What's blocking me on Vertex AI isn't the model, it's opening an account

Recently I moved an agent I had on hand over to Vertex AI, and going through the docs was actually pretty smooth. The Gemini series is native, so you don't have to build your own inference service; AutoML for tabular data is also ready-made, and fraud detection-type work can run with a template; for deployment, I saw someone use the Harper framework and change three files to get an agent up. Technically there's nothing to fault.

What really took time was opening the account.

Overseas payment binding, identity verification, enterprise info verification—going through it layer by layer, at best a day or two, at worst you get stuck on the payment step trying over and over. From my testing, the most awkward part is the gap between individual developers and enterprise accounts: if you want to run a small-scale validation first, you have to get the whole set of qualifications in order first. The setup guides that come up in search are basically all about this, and few talk about the models. A unified ML platform, and what gets discussed most is how to pay money into it.

On platform capability I'd give it high marks—Gemini native, AutoML out of the box, clear agent deployment path, these are real things. Its barrier isn't the model, it's the account-opening process.

So my advice is, if you just want to try out Gemini's performance, go through AI Studio or the Gemini API line first, don't charge straight at the Vertex AI console. Once the model is chosen, you're going to production, and you need the whole MLOps setup, then come back and open this account—only then is that earlier hassle spent on the right thing.

In the end, this platform is made for people already running business in Google Cloud, not for people who want to dabble.