I've been diving deeper into Azure recently, and I keep encountering a challenge: while there's a wealth of data available through tools like Azure Monitor, Cost Analysis, and Advisor, turning that data into actionable optimization changes isn't as straightforward as it seems. I'm especially interested in how to identify what resources are safe to downsize, understand the potential impacts of changes, and clarify ownership across different teams. From what I've seen, the process often ends up being quite manual or ad hoc. I'm curious—how do others manage this in their environments? Do you mostly rely on Azure's built-in tools, or do you have a more structured approach to cost optimization?
7 Answers
Visibility is just the start—actually deciding what to change is the tricky part. We've found success in assigning ownership to each service. Without clear owners, nothing gets optimized. We also use simple rules like "if CPU is below 30% for X days, it’s time to downsize." We monitor during the scaling down process to ensure stability.
To truly optimize, it’s crucial to understand your platform and workloads and size your resources accordingly.
Do you find that keeps being accurate over time, or do you need to revisit your sizing decisions regularly?
Advice from advisors can get you started, but the real cost savings come from deeper structural changes. For instance, if you have steady workloads, consider reserved instances as they can save you a significant amount. Plus, look closely at your storage tiers; many organizations can save by moving data to colder storage. Regularly cleaning up unused resources can also lead to substantial savings.
I’ve been experimenting with tools like Copilot and Claude. They help analyze costs based on screenshots and even provide recommendations for reducing expenses. While no single tool covers everything, thorough management and experience play a big role.
What was the main cause for the high backup costs you found?
That’s interesting! How reliable do you find those recommendations? Do you still need to double-check them before making changes?
One of the big opportunities for saving lies in understanding your logging needs. Teams tend to over-log, which can get costly. Many aren't aware that Azure logs come in different tiers; for example, the default 'analytical' tier can be about five times pricier than the 'basic' tier. Setting a budget with alerts can also help—though I find hardly anyone sticks to it!
That's a valid point! I recently worked with a new client who was generating 200GB of Log Analytics data every day, and they weren't even using the logs. It's insane!
It feels more reactive, right? You notice costs spiking only after they've gone over a certain limit. Have you ever thought about a more proactive strategy for logging?
We utilize ProsperOps for optimizing reservations. Beyond that, it’s crucial to know your environment and stay diligent. If you're careless about costs, you could end up overspending quickly. We've established strict scaling rules and use Azure Policy for necessary audits.
Your approach sounds well-organized! Do you still encounter situations where costs drift or need revising, despite all the controls in place?
Quick note—I'm a solo founder working on a product designed for cost optimization, which includes features for identifying orphaned resources and VM sizing issues. It’s still in beta and completely free to try! If it sounds interesting, let me know here or check out my site.
It’s fascinating how much of this remains a manual process. It seems more like a people issue around trust and accountability. What would you think needs to change to eliminate that validation step?