Prompt
Can I move from Runpod to CoreWeave without downtime?
Latest observation
Oct 11, 2026
OpenAI APIWeb search: off
Yes — usually, but not by “moving” a running GPU workload in place. In practice, you do a blue/green cutover: bring up the same service on CoreWeave, warm it up, then shift traffic from Runpod to CoreWeave gradually. If done well, users see little to no downtime.
What “without downtime” looks like
- For stateless APIs / inference endpoints: very feasible.
- For training jobs: not truly zero-downtime in the classic sense, but you can often resume from checkpoints with only a brief pause.
- For stateful services / databases: possible, but requires replication and careful failover planning.
Typical migration approach
- Provision CoreWeave infrastructure to match or exceed Runpod.
- Deploy the same container/image/config on CoreWeave.
- Sync data/models/checkpoints ahead of time.
- Health-check and warm up the CoreWeave deployment.
- Shift traffic gradually:
- DNS weighted routing, or
- load balancer / API gateway, or
- application-level failover
- Keep Runpod live until CoreWeave is fully verified.
- Decommission Runpod after a soak period.
Things that determine whether downtime is avoidable
- Whether your app is stateless
- How big your model/data artifacts are
- Whether you can replicate persistent volumes
- Your traffic routing setup
- Whether you need GPU-specific warmup time
- If your workload maintains in-memory state that can’t be transferred
Common gotchas
- Cold start latency on GPUs or model loading
- Different storage semantics between providers
- Networking changes (private IPs, security groups, ingress)
- CUDA / driver / image compatibility
- Spot/preemptible capacity differences
Bottom line
- Yes, you can usually migrate with effectively no downtime if you use a staged cutover.
- No, you typically can’t “hot move” the same live GPU instance directly from Runpod to CoreWeave.
If you want, I can help you design a zero-downtime migration plan for your specific setup (API vs training, Kubernetes vs containers, persistent storage, etc.).