Hey everyone! I'm delving into cloud cost management and I've noticed that there are tons of solutions out there claiming to optimize resource allocation in Kubernetes. The theory suggests that we should see minimal waste, but that doesn't seem to be happening in practice.
I'm curious about the tools you've tried and what your experiences have been. How significant is the challenge of implementation compared to just identifying issues? I'm considering focusing on a specific aspect of this — aligning pod CPU and memory usage to enable node reduction — and I'm wondering if it's even worth tackling. What do you all think?
3 Answers
Honestly, I think that chasing down compute cost optimization is often a lost cause. First off, the time and resources spent optimizing usually end up costing more than any savings you'll see. Plus, don't forget about additional expenses like storage and data transfer. We're wrapping up our move to bare metal clusters, which I think is the best route for us, especially with predictable workloads. We’ve set up 440 3.5 GHz CPUs, 1.5 TB of RAM, and 16 TB of storage for just $3k a month — a significant savings compared to the hefty charges we faced using GCP with smaller setups.
In my experience, the main issue isn't about identifying where we can save costs or which tools to use. The real struggle arises from the conflicting priorities between teams. FinOps teams are pushing for reduced cloud expenses, while SREs focus on maintaining system stability and are often hesitant to approve resource reductions. To effectively tackle this, optimization has to be built right into the platform, addressing the needs of everyone involved in the software development lifecycle. We need a process that ensures cost savings for FinOps while keeping the reliability standards SREs expect, and that’s the hurdle we're currently working on.
I'm using CAST AI and it's been effective, especially with the automation for Spot instances. However, successful bin packing and rebalancing relies heavily on the whole Kubernetes team's culture and practices, like setting the right node selectors and ensuring proper configurations. If you're trying to run 100 pod replicas across different nodes, achieving cost efficiency can be really tricky.
I heard CAST AI requires elevated permissions to run efficiently. Did that raise any security concerns for your team?