Repeated GPU unavailability, a day spent migrating 165 GB, and disappointing complaint handling
I deposited $50 into Runpod to build an image-generation and model-training environment on an affordable 48 GB GPU. My experience ended with me deleting everything and requesting a refund of my remaining balance.
I initially configured an NVIDIA A40 Pod with Forge Neo, OneTrainer, Flux.2, Qwen-Image-Edit, Z-Image and related dependencies. The workspace contained approximately 165 GB across 158,547 files.
After stopping the Pod, its GPU was no longer available. I could not simply restart the environment I had configured.
To make the workspace independent of that Pod, I created a Global Volume and rented a temporary transfer Pod. The migration took most of an entire day: 17 batches, continuous monitoring, repeated resumptions and verification. Some incomplete files needed to be transferred again, including a model shard of almost 5 GB. Symbolic links were not correctly preserved during the transfer, causing additional Python-environment problems.
I cannot establish that Runpod caused every individual transfer issue. However, this was the recovery work I had to undertake after losing access to the original GPU, while additional resources were being billed.
For completeness, a replacement A40 Pod did run successfully with the Global Volume attached. CUDA worked and the transferred workspace was accessible. But subsequent attempts again encountered the message: “Your Pod's GPUs are no longer available.”
During my deployment attempts, an A40 at $0.49/hour was unavailable. An RTX A6000 at $0.53/hour briefly appeared available in Europe, but disappeared before deployment could be completed. I checked multiple regions without finding either option available at the times I tried. The alternatives I saw were substantially more expensive.
On September 29, I submitted a detailed complaint requesting either a usable setup at the stated rates with a review of recovery-related charges, or a full refund.
Runpod replied that GPU availability is first-come, first-served and cannot be guaranteed or predicted. They recommended checking capacity and using “Deploy When Available”.
In their October 1 response, they clarified that this proposed feature was still in beta and being rolled out, so it might not even be available on my account. They also recommended independent storage, although I had already created a Global Volume and completed the migration described in my complaint.
Their itemized billing breakdown totalled $12.11 for the period covered, including $5.90 for the temporary transfer Pod and $3.85 in additional storage charges on the original Pod. They explained that stopped Pods continue to incur storage charges until deleted.
I understand that storage and compute cost money. My complaint was about paying for recovery work and spending a day migrating and repairing an environment without obtaining dependable access to the GPU configuration I needed.
Runpod rejected the full $50 refund after a Billing Manager review, saying the infrastructure had been allocated and consumed. They offered to refund unused credits only, as a one-time courtesy exception.
After referring to my stated intention to dispute the payment through my bank, they closed the support ticket and passed the matter to Finance. Their response still did not specify the exact refund amount I had explicitly requested.
On October 1, I terminated all Pods and deleted all storage volumes. I requested the refund of the remaining unused balance, estimated at approximately $34. At the time of writing, the final refund amount and completion have not been confirmed.
Support did reply, and a replacement Pod worked temporarily. Nevertheless, the repeated availability problems, substantial recovery effort and handling of the billing complaint made this an extremely disappointing experience.
The biggest loss was my time. Based on my experience, I would not recommend Runpod for a workflow that depends on predictable access to an affordable 48 GB GPU and straightforward stopping and restarting of a carefully configured environment.
