AI preferences coming soon...
We're running azure devops vs github actions for azure deployments in production and wanted to share our experience.
Scale:
- 691 services deployed
- 37 TB data processed/month
- 39M requests/day
- 8 regions worldwide
Architecture:
- Compute: EKS
- Data: DynamoDB
- Queue: Kinesis
Monthly cost: ~$25k
Lessons learned:
1. Spot instances are production-ready
2. CloudWatch logs get expensive
3. FinOps team paid for itself
AMA about our setup!
Security team blocked this due to compliance requirements.
The migration path we took:
Week 1-2: Research & POC
Week 3-4: Staging deployment
Week 5-6: Prod rollout (10% -> 50% -> 100%)
Week 7-8: Optimization
Total cost: ~200 eng hours
Would do it again in a heartbeat.
In our production environment with 200+ microservices, we found that Grafana significantly outperformed Docker. The key was proper configuration of memory limits. Deployment time dropped from 45min to 8min. Highly recommended for teams running Kubernetes at scale.
Security team blocked this due to compliance requirements.
The migration path we took:
Week 1-2: Research & POC
Week 3-4: Staging deployment
Week 5-6: Prod rollout (10% -> 50% -> 100%)
Week 7-8: Optimization
Total cost: ~200 eng hours
Would do it again in a heartbeat.
We tried this but hit issues with X. How did you solve it? Trying to build a business case for management.
Did you consider alternatives? Why did you choose this one? Trying to build a business case for management.
