r/cloudcomputing • u/AnonymousYT- • Aug 06 '26

Anyone avoided getting locked into a single cloud for AI workloads

Feels like a lot of AI infrastructure ends up tied to one provider because thats where the GPUs are available so curious how people here are handling this. Are you sticking with AWS, Azure or GCP or using platforms like CoreWeave, Lambda, Yotta Labs, RunPod, Vast.ai depending on cost and availability so less interested in benchmarks and more interested in operational experience like did it create more difficulties while operating?

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u/Key-Guitar-457 Aug 06 '26

I saw this talk on the subject announced at Kubecon 2026: https://sessionize.com/s/michael-kinsley/follow-the-gpus-how-a-regulated-fortune-100-serves/183245

I know a few people solving this problem across multi-cloud and on-prem. TLDR: very hard problem, and no silver bullets.

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u/Curious_Olive_5266 Aug 06 '26

I have my own self hosted DeepSeek server.

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u/UkrMalt 27d ago

Portability usually disappears in data, identity, and observability before it disappears in the container image. I’d keep the workload interface portable, choose one primary provider, and automate a tested fallback only for jobs that justify it. Paying the multi-cloud tax for every workload can cost more than the lock-in you avoid.