Hi, in this video we are looking at
how to explore and deploy the many models
Foundry have to offer, and understand the different
deployment types, as it can be quite confusing
to be honest.
I will show you how to deploy the
models you have available, how to get access
to restricted models and how to request more
quota, so let's get started.
If we look at the model catalogue, you
will see there are over 10,000 LLMs
models to choose from, but in real life,
unless you have a very niche scenario, you
will only be dealing with a handful of
the model providers and the model types.
Foundry's most known models come from OpenAI, Anthropic,
XAI, Mistral and DeepSeek.
The only big brand missing is Google Gemini,
but that is to be expected, as Google
and Microsoft are competitors in both the cloud
hosting and the AI space.
Microsoft themselves also have a few models on
their own, but nothing revolutionary.
There's one special model called the model router.
It as such is not a model, but
instead a collection of models that based on
the question, can choose simpler or advanced models
on the fly.
I had a mixed result with this, so
I tend to not use it personally, but
it's an interesting idea and behind the scenes,
you can go in and choose which models
it have available to route to.
Not all models are made for the same
purpose.
Some do text, some do images, some do
embeddings and some do speech, so it's a
good thing to go in and check the
capabilities of each model as you go.
It's also not all models that work with
the agents and workflow services, and since those
are the ones we are going to focus
on in this series, we will tend to
stick with these models as you see on
the screen.
Looking at a specific model, a description is
there and a link to the pricing.
While this screen is okay, I tend to
instead go to the provider's own specific site
as it gives more details and capabilities.
Here is, for example, what GPT 5 have,
and we can see features and pricing directly.
I normally only use models that can do
at least tool calling or function calling, as
it's some kind called, and structured output, as
I tend to use these features all the
time.
Beyond that, I look for at least an
embedding model for VectorStore, and then I choose
non-reasoning chat models and reasoning chat models
for the more advanced workloads.
These are my go-to models as of
December 2025, where GPT 4.1 and 4.1-mini
is my non-reasoning, 5.1
and 5.1 Mini is for reasoning, and
TextEmbedding 3.0 Small is for embeddings.
I am slowly getting rid of GPT 4.1
as it's quite pricey compared to how
old it is, but it's really nice when
you just need to set up a model
without needing to configure all the reasoning settings
and so on.
Now let's dive into how to deploy a
model, and you do that by clicking the
model in the details page, and on the
top, choosing the deploy button.
When you press that, there's a default settings
and a custom settings.
I will very much recommend you choose custom
settings, because if you just press default, it
just creates the model with no questions asked,
so it's better to go with the custom
model, in my opinion.
What you see is this sidebar that will
pop up next to the deploy, where you
give a deployment name, and this is special
for Azure in that you can choose your
own name for the deployment, while, for example,
in OpenAI, if you need to reference GPT
5.1, you need to give this name,
but you could call this my model if
you want to.
But I recommend that you don't change these
names, because these are the ones the different
providers show in their spec sheets and so
on.
Next, you choose a deployment type, and there
are four different types.
There's something called global standards, meaning that you
get the cheapest pricing, the pricing you normally
see on the spec sheets, but if you
can't rely on that, every call you do
will stay within the Azure region that you
have chosen.
For a little more price, you can do
data zone standard, which is within the Azure
region's data zone.
And then there is provisioned version of this,
where you get more reliability on your models,
but you pay by the hour for each
of them, while global standard and data zone
standard, you only pay when you use tokens.
Beyond that, you can choose a model version.
There's often only one, but some of the
older ones have older versions, and then some
upgrade policies that it will automatically go to
the newest version when they become available.
Then you choose your tokens per minute, and
you might feel that you don't have enough
tokens per minute, and if you don't, you
need to go with quotas and raise them,
as we'll talk about in a little bit.
The final thing is guardrails.
There's the default, the default v2, and you
can make your own guardrails as well, which
we'll cover in a later video.
So once you do this, you deploy it,
and you end up with a model.
Certain models you can't press the deploy button
on, and that is because some models are
behind registration, meaning that you need to, it's
often models that are under heavy use or
very resource intensive, because Microsoft in general, like
everyone else, have a hard time keeping up
with providing servers for all this load.
So some of them are behind a request
access, and when you press request access, you
are taken to a website where you fill
out a form.
You need to fill out your address details,
your subscription ID, and why you need access
to the model, and so on.
And it's a bit of a lottery, to
be honest.
Sometimes I have received after requesting access, a
day after I get access, some I never
get an answer back.
And even if I'm on my private system,
or I work with a company that have
dedicated Microsoft contacts and so on.
The contacts I've spoken to is, say it's
a come first, come serve model, which I
don't believe when a Fortune 500 company comes
along, they will probably push them up the
length.
I've also seen that sometimes I need to
request, not here for a month, request again,
and then I suddenly get it.
So it's not like it always works.
If you don't hear something in a couple
of weeks, try and request again.
That is my recommendation.
Or at least speak to some Microsoft contacts
that you have.
But it is a real lottery when we
have so few resources at the moment.
Once you have your models, you can go
to the build tab and then in the
model section here, it will list your default
different models.
And if you press them, you go into
the playground, but also go to the details
where you can edit.
So you can change the number of tokens
and so on.
You can delete your model, not that it
costs you anything just by having it around.
And again, if you find that you use
a model and you constantly run out of
tokens, you might want to request a quota.
And a quota is, again, a website that
you go to, fill out a form, tell
why do you need it, how many thousands
of tokens do you need instead.
And again, this is also a lottery.
I have tried the worst, trying to get
more tokens for one of the models back
in January and took three months before we
got it.
But I've also tried going here and the
next day having many more tokens than I
actually requested.
So again, go out early getting the quota
if you need to go live at some
point and ask for a lot because it
can be really, really difficult.
And I have scenarios right now where we
need to go to less capable models because
we simply cannot get the quota for the
high ones.
But that is everything about models and model
deployment.
See you in the next one.