Deploying Agents: AWS vs Azure vs GCP (Real-World Decision Guide) Agentic AI

Deploying agentic systems on the cloud isn’t about “which cloud is best.”

It’s about constraints.

In this video, I break down AWS vs Azure vs GCP for deploying production agents — based on real architectures, real costs, and real trade-offs.

If you're preparing for interviews and want structured breakdowns like this, I’ve built a focused playbook for experienced engineers.

https://learn.manifoldailearning.com/services/agentic-interview

Download Production Patterns and Checklist:
https://community.nachiketh.in

📅 Next Agentic AI Bootcamp cohort:
https://bootcamp.nachiketh.in
I’ve deployed agent systems across all three clouds:
• Startups on GCP
• Enterprises on Azure
• Scale-ups on AWS

Here’s what we compare in detail:
• Agent compute (Lambda vs Functions vs Cloud Run)
• LLM access (Bedrock vs Azure OpenAI vs Vertex AI)
• Cost breakdown using the SAME agent system
• Storage, vector DBs, orchestration, monitoring
• When each cloud actually makes sense
• When migration is worth it — and when it’s a mistake
• Why multi-cloud usually adds 20–30% operational overhead

Key takeaway:
The clouds are ~80% similar.
Your constraints matter more than cloud choice.

If you’re deploying agents in production — this video will save you months of wrong decisions.

👉 Full production deployment patterns inside
📅 Next Agentic AI Bootcamp cohort:
https://bootcamp.nachiketh.in

Download Production Patterns and Checklist:
https://community.nachiketh.in

Видео Deploying Agents: AWS vs Azure vs GCP (Real-World Decision Guide) Agentic AI канала Manifold AI Learning