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Agentic AI is moving from flashy demos to real production workloads: support bots that triage incidents, “copilot” tools for data engineers, self-healing pipelines, research assistants that orchestrate tools, and more. As soon as you move beyond a single LLM call into multi-step workflows, tools, and state, your cloud platform matters a lot more.
In this article, we’ll compare, contrast, and practically evaluate deploying agentic AI solutions on the three big clouds: AWS, Microsoft Azure, and Google Cloud Platform (GCP). We’ll focus on the things you actually run into in production:
- LLM and embedding choices
- Tooling and orchestration patterns
- Data + security integration
- MLOps / A…
Member-only story
9 min readJust now
–
Press enter or click to view image in full size
Agentic AI is moving from flashy demos to real production workloads: support bots that triage incidents, “copilot” tools for data engineers, self-healing pipelines, research assistants that orchestrate tools, and more. As soon as you move beyond a single LLM call into multi-step workflows, tools, and state, your cloud platform matters a lot more.
In this article, we’ll compare, contrast, and practically evaluate deploying agentic AI solutions on the three big clouds: AWS, Microsoft Azure, and Google Cloud Platform (GCP). We’ll focus on the things you actually run into in production:
- LLM and embedding choices
- Tooling and orchestration patterns
- Data + security integration
- MLOps / AIOps and observability
- Cost, governance, and “org reality”
What “Agentic AI” really means in the cloud
Before diving into vendors, let’s ground the term.
When I say agentic AI, I mean systems where an LLM (or set of LLMs):
- Has clear goals (e.g., “triage this pipeline failure and propose a fix”)
- Can call tools (APIs, databases, search, code…