Serving wrappers create GPU vendor lock-in

The real lock in is not the GPU, it is the serving wrapper around the GPU. RunPod asks teams to package inference as its own serverless endpoint format, while Modal wants Python functions and Replicate wants models packaged through Cog. For a company like Segmind, that means switching providers is not just moving containers, it is rewriting deployment logic, observability hooks, and scaling behavior that sit around the model itself.

• At Segmind, both inference and fine tuning run on RunPod serverless, and the team said a migration would likely take months. That shows the dependency is already in production workflow, not just in test environments.
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• The friction comes from product shape. RunPod uses Python handlers inside serverless endpoints with dashboard level controls for cards, logs, latency percentiles, cold starts, and GPU selection. Modal centers everything on Python decorators and remote function calls. Replicate centers custom deployment on Cog packaging.
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• This is why specialist GPU clouds are not fully commodity infrastructure. Segmind competes as a model API layer, but under the hood it depends on third party GPU platforms, and those platforms create stickiness through deployment format, monitoring surfaces, templates, and developer workflow rather than through raw compute alone.
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The next step in this market is tighter vertical integration. As RunPod, Modal, and others add more prebuilt endpoints, workflow tools, and IDE integrations, the winning platforms will look less like interchangeable GPU rentals and more like opinionated application clouds. That will make migration harder, and retention stronger, for teams that build directly on each platform's serving model.