the right cloud storage class for your data, not just the default standard storage.
A company was storing all of its data, including old, rarely-accessed archives, in the default “standard” storage class. They were paying top dollar for high-performance access to data they hadn’t touched in years. They analyzed their data access patterns and moved their archives to a cheaper, “archive” storage class like Amazon S3 Glacier. This simple change, choosing the right storage class for the right data, cut their monthly storage bill by 60% with no impact on their day-to-day operations.
Stop doing manual data tiering. Do use lifecycle policies to automate it instead.
An administrator’s monthly task was to run a script that would move all files older than 90 days from a standard storage bucket to a cheaper archive tier. It was a tedious, manual process. She then discovered lifecycle policies. She created a simple rule in the cloud console: “After 90 days, transition objects to the Infrequent Access tier. After 365 days, transition them to the Archive tier.” The cloud provider now handled this data tiering automatically, saving her time and ensuring the policy was always enforced.
The #1 hack for reducing your cloud storage costs that is often overlooked.
The most overlooked hack is deleting incomplete multipart uploads. When you upload a large file to object storage, it’s broken into parts. If the upload is interrupted, these orphaned parts can remain in your bucket, invisible to you but still racking up storage charges. A company was puzzled by their high storage bill. They ran a tool to find and delete incomplete multipart uploads and discovered they were paying for terabytes of orphaned data from failed uploads over the years. This simple cleanup saved them thousands of dollars.
The biggest lie you’ve been told about the durability of cloud storage.
The lie is that because cloud storage is highly durable, you don’t need backups. Cloud providers like AWS S3 offer “eleven nines” of durability, meaning the chance of them losing your file is astronomically low. But that protects you from the provider losing your data; it doesn’t protect you from yourself. A developer with administrator access accidentally ran a script that deleted an entire production storage bucket. The data was gone forever. Durability is not a substitute for backups and a solid disaster recovery plan.
I wish I knew this about egress costs before I stored massive amounts of data in the cloud.
A research institute was thrilled with the low cost of storing their massive datasets in the cloud. The storage bill was tiny. But then, they needed to download a 100TB dataset for analysis by a partner institution. They were hit with a shocking, five-figure bill for “data egress.” They had only focused on the cost of storing data (“ingress”), not the often much higher cost of getting it back out. I wish I had known that you must always factor in the cost of data transfer when architecting a data-heavy application.
I’m just going to say it: Object storage is not a file system.
A developer tried to treat an object storage service like AWS S3 as a traditional file system. He tried to mount it as a drive and run an application that required file locking and partial file updates. The performance was terrible, and the application was constantly failing. Object storage is designed for storing and retrieving entire objects (files) via a web API. It doesn’t have the same semantics as a local file system. Understanding this fundamental difference is key to using object storage effectively.
99% of cloud users make this one mistake when configuring their storage buckets.
The most common and dangerous mistake is accidentally making their object storage buckets public. A developer, trying to quickly share a single file, configured the entire storage bucket to be publicly readable. He didn’t realize this exposed every other file in that bucket, including sensitive customer data and application secrets. This simple misconfiguration is one of the leading causes of major data breaches. Buckets should be private by default, and public access should be granted with extreme caution.
This one small action of enabling versioning on your object storage will change the way you protect against accidental deletions forever.
A developer was cleaning up old files and accidentally deleted the wrong folder in a critical storage bucket. The data was gone. On her next project, she enabled versioning on the bucket. A few months later, a similar accident happened. But this time, because versioning was enabled, the “delete” action simply created a delete marker. She was able to easily restore the previous version of the files, turning a potential disaster into a minor inconvenience. Versioning is a simple checkbox that acts as a powerful undelete button.
The reason your application’s access to cloud storage is slow is due to a lack of a content delivery network (CDN).
A company hosted their website’s images and videos in a cloud storage bucket located in a US data center. For their users in Asia and Europe, the website was incredibly slow because the data had to travel across the globe. They put a Content Delivery Network (CDN) in front of their storage bucket. The CDN automatically cached copies of their files in locations all over the world. Now, when a user in Europe requested an image, it was served from a nearby edge location, dramatically reducing latency.
If you’re still using on-premise file servers for all your data, you’re losing accessibility.
An employee needed to access an important sales presentation while traveling, but the file was stored on the company’s on-premise file server, which was only accessible from the corporate network. She couldn’t get to it. A competing company stored their files in a modern cloud storage solution. Their employees could securely access any file they needed, from any device, anywhere in the world. This accessibility and collaboration capability is a major advantage of cloud storage over traditional, siloed file servers.
Cloud Networking
Use a virtual private cloud (VPC) to isolate your cloud resources, not the public internet.
A developer, in a hurry, launched a new database server with a public IP address, open to the entire internet. Within hours, it was being scanned and attacked by bots. A better approach is to launch all your resources within a Virtual Private Cloud (VPC). A VPC is your own private, isolated section of the cloud. By launching the database in a private subnet within the VPC, it is completely inaccessible from the public internet, dramatically improving its security posture.
Stop doing manual IP address management. Do use a cloud-native IPAM solution instead.
A network administrator for a large company used a spreadsheet to keep track of all the IP address ranges used by their different applications in the cloud. It was a manual, error-prone process that often led to IP address conflicts and outages. They switched to a cloud-native IP Address Management (IPAM) solution. The tool automatically managed their IP address space, preventing overlaps and simplifying the process of creating new virtual networks, saving them from the nightmare of spreadsheet-based networking.
The #1 secret for designing a highly available and fault-tolerant cloud network.
The secret is to design your network across multiple Availability Zones (AZs). An Availability Zone is a distinct data center within a cloud region. A company deployed their entire application, including redundant servers, into a single AZ. When that specific data center had a power failure, their entire application went down. A wiser company deployed their application across three different AZs. When one AZ failed, the load balancers automatically redirected traffic to the healthy servers in the other two AZs, and their application stayed online without interruption.
The biggest lie you’ve been told about the simplicity of cloud networking.
The lie, often implied by the simple UI of cloud consoles, is that cloud networking is easy. A developer could easily create a virtual network and connect a few servers. But as soon as they needed to connect their cloud network back to their on-premise data center, or peer with another virtual network, or set up complex routing and security rules, they found themselves in a world of complexity. Production-grade cloud networking requires a deep understanding of concepts like subnets, routing tables, gateways, and VPNs.
I wish I knew this about the difference between security groups and network ACLs when I started in the cloud.
When I first started, I thought security groups and network ACLs (Access Control Lists) did the same thing. I got so confused. I wish I had known this simple analogy: a security group is like a firewall for your specific server (it’s stateful, meaning return traffic is automatically allowed). A network ACL is like a security checkpoint for the entire neighborhood or subnet (it’s stateless, meaning you have to explicitly allow both inbound and outbound traffic). Understanding this distinction is fundamental to securing your cloud network.
I’m just going to say it: Hybrid cloud networking is incredibly complex to get right.
A company wanted to create a seamless “hybrid cloud,” extending their on-premise data center network into the public cloud. They thought it would be as simple as setting up a VPN. They quickly discovered a world of pain: routing conflicts, DNS resolution issues, and latency problems. Securely and reliably connecting two fundamentally different networking environments requires specialized skills and careful planning. For many, the complexity of a true hybrid network outweighs its benefits.
99% of cloud engineers make this one mistake when setting up their VPC peering.
The most common mistake is creating VPC peering connections with overlapping IP address ranges. A team tried to peer their development VPC (using the 10.0.0.0/16 range) with their production VPC (also using the 10.0.0.0/16 range). The peering connection failed because the cloud provider had no way to route the traffic; it couldn’t distinguish between the two identical networks. A core principle of cloud network design is to plan your IP address space carefully upfront to ensure every virtual network has a unique range.
This one small action of using a global load balancer will change the way you distribute traffic to your applications forever.
A company had a global user base but ran their application out of a single cloud region in the US. For users in Asia, the application was slow. They re-architected their application to run in multiple regions around the world. By putting a global load balancer in front of it, they could automatically direct users to the closest, lowest-latency region. This single change dramatically improved their application’s performance for their international users and provided a robust disaster recovery solution.
The reason your cloud application is experiencing high latency is due to poor region selection.
A startup based in London launched their new application on servers in a US cloud region because it was slightly cheaper. For their primary user base in the UK and Europe, every single request had to cross the Atlantic Ocean and back again. The application felt sluggish. By simply relaunching their application in the London cloud region, they eliminated the transatlantic latency and cut their application’s response time in half. Proximity to your users is one of the most important factors in network performance.
If you’re still backhauling all your cloud traffic through your on-premise data center, you’re losing performance.
A company established a direct connection from their office to the cloud. For security reasons, they configured all their cloud traffic, including traffic destined for the internet, to be routed back through their on-premise firewall. This “backhauling” created a massive bottleneck. A user on a cloud virtual machine trying to download a file from the internet had their traffic go from the cloud, to the data center, and then out to the internet. This hairpin turn added significant latency and limited performance.
Cloud Databases
Use a managed database service, not self-hosting your own database on a virtual machine.
A team decided to install and manage their own PostgreSQL database on a cloud virtual machine to save money. They spent hours each week on patching, backups, and security hardening. A different team chose to use a managed database service like Amazon RDS. The cloud provider handled all the administrative overhead automatically. The managed service was slightly more expensive, but it freed up the developers to focus on building their application, not on being database administrators, providing a much higher return on investment.
Stop doing single-region database deployments for critical applications. Do use a multi-region setup instead.
A company’s flagship application and its database ran in a single cloud region. When that entire region experienced an outage, their application was completely down for six hours, violating their customer SLAs and damaging their reputation. For their next critical application, they used a multi-region database. The database automatically replicated data between two different geographic regions. If one region failed, they could fail over to the other one in minutes, ensuring high availability and business continuity.
The #1 tip for choosing the right managed database service for your needs.
The most important tip is to deeply understand your application’s data consistency and query patterns. If you are building an e-commerce platform that requires strong transactional consistency, a managed relational database like PostgreSQL or MySQL is the right choice. If you are building a social media feed that needs to handle a massive scale of reads and writes with flexible data, a managed NoSQL database like DynamoDB or Firestore is likely a better fit. Don’t choose based on hype; choose based on your workload’s specific requirements.
The biggest lie you’ve been told about the performance of cloud databases.
The lie is that cloud databases are inherently slower than their on-premise counterparts. A company migrated their database to a managed cloud service and complained that the performance was worse. The reason? They had chosen the smallest, cheapest instance type available. A properly configured cloud database, with the right instance size, storage type (e.g., Provisioned IOPS), and network configuration, can match or even exceed the performance of most on-premise database servers, with the added benefits of scalability and managed operations.
I wish I knew this about read replicas and how they can scale my application’s read traffic.
My application’s database was getting overwhelmed. Both read and write queries were going to the same primary database server. The performance was suffering. I wish I had known about read replicas. With a few clicks in the cloud console, I created five copies of my database. I then configured my application to send all the intensive read queries (like generating reports) to these replicas, leaving the primary server free to handle the writes. This simple change dramatically improved my application’s performance and scalability.
I’m just going to say it: The cost of managed database services can be surprisingly high.
A startup migrated to a managed database service and loved the convenience. But as their data grew and their traffic increased, their monthly bill grew exponentially. They were paying a premium for the managed service, high-performance storage, and automated backups. While managed services are often worth the cost due to the operational savings, it’s crucial to be aware that they are a significant cost driver in any cloud bill. You must actively monitor usage and choose the right configuration to avoid sticker shock.
99% of developers make this one mistake when connecting their application to a cloud database.
The most common mistake is hardcoding the database credentials (username, password, and endpoint) directly into their application’s source code. This is a massive security risk. If the code is ever leaked, an attacker has the keys to the database. The correct approach is to store these secrets in a dedicated secrets management service (like AWS Secrets Manager or HashiCorp Vault). The application can then fetch the credentials securely at runtime, without them ever being stored in the code.
This one small action of enabling automated backups for your cloud database will change your disaster recovery strategy forever.
A developer accidentally dropped a critical table from the production database. The most recent backup was from the night before, resulting in the loss of a full day of customer data. On their next project, they enabled automated, point-in-time recovery on their managed cloud database. When a similar accident happened, they were able to restore the database to the exact state it was in one minute before the deletion occurred, turning a major disaster into a five-minute fix.
The reason your cloud database is slow is due to unoptimized queries.
A team complained that their powerful, expensive cloud database was slow. They blamed the cloud provider. A database expert was brought in. He analyzed their queries and found they were poorly written, performing full table scans and complex joins without proper indexing. He spent a day rewriting a few key queries. The application’s performance improved by a factor of ten, even after they moved to a much smaller and cheaper database instance. The problem wasn’t the cloud; it was the code.
If you’re still managing your own database patching and upgrades, you’re losing valuable time.
A database administrator spent one weekend every quarter planning and executing the security patching and version upgrades for the company’s self-hosted databases. It was a stressful, high-risk process. A company using a managed cloud database service didn’t have this problem. The cloud provider handled all the patching and upgrades automatically during pre-defined maintenance windows. This allowed their DBA to focus on higher-value tasks like performance tuning and schema design, instead of routine maintenance.
Cloud Cost Management
Use cloud cost management tools, not spreadsheets.
A finance team tried to manage their company’s cloud spending by manually exporting billing data into a massive spreadsheet each month. It was a slow, error-prone process that was always out of date. They switched to a dedicated cloud cost management tool. The tool provided real-time visibility into their spending, automatically categorized costs by project and team, and identified optimization opportunities. They could now manage their cloud costs proactively, not reactively from a month-old spreadsheet.
Stop doing reactive cost optimization. Do proactive FinOps instead.
A company would get a huge cloud bill at the end of the month, and then a “cost optimization” team would scramble to find savings. This was a reactive, firefighting approach. They adopted FinOps, a cultural practice that brings financial accountability to the cloud. They created cross-functional teams of finance, operations, and developers who worked together. Developers could see the cost impact of their code in real-time, and finance could create more accurate forecasts. FinOps made cost a shared responsibility, not an afterthought.
The #1 secret for significantly reducing your cloud bill that your provider hopes you don’t discover.
The secret is aggressively identifying and deleting “orphaned” resources. An orphaned resource is something that is still running and incurring charges but is no longer attached to any active application. The most common example is an expensive disk volume that remains after the virtual machine it was attached to has been deleted. Cloud providers don’t make it easy to find these. Using a specialized tool or script to hunt down and delete these unused resources can often lead to immediate and significant cost savings.
The biggest lie you’ve been told about the “pay-as-you-go” pricing model.
The lie is that “pay-as-you-go” is always the cheapest option. It’s fantastic for spiky, unpredictable workloads. But if you have a server that you know is going to run 24/7 for the next year, paying the on-demand, hourly rate is the most expensive way to run it. By committing to that usage upfront with a “Reserved Instance” or a “Savings Plan,” you can get the exact same server for a discount of up to 70%. Pay-as-you-go offers flexibility, but commitment provides savings.
I wish I knew this about reserved instances and savings plans when I started using the cloud.
For the first year of using the cloud, my company paid the on-demand price for everything. Our bill was high, but we thought that was just the cost of doing business. Then, I learned about reserved instances. I analyzed our usage and found several large database servers that had been running continuously for the entire year. By purchasing a one-year reservation for these servers, I cut their cost by 40% overnight, with no changes to the infrastructure. I wish I had known that I was leaving a huge amount of money on the table.
I’m just going to say it: The biggest driver of cloud cost overruns is developers.
In the old world, a developer had to file a ticket and get budget approval to get a new server. In the cloud, they can spin up a massive, expensive database cluster with a single API call, and they often don’t see the bill. A developer at a startup, experimenting with a new AI model, accidentally left a cluster of powerful GPU instances running over the weekend, resulting in a $20,000 bill. Empowering developers is great, but without giving them visibility into the cost of their actions, you are setting yourself up for financial disaster.
99% of organizations make this one mistake with their cloud spending.
The most common mistake is failing to establish a “showback” or “chargeback” model. Different teams and projects use cloud resources, but the bill just goes to a central IT budget. Nobody knows how much their specific project is costing the company, so nobody has an incentive to be efficient. By implementing a system that shows each team their specific slice of the cloud bill, you create accountability. When a team sees that their un-optimized application is costing the company $10,000 a month, they are suddenly very motivated to fix it.
This one small action of tagging all your cloud resources will change the way you track your costs forever.
A company’s cloud bill was a single, terrifying number. They had no idea which projects or teams were responsible for the cost. They implemented a mandatory tagging policy. Every single resource launched in the cloud had to be “tagged” with metadata, like the project name, the team owner, and the cost center. Now, their cloud billing console could group the costs based on these tags. They could finally see exactly where their money was going, which is the first and most critical step to controlling it.
The reason your cloud bill is so high is because of orphaned resources.
A developer was testing a new application and spun up a powerful virtual machine with a large, expensive disk attached. After the test, she terminated the virtual machine. But she forgot to delete the disk volume. That “orphaned” disk, now attached to nothing, sat in their cloud account, silently incurring charges month after month. Multiplying this small mistake by hundreds of developers over a year is a primary reason for cloud cost bloat. Regularly scanning for and deleting these unattached resources is essential cost hygiene.
If you’re still not setting budgets and alerts for your cloud spending, you’re losing control of your finances.
A startup gave their developers free rein to use the cloud. They didn’t set up any budgets or alerts. A bug in a script caused an infinite loop that created thousands of expensive cloud resources. By the time they discovered it two days later, they had a bill for over $50,000. A simple billing alert, configured to send an email when the projected monthly spend exceeded their budget, would have notified them of the problem within hours, saving them a massive amount of money.
Multi-Cloud
Use a multi-cloud strategy for resilience and avoiding vendor lock-in, not just because it’s a buzzword.
A company adopted a multi-cloud strategy for a good reason. Their primary cloud provider had a major, region-wide outage that took their application offline for hours. They re-architected their critical services to run actively on two different cloud providers. Now, if one provider has an issue, they can seamlessly fail over to the other. This deliberate strategy to improve resilience is a valid reason for going multi-cloud, unlike just using different clouds for different projects without a coherent plan.
Stop doing manual deployments to multiple clouds. Do use a multi-cloud management platform instead.
A team was trying to maintain identical infrastructure on both AWS and Azure. They were managing two separate sets of deployment scripts and Terraform configurations. It was a complex, error-prone mess. They adopted a multi-cloud management platform that provided a single, unified interface to deploy and manage their resources across both clouds. This abstraction layer simplified their operations and ensured consistency, allowing them to truly manage their multi-cloud environment instead of just struggling with it.
The #1 tip for a successful multi-cloud strategy that will save you headaches.
The most important tip is to standardize on open-source tools and technologies that are cloud-agnostic. Instead of using a proprietary database service that only exists on one cloud, use an open-source database like PostgreSQL that you can run anywhere. Instead of using a provider’s specific container service, use Kubernetes, which is the industry standard. By building your application on a foundation of portable, open-source technology, you make it much easier to move and run your workloads across different cloud environments.
The biggest lie you’ve been told about the simplicity of multi-cloud.
The lie is that multi-cloud is as simple as using a little bit of AWS here and a little bit of Google Cloud there. The reality is that a true multi-cloud strategy, where applications can run across and between different clouds, is incredibly complex. Each cloud has its own unique APIs, networking models, and security paradigms. Managing this complexity requires a highly skilled team and sophisticated tooling. For most companies, the operational overhead of a true multi-cloud strategy far outweighs its benefits.
I wish I knew this about the data transfer costs between different cloud providers.
We had a brilliant multi-cloud architecture. Our application was running on one cloud provider, and our large data warehouse was on another. We thought we were getting the best of both worlds. Then we got the first bill. The cost of transferring terabytes of data every day from the application cloud to the data cloud was astronomical. I wish I had known that while data ingress (moving data in) is cheap, data egress (moving data out) between clouds is very expensive. This cost can completely undermine the financial viability of a multi-cloud architecture.
I’m just going to say it: For most companies, a single cloud provider is the better choice.
The tech industry loves to talk about the strategic advantages of multi-cloud. But for the average company, the reality is that going all-in on a single, major cloud provider is a more pragmatic and productive choice. It allows your team to develop deep expertise in one ecosystem, you can take advantage of volume discounts, and you avoid the massive operational complexity of managing multiple platforms. The risks of vendor lock-in are often exaggerated and are, for most, a smaller problem than the guaranteed complexity of multi-cloud.
99% of companies make this one mistake when going multi-cloud.
The most common mistake is assuming that skills are portable. A company had a team of expert AWS engineers. They decided to adopt a multi-cloud strategy and start using Azure. They assumed their team could just figure it out. They were wrong. While the high-level concepts are similar, the implementation details, service names, networking, and security models are completely different. Their team struggled, and the project was delayed. A successful multi-cloud strategy requires a deliberate investment in training and hiring for each specific platform.
This one small action of abstracting your application from the underlying cloud provider will change your multi-cloud strategy forever.
A team built their application to be deeply integrated with the specific managed services of one cloud provider. When they tried to move it to another cloud, they found it was almost impossible without a complete rewrite. A different team made a conscious effort to abstract their application from the cloud. They used standard interfaces and open-source tools like Kubernetes. This one action of creating a layer of abstraction made their application portable, allowing them to deploy it to any cloud provider with minimal changes.
The reason your multi-cloud strategy is failing is due to a lack of a unified control plane.
A company was using three different cloud providers. Their security team had to use three different consoles to manage identities and permissions. Their operations team had to use three different sets of tools to deploy and monitor applications. There was no single source of truth. This lack of a unified control plane created security gaps, operational inefficiencies, and chaos. A successful multi-cloud strategy requires a tool or platform that can provide a single pane of glass for managing security, governance, and operations across all clouds.
If you’re still thinking that multi-cloud is just about using services from different providers, you’re losing the bigger picture.
An organization said they were “multi-cloud” because their marketing team used Mailchimp and their sales team used Salesforce. This is not a multi-cloud strategy; it’s just using SaaS applications. A true multi-cloud strategy involves making conscious architectural decisions about where to run your own custom-built applications and data workloads. It’s about resilience, workload placement, and avoiding lock-in for your core infrastructure, not about the third-party software you use.
Cloud Governance
Use policy as code to enforce your cloud governance, not manual reviews.
A company’s cloud governance process required a developer to fill out a form and wait for a manual review before they could create a new database. The process was slow and bureaucratic. They switched to a “policy as code” model using a tool like Open Policy Agent. They wrote their governance rules as code—for example, “all databases must be encrypted.” Now, when a developer tries to create a non-compliant resource, the action is automatically blocked with a clear error message. This made governance automated, instant, and scalable.
Stop doing a “wild west” approach to cloud adoption. Do establish a cloud center of excellence (CCoE) instead.
A company let every team adopt the cloud on their own. The result was chaos: dozens of different account structures, no consistent security standards, and rampant cost overruns. It was a “wild west.” They fixed this by creating a Cloud Center of Excellence (CCoE). This central team of cloud experts was responsible for establishing best practices, creating standardized templates, and providing guidance to the rest of the organization. The CCoE brought order to the chaos and enabled the company to adopt the cloud in a secure and cost-effective way.
The #1 secret for effective cloud governance that fosters innovation, not bureaucracy.
The secret is to create paved roads, not roadblocks. A bureaucratic governance model says “no” to everything. An effective model says, “You can do whatever you want, as long as you stay on this ‘paved road’ that we’ve built for you.” A CCoE created a set of pre-approved, secure, and cost-optimized templates for launching common applications. Developers were free to innovate and deploy as quickly as they wanted, and the organization was confident that they were doing so in a way that complied with all the governance rules.
The biggest lie you’ve been told about cloud governance stifling agility.
The lie is that governance and agility are enemies. Bad governance—the kind that involves manual review boards and month-long approval processes—certainly stifles agility. But good, automated governance actually enables agility. When you have automated guardrails in place that prevent developers from launching insecure or non-compliant resources, you can give them more freedom and autonomy to innovate safely. They can move faster because they know the automated governance will protect them from making a major mistake.
I wish I knew this about the importance of a consistent tagging strategy for cloud governance.
When we first started using the cloud, we had no tagging strategy. Resources were created without any metadata. A year later, we had a major security incident and had no way of knowing which team owned the affected server. We also couldn’t track our costs by project. I wish I had known that a consistent tagging strategy is the absolute foundation of cloud governance. By mandating that every resource be tagged with its owner, project, and environment, we could finally manage our security, costs, and compliance effectively.
I’m just going to say it: Your organization is not ready for the cloud without a solid governance framework.
A company, eager for the benefits of the cloud, gave all its developers access to a cloud account with no rules or guardrails. Within six months, they had multiple public S3 buckets with sensitive data, massive cost overruns from orphaned resources, and no consistent security posture. They had to halt their cloud adoption and spend months cleaning up the mess. You wouldn’t give a teenager the keys to a Ferrari without any driving lessons or rules. Similarly, you shouldn’t give your organization access to the cloud without a clear governance framework.
99% of organizations make this one mistake with their cloud governance.
The most common mistake is creating a governance plan in a vacuum and then trying to impose it on the development teams. The central IT team will create a 100-page document of rules without consulting the people who actually build the software. The developers, seeing the rules as impractical and bureaucratic, will then find ways to work around them. Effective governance is a collaborative process. It involves working with the development teams to create practical, automated guardrails that help them move faster, not slower.
This one small action of automating your guardrails will change the way you manage your cloud environment forever.
A cloud administrator used to spend his days manually scanning the environment for security issues, like a storage bucket that had been made public. It was a stressful, reactive game of whack-a-mole. He then implemented an automated guardrail using a cloud-native service. Now, if anyone tries to make a storage bucket public, the action is automatically blocked in real-time. This one small action of moving from manual detection to automated prevention transformed his job from a firefighter to an architect.
The reason your cloud governance is failing is due to a lack of buy-in from your development teams.
A company’s governance board mandated a new security policy that required a complex, manual approval process for every new server. The development teams, seeing this as a major roadblock to their agility, simply ignored the policy and found shadow IT workarounds. The governance plan failed because it didn’t have the consent of the governed. A successful governance strategy is developed in partnership with the engineering teams, focusing on solutions that are both secure and developer-friendly.
If you’re still manually approving every cloud resource creation, you’re losing the agility benefits of the cloud.
A developer at a large bank had to fill out a 10-page form and wait three weeks for a committee to approve her request for a new test database. This legacy, on-premise mindset completely negated the primary benefit of the cloud: agility. A modern, well-governed organization automates this process. Using policy as code, the developer’s request for a database would be automatically checked against the company’s security and cost policies, and if compliant, it would be provisioned in minutes, not weeks.
Cloud Certifications
Use cloud certifications to validate your skills, not just to get a job.
A developer crammed for a cloud certification exam, memorizing facts and passing. He put the certification on his resume but had never actually built anything in the cloud. He struggled in interviews when asked to solve real-world problems. Another developer earned the same certification after spending months building a real application. The certification for her wasn’t the goal; it was a validation of the practical skills she had already acquired. She aced her interviews because she had the hands-on experience to back up the piece of paper.
Stop doing rote memorization for your certification exams. Do hands-on practice instead.
A student tried to pass a cloud certification exam by only reading the study guide and watching videos. He failed. The exam questions were scenario-based and required a deeper understanding than rote memorization could provide. For his second attempt, he spent 80% of his study time in the cloud console, actually building things. He set up virtual networks, launched databases, and configured security policies. This hands-on practice gave him the practical knowledge he needed to understand the “why” behind the concepts, and he passed easily.
The #1 tip for passing your cloud certification exam on the first try.
The most effective tip is to take as many high-quality practice exams as you can find. A candidate studied all the material and felt confident. She then took a practice exam and scored a 50%. The practice test exposed her weak areas and, more importantly, got her used to the style and difficulty of the real exam questions. She spent the next week focusing on the topics she missed and taking more practice tests. When she walked into the real exam, it felt familiar, and she passed with a high score.
The biggest lie you’ve been told about the value of cloud certifications.
The lie is that getting a cloud certification will automatically land you a high-paying job. A recent graduate with no experience earned three different cloud certifications and was frustrated that he wasn’t getting any job offers. The certification is a signal to employers that you have a baseline level of knowledge, but it is not a substitute for experience. A portfolio of personal projects you’ve built in the cloud is often far more valuable to a hiring manager than another certificate on your resume.
I wish I knew this about the different learning paths for cloud certifications when I started.
When I decided to get a cloud certification, I just picked the most popular one: the Solutions Architect associate exam. It was a great certification, but it was very broad. I later discovered that there were specialized certification paths for roles like security, networking, and data analytics. I wish I had known to align my certification choice with my actual career interests from the beginning. Choosing a specialty path would have given me deeper, more relevant knowledge for the specific cloud role I wanted.
I’m just going to say it: A cloud certification does not make you an expert.
A manager hired a new employee who had an impressive-sounding “Professional Cloud Architect” certification. He assumed the new hire was an expert who could lead their cloud strategy. He was disappointed to find that while the employee could answer trivia questions about different cloud services, he lacked the deep, practical experience needed to design a complex, real-world system. A certification proves you can pass a test; expertise is earned through years of hands-on experience, solving real problems and learning from failures.
99% of people make this one mistake when preparing for a cloud certification exam.
The most common mistake is only studying the “happy path.” They learn how to correctly configure a service according to the documentation. But the certification exams are full of questions about what happens when things go wrong. “What is the most likely reason for this error?” or “How would you troubleshoot this connectivity issue?” The best way to prepare is to intentionally try to break things in a test environment. This hands-on troubleshooting is the best way to learn the material at a deeper level.
This one small action of building real-world projects in the cloud will change your understanding of the concepts forever.
A student was struggling to understand the difference between various cloud networking concepts by just reading about them. She decided to build a simple, real-world project: a three-tier web application with a public-facing web server, a private application server, and a database in an isolated subnet. The act of actually configuring the virtual network, the subnets, the security groups, and the routing tables made the concepts click in a way that no book or video ever could. You don’t truly understand it until you’ve built it.
The reason you failed your cloud certification exam is because you didn’t have enough hands-on experience.
A candidate failed his certification exam despite having memorized the official study guide. He was stumped by the scenario-based questions that required him to choose the “best” solution for a given problem. These questions test your judgment, which is something you can only develop through experience. For his second attempt, he spent a month working on hands-on labs and personal projects. This practical experience gave him the context and intuition needed to correctly answer the scenario-based questions and pass the exam.
If you’re still thinking that a certification is all you need to get a cloud job, you’re losing out to more experienced candidates.
Two candidates applied for a junior cloud engineer role. Both had the same associate-level cloud certification. The first candidate’s resume just listed the certification. The second candidate’s resume also included a link to his GitHub profile, where he had a portfolio of projects he had built in the cloud, with well-documented code. The hiring manager interviewed the second candidate. The certification might get you past the initial HR filter, but a portfolio of tangible, hands-on work is what will get you the job.