Container platform decisions
Useful for
Introduction
For a POC, the decision between Azure Container Apps and AKS does not have to be final. The important thing is to get the product running in a way that teaches the right habits: build an image, deploy it repeatably and avoid configuring servers by hand.
Knowledge scope
This is common CTO knowledge. It applies beyond the startup journey, but the public playbook places it where it usually becomes important for an early-stage company.
Why it matters
Platform decisions become expensive when complexity creeps in unnoticed. A simple container route is often right early, but multiple services, scheduled jobs, deployment coordination and networking requirements can eventually make a stronger platform model worthwhile.
How it fits the playbook
This reference supports the Company Ready -> POC Started stage of the startup CTO playbook. It gives the public context for the decision without exposing the deeper assessment method behind the agentic operating model.
Design considerations
- Keep the SPA frontend separate and simple, commonly served by nginx or equivalent static hosting.
- Use a single backend container shape where possible, with commands or entrypoints for different worker roles.
- Delay AKS until orchestration complexity, deployment coordination or operational control justifies it.
- Capture the point where Container Apps stops being simpler than a managed Kubernetes approach.
- Make platform decisions explicit before Production promises depend on them.
What good looks like
The company can explain why the current container platform is simple enough for today and what signals would justify moving to a more capable platform.
How Brokenhouse helps
Turn this into a practical plan.
I help technology teams turn this guidance into decisions, implementation plans, governance evidence and production-ready operating models.
Talk through your situationNext guidance
Related decisions to work through
Is the company ready?
The first few months of a software business are not just about building the product. They are about creating the conditions that allow the product to be built, deployed, governed and supported without the company tripping over its own foundations.
Agentic software delivery governance
Agents used by the delivery team need a different governance model from AI models embedded in the product. Delivery agents may not sit in the customer-facing service, but they can still read code, write code, inspect logs, summarise documents, generate infrastructure changes or draft customer-facing material.
AI model governance
AI models used by the product need their own governance model. They sit close to customer workflows, user data, automatic processing and contractual promises, so they need stronger control than delivery agents used internally.