AI preferences coming soon...
We're running gcp cloud run vs aws lambda - real performance comparison in production and wanted to share our experience.
Scale:
- 440 services deployed
- 76 TB data processed/month
- 41M requests/day
- 6 regions worldwide
Architecture:
- Compute: App Runner
- Data: S3 + Athena
- Queue: Kinesis
Monthly cost: ~$69k
Lessons learned:
1. Serverless not always cheaper
2. S3 lifecycle policies are essential
3. FinOps team paid for itself
AMA about our setup!
What about security? Did you run into any compliance issues? Trying to build a business case for management.
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.
Security team blocked this due to compliance requirements.
We evaluated this last year. The main challenge was...
The learning curve is steep. Any good resources to recommend? Our team is particularly concerned about production stability.
In our production environment with 200+ microservices, we found that Jenkins significantly outperformed GitLab CI. The key was proper configuration of timeout settings. Deployment time dropped from 45min to 8min. Highly recommended for teams running Kubernetes at scale.
Did you use version X or Y? We found Y more stable. We're evaluating this for Q1 implementation.
