Aliens.
Dark Knight.
Empire Strikes Back.
Die Hard with a Vengeance.
If there’s one thing we love at Incredibuild, it’s a great sequel.
So, we’re following up with a more in-depth sequel about cloud optimization — but this time, we’re talking cloud cost optimization, DIY-style.
(If you’re not sure what cloud optimization is, go check out our previous blog post for the basics, and come back for more. Like any good sequel, you need to see the first part to make it make sense!)
In this blog post, we’ll take a look at what DIY cloud cost optimization is, the challenges it can bring, and how best to get around them with a mix of manual and managed cloud-based software.
What is DIY cloud cost optimization?
As we discovered in our first post, businesses are embracing the flexibility and scalability of cloud services in a big way.
But with the cloud computing benefits come challenges — one of the significant concerns is how to manage cloud costs effectively.
It’s only by doing this that organizations can maximize their investments and ensure financial efficiency in the cloud.
Enter cloud cost optimization — a strategic approach that implements various techniques, best practices, and tools to reduce cloud spend while maintaining or improving your overall business value.
Pretty handy, if you ask us.
Cloud cost optimization practices balance costs against business goals, identify and eliminate wasteful spending, and ensure that computing resources are appropriately sized and used efficiently.
Methods for DIY cloud cost optimization
But here’s the next big question — how do you carry out DIY cloud cost optimization?
The answer: there’s no single ‘proper’ way to do it.
Instead, what you’re typically looking at is a series of manual methods combined with a mix of different options — such as community-based, open source, or homegrown cloud-based software — that you can compile to address different parts of the process and monitoring.
This could include:
- Monitoring and tracking cloud spend with spreadsheets
- Automating certain tasks with specific scripts — especially with robotic process automation (RPA)
- Using AWS Cost Explorer or other native tools
- Managing scaling and rightsizing manually
- Using custom dashboards (plenty of cloud computing companies offer these)
And while these aren’t terrible options, none of them are ideal – and they also pose some challenges for developers.
Challenges of DIY cloud cost optimization
1. Visibility
One of the biggest challenges to your cloud cost optimization efforts will be a lack of visibility and restricted access to information.
The risk is that your work will be siloed, repeated, and waste valuable time.
Put it this way — you could be working on your own cloud budgeting spreadsheet. Your colleague could be working on a different spreadsheet that they’d set up. And your manager could also be creating a new spreadsheet by themselves.
Unless you’re communicating what you’re all doing every step of the way, there’s no way to know who’s worked on what, what still needs to be done, and who can access what spreadsheets, resulting in major data fragmentation.
2. Time
No matter where you work, time is money.
And doing cloud cost optimization DIY takes a heck of a lot of time and resources to make it work.
Perhaps you’re flying solo and organizing everything yourself, or part of a small development team. You may be able to set aside the time as a full-on project to sort out spreadsheets, scripts, cost optimization tools, dashboards, you name it.
But with a larger organization in the picture, where you or your team might be on several different projects at once, you’ll have less time to dedicate to DIY cloud cost optimization, making it more and more difficult to finish what you’ve started.
3. Scalability
Imagine 100 people all trying to work from a single cloud cost optimization spreadsheet.
Someone accidentally removes a number. Another developer wants to try something new and amends the spreadsheet, accidentally removing some previous important information. And someone in Finance doesn’t like the layout and decides to change it on a whim.
Now imagine that 100 times over.
Larger organizations will struggle to scale up these processes, simply because too many people will be working from the same documents at once. It’s like the saying goes — too many cooks spoil the broth.
Meanwhile, independent developers or small development teams can stay agile and work from a DIY cost optimization single spreadsheet if need be, without introducing the possibility of more errors or random changes.
4. Missed opportunities
Let’s say there’s a 50% discount on a cloud cost optimization tool, for a limited time only.
But you didn’t see it that day — you were too busy on another task or project.
By the time you see the email with the big shiny discount, you notice that the discount has already expired.
That’s another of the big challenges when you’re working DIY: you’re never going to be on top of all the latest offers 100% of the time.
So this means you have to be laser-focused on any offers to save money or optimize costs, by jumping on limited-time-only discount opportunities, or being quick enough to reserve instances at exactly the right moment.
And as any developer knows, that’s like being asked to be in three different places at one time — impossible.
5. Prioritizing waste reduction over cost reduction
You might be re-reading that title again and wondering, “What’s the difference?”
It might sound semantic. But ‘waste reduction’ and ‘cost reduction’ aren’t the same thing.
Cost reduction, i.e., optimization, is all about building more effective processes to keep your cloud costs low.
Waste reduction is about constantly finding ways to reduce waste in the work being created.
But by focusing more on cutting down waste rather than building effective processes, you’re essentially only looking at potential outcomes at the end of the process, rather than looking at the entire process from start to finish.
What about recommendation-based software?
It’s about this time you’ll be thinking, “Surely recommendation-based software can help avoid all of these challenges?”
Yes and no.
Recommendation-based software is designed to analyze cloud usage patterns, identify potential areas for cost savings, and provide actionable recommendations. To do that, these tools leverage algorithms and historical data to suggest optimizations, ranging from rightsizing instances to using discount instruments effectively.
But recommendation-based tools bring their own set of challenges.
For example, recommendation-based software still needs manual validation and management to implement any suggested optimizations. Even with these recommendations, developers still need to validate, implement, and check that these proposed changes align with their specific requirements.
And speaking of requirements, these recommendation-based tools don’t understand your organization’s overall infrastructure, applications, and business needs. So the recommendations that you do get might not align with the broader goals of your company, or overlook certain critical factors.
That puts pressure on the development staff to justify each recommendation to non-development teams, relying on your expertise for decision-making which can slow down the whole optimization process.
So to answer the previous question: recommendation-based software is helpful but still a challenge, and it relies on your manual optimization and management.
Find the right blend of manual and managed
So to recap, recommendation-based tools aim to automate parts of the optimization process, but this often results in a hybrid approach where manual effort remains a significant component.
And development teams that rely on manual DIY optimization face challenges like data fragmentation, a lack of centralized reporting, and difficulties in decision-making and validation.
So what’s the answer?
While organizations aim for more efficient and streamlined cloud cost optimization, a balance between manual and autonomous cloud cost management is the next logical step.
To overcome the challenges brought by recommendation-based tools and the limitations of manual efforts, you need to shift towards autonomous cloud cost management.
This means using algorithms and real-time telemetry to autonomously recognize resource usage patterns, deploy discounts, and optimize for savings performance.
This automation is key — streamlining the optimization process, removing manual back-and-forth processes, and efficiently handling complex discount management.
By embracing automation, you can achieve the full potential of your cloud computing, making sure you’re not only cutting costs but also improving efficiency.
