I Built an Azure Functions Hosting Plan Advisor
A walkthrough of the decision model, the tradeoffs between hosting plans, and why I built this instead of writing another comparison post.
Every few months, the same question comes up on a project:
“Which Azure Functions hosting plan should we use?”
The answer is usually, “It depends.”
Microsoft’s documentation does a great job explaining the capabilities and tradeoffs of each hosting plan. The challenge is applying those tradeoffs to a real workload. That’s where teams often end up guessing.
So instead of writing another comparison article, I built a small decision advisor.
Try it here: Azure Functions Hosting Plan Advisor
What it does
The tool asks a handful of questions about your workload, including networking, execution patterns, cold start tolerance, operating system requirements, containers, and pricing preferences.
From there, it:
Scores all five Azure Functions hosting plans.
Recommends the best fit.
Explains why it made that recommendation.
Highlights the main tradeoffs.
Suggests alternative plans worth considering.
The goal isn’t to replace the documentation. It’s to make Microsoft’s decision guidance easier to apply.
Under the hood
The first version was a simple decision tree. It worked, but it had an obvious weakness: one early answer could outweigh everything else.
The current version uses a weighted scoring model instead. Every answer contributes to every hosting plan, producing a recommendation based on the overall workload rather than a single branching path.
The result is a recommendation that’s easier to explain and easier to challenge. All based on Microsoft official docs.
Why this exists
There are plenty of comparison tables online. What I wanted was a practical decision tool that helps answer:
Given my workload, which hosting plan should I actually choose?
That’s the problem this tool is trying to solve.
If you’ve deployed Azure Functions in production, I’d love your feedback. If the recommendation doesn’t match your experience, let me know. That’s exactly how the model improves.
That’s all for today, see you in the next one.