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Lab 31: Choosing the Right Deployment Strategy Challenge

Goal​

In this lab, you will apply the decision framework from the theory section to a series of real-world scenarios. This is a conceptual exercise to help you develop the strategic thinking needed to choose the right deployment path for your agents. There is no coding in this lab.

The Scenarios​

For each of the following scenarios, read the description and decide which deployment platform is the best fit: Cloud Run, Agent Engine, GKE, or Custom Server on Cloud Run.

Be prepared to justify your choice based on the key requirements of each scenario.

Scenario 1: The Startup MVP​

  • Company: A small, venture-backed startup.
  • Product: A new AI-powered customer service chatbot for e-commerce sites.
  • Key Requirements:
    • Get a working version deployed for a pilot customer by the end of the week.
    • Keep infrastructure costs as low as possible.
    • The development team has minimal DevOps experience.
    • Standard security (HTTPS, basic protection) is sufficient for now.

Your Task: Which platform should they choose and why?

Scenario 2: The Government Contractor​

  • Company: A large defense and technology contractor.
  • Product: An internal agent that helps employees search through sensitive but unclassified government compliance documents.
  • Key Requirements:
    • The system must be FedRAMP compliant.
    • All access must be strictly controlled and auditable.
    • The infrastructure must be fully managed to reduce the internal operational burden.

Your Task: Which platform is the only viable choice here, and why?

Scenario 3: The FinTech Enterprise​

  • Company: A large financial services company with a mature IT department.
  • Product: A complex system of microservice agents for financial analysis that need to communicate with each other over a private network.
  • Key Requirements:
    • The entire system must be deployed within the company's existing Kubernetes ecosystem.
    • The security team requires fine-grained network policies to control traffic between agent services.
    • Some analysis agents require access to GPU nodes for performance.

Your Task: Which platform should they use, and what are the trade-offs?

Scenario 4: The University Integration​

  • Company: A university's IT department.
  • Product: An agent that allows students to ask questions about course availability.
  • Key Requirements:
    • The agent must authenticate users against the university's central LDAP directory.
    • The platform should still be serverless and cost-effective.

Your Task: Which hybrid approach is necessary here, and why?

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Lab Summary​

You have now practiced applying a strategic framework to real-world deployment decisions. You have learned to:

  • Analyze business and technical requirements to choose a deployment platform.
  • Prioritize factors like speed, cost, compliance, and control.
  • Understand the specific use cases for Cloud Run, Agent Engine, GKE, and custom server deployments.

Check the lab-solution.md to see the recommended answers for each scenario.

Self-Reflection Questions​

  • For the Startup MVP, what are the potential downsides of choosing Cloud Run? At what point might they need to consider migrating to a different platform like GKE?
  • Why is a "platform-first" security model, where you rely on the deployment environment for features like authentication and DDoS protection, generally a better approach than trying to build these features into your agent application code?
  • If a new compliance requirement (e.g., PCI for handling credit card data) was introduced, how would that influence your choice of deployment platform?

🕵️ Hidden Solution 🕵️​

Looking for the solution? Here's a hint (Base64 decode me): L2RvYy1hZGstdHJhaW5pbmcvbW9kdWxlMzEtcHJvZHVjdGlvbi1kZXBsb3ltZW50LXN0cmF0ZWdpZXMvbGFiLXNvbHV0aW9u

The direct link is: Lab Solution