Breaking: Google Cloud Run now supports GPU workloads for ML pipelines
This is huge for the DevOps community. I've been following this development for weeks and it's finally here.
Impact on our workflows:
✓ Reduced costs
✓ Simplified configuration
✗ Initial bugs expected
What's your take on this?
Been using this for 6 months. Here's what I learned...
Resource consumption is a concern. What's your experience? Trying to build a business case for management.
Exactly! This is what we implemented last month.
For those asking about cost: in our case (AWS, us-east-1, ~500 req/sec), we're paying about $500/month. That's 50% vs our old setup with Grafana. ROI was positive after just 2 months when you factor in engineering time saved.
Security team blocked this due to compliance requirements.
What about monitoring? How do you track metrics? Our team is particularly concerned about production stability.
Did you use version X or Y? We found Y more stable. Looking for real-world benchmarks if anyone has them.
Great for small teams, but doesn't scale well past 50 people.
We evaluated this last year. The main challenge was...
How did you handle the migration? Any gotchas to watch for? Trying to build a business case for management.
In our production environment with 200+ microservices, we found that Docker significantly outperformed Grafana. The key was proper configuration of memory limits. Deployment time dropped from 45min to 8min. Highly recommended for teams running Kubernetes at scale.
What about monitoring? How do you track metrics? Trying to build a business case for management.
We benchmarked 5 solutions:
1. Option A: fast but expensive
2. Option B: cheap but limited
3. Option C: goldilocks zone ✓
Ended up with C, saved 40% vs A.
In our production environment with 200+ microservices, we found that GitLab CI significantly outperformed GitHub Actions. The key was proper configuration of scaling parameters. Deployment time dropped from 45min to 8min. Highly recommended for teams running Kubernetes at scale.