By Mark Lovett
Look, I’ve been in this industry long enough to remember when “moving to the cloud” sounded like some weird spiritual journey rather than a tech decision. After 15+ years of building software – from the old-school days of physical servers to today’s cloud-everything world – I’ve seen firsthand how dramatically things have changed. These days, any decent custom application development services worth their salt are building cloud-first, and for good reason. The cloud has completely transformed not just where our code runs, but how we write it in the first place. Let me walk you through what I’ve seen in the trenches.
Goodbye Monoliths, Hello Microservices (Usually)
Remember when every app was basically one massive codebase? Man, those were simultaneously simpler and more frustrating days. I spent three years maintaining a monolithic insurance processing system that required an entire weekend and a small army of DevOps people just to deploy minor updates. When something went wrong (and something always went wrong), the entire system would crash spectacularly.
Cloud platforms pushed us toward breaking things into smaller services, and despite the initial pain, I’m grateful. I’ve built both ways, and while microservices aren’t perfect (the debugging across services can be a nightmare), they’ve solved more problems than they’ve created for most of my projects.
A client of mine in the healthcare space had this ancient patient portal that would crumble under load at unpredictable times. After we broke it into services and moved to AWS, we could scale just the appointment booking component during high-traffic periods. Their support tickets dropped by like 70% almost overnight.
That said, I still occasionally build monoliths when it makes sense – not everything needs to be split into 20 different services. I worked with a startup last year where we deliberately kept things monolithic because their dev team was tiny and the operational complexity of microservices would’ve killed them. Cloud doesn’t always mean you need microservices – that’s a nuance many articles miss.
Infrastructure as Code: The Thing I Resisted Then Fell in Love With
I’ll admit it – I dragged my feet on infrastructure as code for way too long. Command-line configuration felt faster, and I liked having that manual control. Plus, learning Terraform syntax on top of everything else? No thanks.
Boy was I wrong. Now I can’t imagine going back. Just last month, our client’s AWS account got messed up beyond repair (long story involving merged accounts and some permissions nightmares). We were able to completely rebuild their environment in a different account in under a day because everything was defined in code. In the old world, that would have been weeks of work and probably some data loss.
The real game-changer isn’t even the automation – it’s the consistency. No more “it works in production but not in test” mysteries because someone manually configured something slightly differently. No more forgotten settings or undocumented changes. Everything’s in git where I can see exactly who changed what and when.
But let’s be real – there’s still a learning curve. A junior developer on my team recently created 100 security groups by accident with a misplaced loop in Terraform. Thank goodness for plan before apply!
Continuous Deployment That Actually Works
I’ve lived through so many flavors of “continuous” deployment over the years. There was the “continuous but only on Tuesdays” approach. The “continuous but only after three sign-off meetings” method. The “continuous but actually we’re scared so we batch everything up” technique.
Cloud providers have finally made true continuous deployment accessible for normal teams (not just the Googles and Netflixes of the world). The built-in pipelines on AWS, Azure and GCP aren’t perfect – I still prefer GitLab CI personally – but they’ve made advanced deployment patterns achievable without needing deployment wizards on staff.
I was working with a media company that was terrified of frequent deployments after a bad experience took down their site during peak traffic. We set up a cloud pipeline with proper canary deployments, and within two months they were deploying multiple times daily with more confidence than they used to have with their monthly releases.
The biggest obstacle is usually organizational, not technical. I’ve seen teams with amazing cloud CI/CD pipelines who still can’t deploy quickly because they need seventeen approvals and a signed statement from the CEO’s dog. The tools are only as good as your willingness to trust them.
Serverless: Amazing When It Fits, Painful When It Doesn’t
Serverless is probably the most transformative cloud development approach I’ve worked with, but it’s also been the source of my biggest headaches.
When it works, it’s magical. A property management system I built runs entirely on Lambda functions and API Gateway, costs about $50/month to operate (compared to $1000+ for the previous EC2 version), and I haven’t had to think about scaling or availability in over two years. It just works.
But serverless has also bitten me hard. Debugging is often a nightmare. Cold starts can kill latency-sensitive applications. The programming model requires rethinking how you structure code. And don’t get me started on the local development experience – it’s getting better with tools like LocalStack, but it’s still not great.
I built an event processing system on Lambda that worked beautifully in testing but fell apart in production because we hit concurrency limits we didn’t know existed. We had to do an emergency redesign that cost us a week of late nights.
My rule of thumb now? Serverless is perfect for:
- Background processing
- API endpoints with moderate traffic
- Event-driven workflows
- Scheduled tasks
But I avoid it for:
- Ultra-low latency requirements
- Very complex business logic
- Long-running processes
- Anything that needs a hefty runtime memory footprint
Managed Data Services That Sometimes Feel Like Magic
One of the biggest wins in cloud development has been managed database services. I used to consider myself decent at database administration, but honestly, I never enjoyed it. Now I can get better performance and reliability than I could configure myself, without spending hours on backups, replication, and patching.
I’m working with a logistics company that uses Amazon Aurora, and we accidentally discovered the automated backup feature when a developer ran a delete query without a where clause (we’ve all been there). We were able to restore to literally 5 minutes before the mistake with a few clicks. In the old world, that might have been game over or at least a major incident.
The tradeoff is definitely cost – you pay a premium for managed services. I’ve had more than one client shocked by their RDS bill compared to running their own MySQL on EC2. But when I calculate the actual cost of a DBA’s time plus the risk of downtime, it usually makes sense.
One thing that still frustrates me – the default settings on managed services aren’t always sensible. Azure SQL in particular seems to ship with configurations that work fine in testing but fall over under real load. You still need to know what you’re doing, even with “fully managed” services.
Security: Both Easier and Harder
Cloud security is a mixed bag. On one hand, providers like AWS give you incredible tools – IAM roles, security groups, KMS, etc. On the other hand, the sheer number of settings and interactions between services creates complexity that’s easy to mess up.
I’ve seen brilliant developers accidentally expose sensitive data because they didn’t understand S3 bucket policies. I’ve done it myself! The default on many cloud services still isn’t secure enough, and the “just click through the console” approach can lead to security holes.
The best teams I’ve worked with bake security scanning directly into their pipelines and treat security configurations as code just like everything else. It’s more work upfront but saves massive headaches later.
The Cost Management Migraine
If there’s one thing that’s caught nearly every team I’ve worked with by surprise, it’s cloud costs. The pay-as-you-go model is fantastic for getting started, but it can spiral quickly if you’re not careful.
I learned this lesson the hard way when a test script I wrote started hammering an API endpoint continuously over a weekend. What should have been a few dollars turned into a $3,000 surprise. Now I set up billing alerts religiously.
The teams that manage cloud costs well make it a development concern, not just an operations issue. Questions like “how will this scale?” and “what’s the cost impact?” should be part of code reviews, not afterthoughts.
Conclusion
Cloud computing has fundamentally changed how we build applications, mostly for the better. It’s democratized access to tools and capabilities that used to require massive teams and budgets. A small team today can build and run systems that would have required dozens of specialists just a decade ago.
But the cloud isn’t magic – it’s still computers running code, just computers someone else maintains. The fundamental principles of good software development haven’t changed. Clean code, proper testing, sensible architecture, and understanding your requirements are as important as ever.
What has changed is the realm of possibility. The barriers to building scalable, resilient applications have never been lower. The challenge now isn’t whether you can build something amazing – it’s making the right choices among the overwhelming number of options the cloud provides.
At the end of the day, cloud or no cloud, development is still about solving problems for users. The cloud just gives us better tools to do it with – if we use them wisely.
About the Author: Mark is a tenured writer for NewsWatch, focusing on technology and emerging trends. Mark gives readers insight into how tomorrow’s innovations will transform our relationship with technology in everyday life.
