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Deploying Agentic AI on GCP: Building Data-Native Intelligent Agents
If we look at the cloud landscape today, the distinctions are becoming clear. If AWS is the infrastructure powerhouse and Azure is the hub for enterprise governance, Google Cloud Platform (GCP) has staked its claim as the home for data-native, analytics-heavy, and ML-forward agentic systems.
GCP’s ecosystem, specifically the combination of Vertex AI, BigQuery, and Cloud Run, is uniquely positioned for agents that need to process massive amounts of context, perform real-time analytics, and integrate tightly with machine learning pipelines.
This guide breaks down how to architect and deploy these systems on Google Cloud, moving from model se…
5 min readJust now
–
Press enter or click to view image in full size
Deploying Agentic AI on GCP: Building Data-Native Intelligent Agents
If we look at the cloud landscape today, the distinctions are becoming clear. If AWS is the infrastructure powerhouse and Azure is the hub for enterprise governance, Google Cloud Platform (GCP) has staked its claim as the home for data-native, analytics-heavy, and ML-forward agentic systems.
GCP’s ecosystem, specifically the combination of Vertex AI, BigQuery, and Cloud Run, is uniquely positioned for agents that need to process massive amounts of context, perform real-time analytics, and integrate tightly with machine learning pipelines.
This guide breaks down how to architect and deploy these systems on Google Cloud, moving from model selection to production readiness.
Why GCP is Uniquely Strong for Agents
While you can build an agent on any cloud, Google offers a specific set of advantages for data-intensive workloads.
1. Vertex AI: The Unified Platform
Vertex AI isn’t just a model garden; it is a cohesive platform that tightly integrates every…