7 ways networking powers your AI workloads on Google Cloud

When we talk about artificial intelligence (AI), we often focus on the models, the powerful TPUs and GPUs, and the massive datasets. But behind the scenes, there's an unsung hero making it all possible: networking. While it's often abstracted away, networking is the crucial connective tissue that enables your AI workloads to function efficiently, securely, and at scale.

In this post, we explore seven key ways networking interacts with your AI workloads on Google Cloud, from accessing public APIs to enabling next-generation, AI-driven network operations.

#1 - Securely accessing AI APIs

Many of the powerful AI models available today, like Gemini on Vertex AI, are accessed via public APIs. When you make a call to an endpoint like *-aiplatform.googleapis.com, you're dependent on a reliable network connection. To gain access these endpoints require proper authentication. This ensures that only authorized users and applications can access these powerful ...