Machine learning for cost optimization in multi-cloud environments - has anyone else tried this approach?
We're evaluating AI-powered solutions for cost management and this looks promising.
Concerns:
- Data privacy: are we comfortable sending metrics to external AI?
- Accuracy: can we trust AI for security-critical tasks?
- Cost: is the ROI there for regulated industries?
Looking for real-world experiences, not marketing hype. Thanks!
We implemented this using the following approach:
1. First step...
2. Then we...
3. Finally...
Results: significant improvement in deployment speed. Team: 14 engineers, 2 DevOps.
Great for small teams, but doesn't scale well past 50 people.
Works well in theory, but production reality is different.
We evaluated Prometheus last quarter and decided against it due to licensing costs. Instead, we went with GitHub Actions which better fit our use case. The main factors were cost (30% cheaper), ease of use (2-day vs 2-week training), and community support.
Great point! We've seen similar results in our environment.
For those asking about cost: in our case (AWS, us-east-1, ~500 req/sec), we're paying about $10000/month. That's 70% vs our old setup with Jenkins. ROI was positive after just 2 months when you factor in engineering time saved.
What about security? Did you run into any compliance issues? Looking for real-world benchmarks if anyone has them.
Has anyone else encountered issues with ArgoCD when running in AWS ap-southeast-1? We're seeing intermittent failures during peak traffic. Our setup: hybrid cloud with New Relic. Starting to wonder if we should switch to ArgoCD.
Thanks for sharing! We're planning to try this next quarter.
How did you handle the migration? Any gotchas to watch for? Trying to build a business case for management.
Pro tip: if you're implementing this, make sure to configure memory limits correctly. We spent 2 weeks debugging random failures only to discover the default timeout was too low. Changed from 30s to 2min and all issues disappeared.
Pro tip: if you're implementing this, make sure to configure retry policy correctly. We spent 2 weeks debugging random failures only to discover the default timeout was too low. Changed from 30s to 2min and all issues disappeared.
Be careful with this approach. We had production issues.