Anycloud vs SkyPilot for spot GPU training
Compare container-based spot training, checkpoint recovery, and workflow requirements before choosing Anycloud or SkyPilot.
Cloud workload answers
Direct answers about cloud containers — choosing tools, running GPU Jobs, hosting HTTP Services, and keeping development workspaces available.
Anycloud vs Azure Container Apps for HTTP services
Choose between an Anycloud Service on selected compute and Azure Container Apps with managed ingress, revisions, and autoscaling.
Anycloud vs DevPod for persistent development VMs
Compare a published development container on an Anycloud VM with DevPod workspaces defined through devcontainer.json.
How do you compare GPU prices across cloud providers?
Compare GPU jobs by completed-run cost, not only hourly price. Region, quota, capacity, spot risk, storage, and startup time all change the real number.
Can you train on spot GPUs without losing progress?
Spot GPUs can work for training if the job writes durable checkpoints and can restart cleanly after the VM is reclaimed.
How do you run GPU jobs without Kubernetes?
For many batch workloads, running a Docker image on a GPU VM or using a managed batch service or job runner is simpler than operating a GPU cluster.
Should I use spot, on-demand, or reserved GPUs?
Use spot for resumable work, on-demand while usage is uncertain, and Capacity Blocks when a predictable paid GPU window matters.
Can one Docker image run across AWS, Azure, GCP, and Lambda?
One image can work across GPU clouds when it targets linux/amd64, keeps cloud-specific settings out of the image, and matches driver/runtime constraints.
Do you need a custom Docker image for GPU training?
Often no — public PyTorch and CUDA images cover most training jobs. Build your own only when dependencies are not available off the shelf.