One last topic we have to cover before we get started is that there are technically multiple Gemini
APIs.
There's two different ways that we can write code to talk to and communicate with the models.
This is straight from the Google documentation.
We have the Google I Gemini API and the Google Cloud Vertex AI Gemini API.
It's a little bit confusing.
The naming doesn't help, but basically on the left side we have a newer product that Google has released
that goes along with this thing called Google AI studio that we spend a lot of time covering in this
course.
It is completely free, and it's oriented towards developers and people who want to use and build as
soon as possible with the Gemini APIs, without having to get a Google Cloud account, without having
to have a credit card, without having to deal with all the authentication stuff that goes along with
Google Cloud vertex AI.
On the other hand, we have Google Cloud Vertex AI, which has its own Gemini APIs, which is a paid
service.
It's tied to a Google Cloud account, which means you have to have a credit card.
Got to have sign the billing agreement.
Um, the biggest distinction here, aside from the payment structure and how that works, is that Google
Cloud and Vertex AI is enterprise oriented.
It's all about scalability and it's oriented towards large operations.
Google I, Gemini API and the Google AI studio sounds like just complete word salad when I say those
rapidly, but what we have on the left here is the friendlier, um, more accessible way to work with
Gemini.
This is going to be a bit of a theme throughout the course.
I'll revisit this conversation in this particular video, but we start with Google AI studio, which
is this great visual interface that helps you build iterate test prompts.
It's a really cool tool, whether you're a developer or not.
Then we can get an API key to use this particular version of the API, the Google Gemini APIs that Google
itself calls the fastest way to start building AI later.
We talk about vertex, the enterprise AI platform that's tied to Google Cloud.
Now it does a whole bunch of other stuff, right?
You can train your own, your own models on here.
You can fine tune models.
Um, there's a lot of things that you can do with vertex AI.
It's integrated with Google Cloud.
You can deploy models.
It's enterprise grade, right?
It's made for scalability.
But of course, the big catch is that it's kind of a pain to get started and get set up with.
It's not very user friendly if you're just a solo developer, right.
It's kind of oriented towards teams and large organizations, but we do cover it.
So I'm not saying anything bad about vertex.
It's just not a great place to start.
And I think Google acknowledges that.
Right.
They in their own documentation say if you are new to Gemini, using Google AI studio is the fastest
way to get started if things start to mature.
If you need a larger platform, you need to deploy your own applications.
You need an end to end solution.
Well, maybe come to Google Cloud vertex AI.
This is more from Google the vertex AI Gemini API, and the Google AI Gemini API both let you incorporate
Gemini into your apps.
Choose the platform depending on your goals.
Right vertex is for scale, it's for enterprise.
It's feature rich.
There's tech support built in.
There's enterprise security, but you have to pay kind of a lot.
And it's really oriented towards large scale apps.
If you're a developer student hobbyist, you want to use the Gemini APIs as soon as possible.
You want to use the Google AI Gemini API, which is what we cover first.
It's very confusing, right?
The names are just too similar, but there's two products.
Uh, the you can think of the Google AI Gemini API as something pretty similar to the GPT family of
APIs, and how those work where you just get an API key.
It's that simple.
When we get to the vertex AI content towards the end of the course, it's a little bit trickier to get
authenticated.
Not to mention that you have to pay.
So I just want to get that out of the way because it's going to be something we revisit throughout the
course.
It is confusing.
Google has multiple pages of their documentation, comparing the two and telling you how to migrate
from one to the other, which one you should start with.
Honestly, if I had to guess, I know that vertex AI has been around for quite a while at this point,
and then Google saw that they needed something that was a bit friendlier and easier to get into and
free.
And that's what led to Google AI studio and the Google AI Gemini API.
What a mouthful.
So we're going to cover both.
That's what you should know.
You should know that they exist.
There's a distinction both allow us to work with and send requests to Gemini.
But we authenticate differently.
And the payment structure is different.
And if you're watching this as someone who's not a developer, you probably have no idea what I'm talking
about.
That's okay.
We're going to start nice and easy with barred and prompt engineering.
Let's get a taste of Gemini.