Puneetha Jalagam

Posted on Jun 30

From Resource Allocation to Resource Optimization: The Kubernetes Journey

#kubernetes #devops #cloudnative #finops

If you have ever looked at your cloud bill after running Kubernetes for a while and felt shocked, you are not alone. Most teams start out just trying to get things running. You set some numbers for CPU and memory, deploy, and move on. Later, the bill arrives, and you realize running something and running it well are not the same thing.

This is a journey almost every Kubernetes team goes through. It starts simple and slowly gets smarter. Knowing where you stand can save your team a lot of money and stress.

Why This Matters

Kubernetes does exactly what you tell it to do, even if it wastes money. Nobody gets a warning that says "you are paying for way more than you use." You have to find that out yourself.

Most companies waste 30 to 50 percent of their Kubernetes spend on resources they do not need. That is a lot of money. This is not just a tech problem. It is a business problem too.

The Five Stages of the Journey

Stage 1: Basic Allocation

This is where everyone starts. You tell Kubernetes how much CPU and memory each app needs.

There are two numbers here:

A request is what the app is guaranteed to get
A limit is the most it is allowed to use

Most teams set these too high at first. Usually for two reasons:

Fear that the app will crash if it gets too little
Copying the same numbers across many apps without checking if they fit

For example, an app that only needs a small amount might get set up with five times that much, just to be safe. Do that across fifty apps, and your cluster becomes much bigger than it needs to be.

This is normal. It is just the starting point.

Stage 2: Visibility

You cannot fix a problem you cannot see. This stage is about noticing the gap between what you asked for and what you actually use.

Simple tools can help here, like built-in usage commands or dashboards that show usage over time. Some tools can even suggest better numbers based on real usage.

This step is often a wake-up call.

Many teams discover they requested most of a server's capacity but are only using a small part of it.

Stage 3: Right-Sizing

Once you can see the gap, the next step is closing it. This means adjusting your numbers to match real usage instead of guesses.

A simple way to do this:

Watch usage for at least one to two weeks
Look at the busiest moments, not just the average
Set your numbers close to normal usage
Leave a little extra room for spikes
Check again after big changes or new features

This step alone often cuts wasted resources by a large amount, sometimes by half or more.

Stage 4: Autoscaling

Right-sizing fixes individual apps. Autoscaling makes the whole system adjust on its own as demand changes.

Three tools usually work together here:

One that adds or removes copies of an app based on traffic
One that adds or removes entire servers based on need
One that automatically adjusts an app's resource numbers over time

Together, these keep your system matched to real demand instead of guessing ahead of time.

Stage 5: Continuous Optimization

The last stage is realizing this work is never really finished. It becomes a habit, similar to checking security or reviewing code.

Teams that do this well usually:

Review resource usage every month or quarter
Set alerts when usage and requests are far apart
Track costs by team, so people see the impact of their own choices
Clean up unused storage and leftover resources regularly

Best Practices

Use real usage data, not guesses
Limit how much any one team can use
Make sure scaling does not accidentally cause downtime
Label apps by team so costs are easy to track
Be careful using two automatic scaling tools on the same setting
Pay as much attention to limits as you do to requests

Common Mistakes to Avoid

Setting requests and limits to the exact same number, which removes flexibility
Ignoring memory limits, since going over memory kills an app instead of just slowing it down
Treating this as a one-time fix instead of an ongoing habit
Cutting resources too aggressively and hurting performance just to save money
Making changes without talking to the team that owns the app

Simple Tips to Start This Week

Check your busiest app's real usage and compare it to what it is requesting
Pick your three biggest apps and look at their usage over the last two weeks
Try a recommendation tool on one low-risk app to see what it suggests
Build a simple chart comparing requested versus actual usage
Set a short monthly meeting just to talk about resource use

Conclusion

Moving from basic allocation to real optimization does not happen overnight. It is a journey every Kubernetes team goes through, starting with cautious guesses and slowly moving toward smarter, data-based decisions.

The teams that get the most value from Kubernetes are not the ones with the biggest servers. They are the ones who keep checking and adjusting.

Key Takeaways

Most teams start out asking for more than they need
You need visibility before you can fix anything
Right-sizing should be based on real data, not guesses
Automatic scaling tools work best when used together carefully
Optimization is an ongoing habit, not a one-time task
Memory limits need extra care since going over them kills an app

FAQ

1. What is the difference between a request and a limit?
A request is what an app is guaranteed to get. A limit is the most it can use before it gets slowed down or stopped.

2. Why do teams ask for more resources than they need?
Mostly out of caution. Without real usage data, people tend to play it safe.

3. How do I check real usage?
Simple built-in tools can show current usage. Dashboards can show trends over time.

4. What is the efficiency gap?
It is the difference between what you asked for and what you actually use. A big gap usually means wasted money.

5. How often should I review resource usage?
At least once a quarter. Many teams do it monthly or after big changes.

6. Is automatic resource adjustment safe to use?
Yes, especially when started in a recommendation-only mode that does not change anything until you are ready.

7. Can I use multiple scaling tools together?
Yes, but avoid having two tools control the exact same setting, since this can cause conflicts.

8. What happens if memory limits are too low?
The app gets stopped. This is different from CPU, where going over a limit just slows things down.

9. Is this only about saving money?
No. It also improves stability and performance by avoiding both too much and too little.

10. What tools help with this kind of visibility?
Built-in tools are a good start. Dedicated platforms like EcoScale are built specifically for this.

11. How much usage data should I collect before adjusting?
At least one to two weeks, to capture both busy and quiet periods.

12. What is the difference between adding more copies of an app and adding more servers?
One adjusts how many copies of an app are running. The other adjusts how many servers are available to run them.

13. Should requests and limits ever match exactly?
Sometimes, for apps that need guaranteed performance. Most apps do better with some room between the two.

14. Why does scaling need safety limits in place?
To make sure enough copies of an app stay running during changes, so things do not go down unexpectedly.

15. How do I get my team to care about this?
Show them their own usage numbers. Clear data tends to build interest faster than rules.