AI preferences coming soon...
We've been experimenting with automated root cause analysis using ai - case study for the past 2 months and the results are impressive.
Our setup:
- Cloud: Azure
- Team size: 10 engineers
- Deployment frequency: 12/day
Key findings:
1. Deployment time reduced by 40-70%
2. False positives still an issue
3. Still needs human oversight
Happy to answer questions about our implementation!
Did you consider alternatives? Why did you choose this one? Our team is particularly concerned about production stability.
Did you use version X or Y? We found Y more stable. Trying to build a business case for management.
After 3 months of testing, here are our findings:
✓ Pros: faster deployments, better visibility
✗ Cons: initial setup complexity, team training
Overall: worth it for teams 10+
For those asking about cost: in our case (AWS, us-east-1, ~500 req/sec), we're paying about $10000/month. That's 30% vs our old setup with GitLab CI. ROI was positive after just 2 months when you factor in engineering time saved.
We evaluated ArgoCD last quarter and decided against it due to vendor lock-in. Instead, we went with Grafana 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.
Resource consumption is a concern. What's your experience? Looking for real-world benchmarks if anyone has them.
For those asking about cost: in our case (AWS, us-east-1, ~500 req/sec), we're paying about $500/month. That's 40% vs our old setup with ArgoCD. ROI was positive after just 2 months when you factor in engineering time saved.
Has anyone else encountered issues with Jenkins when running in DigitalOcean us-west-2? We're seeing intermittent failures during peak traffic. Our setup: hybrid cloud with Prometheus. Starting to wonder if we should switch to Ansible.
We evaluated Terraform last quarter and decided against it due to vendor lock-in. Instead, we went with Ansible 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.
