Neoclouds like CoreWeave, Lambda, and Nebius are saving AI startups millions of dollars. But they introduce a new risk: Vendor Instability.

Unlike AWS (which is too big to fail), a specialized GPU provider is a startup. They could pivot. They could be acquired by a competitor. They could drastically raise prices once venture capital subsidies dry up. If you build your entire stack on "Nebius Proprietary APIs," you are trading AWS Lock-in (stable) for Neocloud Lock-in (risky).

To safely use cheap GPU compute, you must design your architecture with a pre-planned Exit Strategy available from Day 1.

1. The Golden Rule: Compute is Ephemeral, Data is Persistent

Treat the Neocloud as a "Compute Socket"—a place where you plug in, process data, and unplug. Never use it as a "Data Vault."

• Wrong: Storing your TB of training data on the Neocloud's block storage as the primary copy.

• Right: Storing your "Golden Copy" on a neutral, high-durability object store like AWS S3 or Cloudflare R2.

When you start a training run, your script should hydrate the Neocloud's local NVMe drives from the Golden Copy. When the run finishes, it must push the checkpoints back to the Golden Copy immediately. If the Neocloud vanishes overnight, your IP is safe.

2. Containerization is the "Universal Adapter"

Never rely on a provider-specific "Machine Image" (AMI equivalent). If you spend weeks configuring a specific Ubuntu environment on a Lambda Lab instance, you are locked in. You must build your own OCI-compliant Docker images.

• Include all CUDA drivers, Python dependencies, and system libraries.

• Push these images to a neutral registry (Docker Hub, GitHub Container Registry, or AWS ECR).

• Do not use the cloud provider's internal container registry.

3. Kubernetes as the API

The beauty of the modern AI stack is that Kubernetes has won. CoreWeave, Nebius, and Scaleway all offer Managed Kubernetes. If you define your infrastructure using standard K8s manifests (Deployments, StatefulSets, Ingress), you can migrate from CoreWeave to Scaleway by simply changing the kubeconfig and running kubectl apply.

Warning: Avoid using provider-specific Load Balancers or Ingress Controllers directly. Use an abstraction layer or a portable ingress like Traefik or Nginx that you manage yourself.

4. Abstracting the Storage Layer

Different clouds use different storage protocols (S3, GCS, Azure Blob). To avoid rewriting your code, use a storage abstraction tool like JuiceFS or MinIO. These tools allow your application to see a standard POSIX file system, while the tool handles the backend communication with whatever object store you are currently using. This decouples your code from the storage vendor's API.

Conclusion

Neoclouds are fantastic tools for cost optimization, but they require "Defensive Engineering." Build your stack assuming you will have to leave significantly faster than you arrived.