Are you tired of spending time with

serving infrastructure when you just

want to deploy an open model? What if

you could go from model weights to a

scalable production ready API quickly?

In this new series, we are going to

answer this question and show you how to

serve open models on Vert.Exi. So, let's

get started. Hi everyone, my name is

Ivan Ardini and welcome to this first

video in our developers guide to serving

open models on Vert.exi. XAI. In this

series, we are going to provide a

complete road map with practical code

for every serving option on Vert.Ex AI.

We will start with simplest serverless

APIs, cover self-deployed model, and go

all the way to high performance custom

containers using VLMs both on GPUs and

TPUs, and even show you how to run

benchmarks. But before we dive into the

how, we need to understand the what and

the why. Today's goal is to give you a

clear decision framework. And by the end

of this video, you will have a map to

navigate all the serving options so you

can confidently choose the perfect

strategy for your project. So you need

to serve an open model. The first thing

to ask is how much control do you need

versus how much simplicity do you want.

This decision tree will be our guide for

the entire series. We will walk through

each path today at the eye level and in

the future videos we will deep dive into

each of them with hands on code. If your

goal is um maximizing simplicity maybe

for wring prototyping or if you just

want don't want to touch infrastructure

then the full manage path is for you.

This is vertex AI model as a service or

mass. We provide popular models as a

serverless and pay as you go APIs. You

just find the model, enable the API, and

then you get an endpoint to start eating

immediately. The tradeoff is less

control, but you get speed to value and

this is imbidable. This will be the

focus of our first hands-on video in

this series. Now, what if mass it's a

bit too restrictive? You want a balance

between easy to use and flexibility. And

so, the singleclick deployment path is

your sweet spot. With model garden

self-deploy models, you can choose from

a huge list of created open open models,

but the key difference is that you

choose the hardware. This gives you

direct control over performance and

cost. So, it's a fantastic alternative

and we will dedicate a future episode to

showing you exactly how to configure and

deploy these models. Finally, for those

who need control performance or just

fine-tune a model and want to deploy it,

your path leads to containerbased

serving. Here we have two powerful

option. First, using pre-built optimized

containers that leverage backends like

VLM or SG lang. You get a great

performance without building the

container yourself. The trade-off here

is the number of deployment parameters

that you can tweak which is limited to

what those container expose. That's why

for the full flexibility and control,

you can serve with your own custom

container. This is where you can package

any model, use any framework and bake in

any custom logic you need. This approach

offer total control and later we will

have videos in this series showing you

how to build these containers from

scratch from both GPUs and TPUs. So

that's our map. These are the core

strategy for serving open models on

Vert.exai. At this point, you have a

better understanding of all the

landscape of options. Remember, the goal

isn't to find the single best way, but

the best way for your project's needs.

And this framework is your guide for the

rest of our journey together. Now, you

have the map and in our next episode, we

will start exploring and getting

hands-on. We will start with the

simplest path, model as a service. We

will walk you through step by step how

to find and enable and call a serverless

open model API for your application. If

you don't want to miss that or the rest

of the series where we will cover self-

deployment, custom VLM containers and

more, don't forget to subscribe to the

Google Cloud Developer channel and turn

on notifications and let me know if you

have comment which uh part of this

series you are most excited about.

Connect with me on social media and

until next time, happy building.