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Shing Lyu
Disclaimer: This content reflects my personal opinions, not those of any organizations I am or have been affiliated with. Code samples are provided for illustration purposes only, use with caution and test thoroughly before deployment.
While working on my recent exploration of Rust on Google Cloud Run, I was impressed by how seamless and developer-friendly the experience turned out to be. The container-based approach provided more flexibility and better tooling integration than I expected, sparking my interest in Google Cloud’s broader ecosystem and architectural patterns.
This hands-on experience led me to consider pursuing the Professional Cloud Architect certification. As I began researching study materials and exam guides, I discovered that Google Cloud was releasing an updated version of the certification exam in October 2025. Rather than rush into the old version, I decided to wait and understand what changes were coming. The updates include new AI-focused content and formal incorporation of the Well-Architected Framework principles.
I was really into Raycast. The global hotkeys, the instant app launching, the clipboard management – it transformed how I worked on my Mac. But I really don’t want to use their AI features. For the AI features it provides, I prefer using my own LLM provider. But I found out it’s quite difficult to completely turn it off. With sensitive data flowing through my clipboard and workflows, I want absolute certainty that nothing leaves my machine. Also Raycast is always in the gray area for corporate IT, and I live in the constant fear of it being banned by my employer.
That’s when I discovered something surprising: I could rebuild almost everything I loved about Raycast using tools that were already on my Mac.
Multiple comments and messages pointed out that I hadn’t given Cloud Run a fair evaluation. They were right. I was so focused on function-as-a-service models that I didn’t appreciate what GCP was actually offering with their “serverless containers” approach.
I had wrongly assumed that “containers” meant always-on hosts, but Cloud Run actually uses a microVM security model that provides strong isolation while still billing you only for the time your function runs - just like other serverless platforms.
So I did what any curious engineer should do: I went back and gave it another try.
And honestly? I was pleasantly surprised. Cloud Run’s container-first approach actually works brilliantly with Rust, and the developer experience is surprisingly smooth once you understand the optimization patterns.
When you’re choosing a cloud platform for your next Rust serverless project, the landscape can be confusing. Each of the big three cloud providers—AWS, Azure, and Google Cloud Platform—offers different levels of maturity and support for Rust in serverless environments. I’ve spent some time experimenting with two platforms (more on why I’m missing one later), and the differences are more significant than you might expect.
Why does this comparison matter? Rust brings compelling advantages to serverless computing: it runs with low resource requirements and delivers high performance, which translates to improved performance and lower cloud costs. Additionally, Rust eliminates many bugs during compile time, so your code is less like a ticking time bomb that will break during runtime. This means less late night on-call madness and happier developers.
What I Mean by “Serverless”
Before diving into the comparison, let me clarify what I mean by “serverless” in this context. Many cloud providers give vague definitions, so here’s my own practical definition that guided this evaluation.
“Sorry, but that just won’t work for our organization.”
Ever been in a customer meeting where the conversation seems to hit a brick wall? As a solution architect, you pitch your best cloud strategy, only to be met with a polite—sometimes not-so-polite—rejection. Most folks would pack up and move on. But what if this is the moment you should lean in and ask… why?