If I must choose between a cloud vendor’s managed GPU clusters and self-hosting GPUs on Kubernetes, I weigh these operational trade-offs:
Productivity
- Managed: Faster onboarding — vendor handles drivers, drivers/ML stacks, autoscaling, monitoring. Engineers spend more time on models.
- Self-hosted: More setup and debugging overhead (node drivers, CUDA, device plugins), slower iteration.
Cost predictability
- Managed: Higher unit cost but predictable billing and reduced overhead; good for bursty/variable workloads via pay-as-you-go.
- Self-hosted: Lower per-hour cost for committed hardware (especially reserved/spot buys) but less flexible; capital and utilization risk increase forecasting complexity.
Hardware control
- Managed: Limited to vendor-supported GPU models and firmware; less granular control over BIOS, custom interconnects.
- Self-hosted: Full control — choose GPU models, NVLink, PCI topology, custom drivers, and upgrades.
Scheduling complexity
- Managed: Built-in autoscaling and placement tuned for GPUs; less ops work.
- Self-hosted: Need to integrate device-plugins, topology-aware schedulers, taints/tolerations, and custom autoscalers to avoid fragmentation and wasted GPU cycles.
Portability
- Managed: Vendor APIs and managed autoscalers can create lock-in; migrating workloads requires adapting config and possibly retraining infra automation.
- Self-hosted: Kubernetes-native deployments are more portable across clouds/data centers if you adhere to standard deployments.
Long-term maintenance
- Managed: Vendor handles OS/driver/security patches; you rely on their SLAs.
- Self-hosted: Ongoing patching, hardware replacement, capacity planning, and lifecycle management—requires dedicated ops expertise.
Conclusion: For rapid experimentation and operational simplicity choose managed; for tight cost control, specialized hardware, or full portability choose self-hosted—often teams use a hybrid approach (managed for experimentation, self-hosted for steady-state production).
