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Diving into Cloud and DevOps like a kid who learned cycling yesterday and signed up for a race today. I break things, read error logs like bedtime stories, and slowly figure out how not to repeat the same mistakes. If you enjoy honest tech journeys with a bit of humor, you’ll fit right in.
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Aditya Raj Singh
17 posts
When I started learning cloud and DevOps, the free tiers felt like a gift. AWS, Google Cloud, Azure — all of them offered a year of generous limits or a certain amount of credits for new accounts. I remember reading the marketing pages and thinking, “This is perfect. I can experiment without worrying about money.” Most beginners I talked to felt the same. The common belief was simple: as long as you stay within the free tier boundaries, nothing costs anything.
It was small about $8 but it shook me. I had been careful, or so I thought. I’d only spun up a few virtual machines, stored some files, and run a couple of experiments. Nothing extravagant.
Yet there it was: charges for data transfer, storage, and something called a NAT gateway that I barely remembered creating.
I spent an evening digging through the billing dashboard, trying to match line items to my actions. That was the moment the “free” part started to feel conditional.
Over the next year, as I kept practicing — building small projects, tearing them down, making mistakes, rebuilding — I began to see patterns.
The surprises weren’t random; they followed predictable rules that weren’t obvious from the promotional pages. The free tier wasn’t a trap, but it also wasn’t a blanket safety net. It had gaps, and those gaps taught me more about cloud economics than any tutorial ever could.
One of the earliest patterns I noticed was around idle resources.
I’d launch a virtual machine to test something, get distracted, and forget about it. A few days later I’d see it still running. The free tier in AWS, for example, gives you 750 hours per month of t2.micro or t3.micro usage. That sounds like a lot — roughly one instance running continuously.
But if you launch two instances, even small ones, the hours add up across all of them. The meter runs on total usage, not per instance. I learned this the hard way when I left a second instance running for a weekend “just in case” I needed it.
Storage works similarly.
You get a certain amount of free object storage (like S3 or Cloud Storage), but every object you upload counts until you delete it. I once created multiple buckets while experimenting with static websites, forgot to empty them, and slowly accumulated gigabytes.
The storage itself stayed free for a while, but retrieval requests and eventual overflow pushed me over the limit.
Region mistakes were another recurring surprise.
I initially assumed pricing and free tier eligibility were consistent across the world. They’re not. Some services have different limits or costs depending on the region.
More importantly, data transfer between regions — or out to the internet — often costs money, even when the compute or storage is free. I once deployed a small database in a region close to me for lower latency, then copied data from a tutorial bucket in a US region. The egress charges appeared quietly in the bill.
The cloud providers optimize their infrastructure regionally, so moving data across those boundaries has a real cost for them, which they pass on.
Another subtle one: certain resources have minimum charges regardless of usage.
NAT gateways and load balancers, for example, bill by the hour they exist, not just when traffic flows through them. I created a NAT gateway while following a guide on private subnets, then deleted the tutorial environment but forgot that one piece.
It sat there, idle, costing a few cents per hour — enough to add up over a month.
Through all these small incidents, my understanding evolved from “free means zero cost” to “free means heavily discounted with sharp edges.”
The system is logical once you see it: providers want to attract learners and startups, so they subsidize common learning workloads. But they also need to protect themselves from abuse and cover real infrastructure costs.
The free tier boundaries are drawn where typical experimentation ends and production-like usage begins. Idle high-bandwidth resources, cross-region data movement, and long-lived networking components all resemble production patterns more than learning ones, so they fall outside the subsidy.
There are trade-offs, of course.
The free tier encourages hands-on practice, which is invaluable. Without it, far fewer people would learn cloud skills. But it also trains a kind of vigilance early on.
You learn to check regions deliberately, to tag resources, to set reminders or scripts to clean up. The alternative — truly unlimited free usage — would either bankrupt the providers or force them to impose heavy restrictions that would hurt learning more than occasional small bills do.
This experience quietly changed how I approach cloud work.
I no longer assume an action is free just because I’m on a new account. I check the pricing page for the specific service and region before I click “create.” I make a habit of reviewing running resources every few days.
I even started using the billing alarms that most providers offer — simple thresholds that email you if spending crosses a limit. None of this feels like extra work anymore; it feels like part of understanding the platform.
Looking back, those small unexpected bills were some of the most effective teachers I had. They carried real-world consequences — not large enough to hurt, but enough to focus attention. The fear of another surprise charge made me read documentation more carefully, ask better questions, and notice details I’d previously skimmed over.
In a way, the hidden costs of the free tier aren’t hidden at all once you’ve paid them a few times. They’re just the tuition for learning how cloud pricing actually works.