Fully Automated AI Inference on AWS, Azure, and Google Cloud with Pulumi

The process of deploying AI inference servers using Pulumi on cloud platforms like AWS, Azure, and Google Cloud is streamlined by treating infrastructure as code, allowing for reproducibility, version control, and easy teardown with a single command. Inspired by an Akamai project, the approach involves deploying a GPU instance with Ollama model serving, which maintains the model in GPU memory to optimize performance. By utilizing OpenID Connect (OIDC) for authentication, long-lived cloud keys are replaced with short-lived credentials, enhancing security. The deployment process is consistent across clouds, involving setting up a GPU virtual machine, a firewall, and a cloud-init script, while addressing cloud-specific nuances. The model is served via an HTTP API, and Pulumi's approach to cloud infrastructure management simplifies the setup, reducing reliance on static tokens and improving deployment efficiency. The emphasis is on ensuring the AI endpoint is secure and cost-effective, with best practices including scoping firewall rules and leveraging TLS or private networking for production environments.

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