Video summary
Before enterprises commit to large-scale, long-term AI inference deployments, they must critically evaluate their infrastructure against essential cloud buying criteria, with portability being a primary concern. Organizations should avoid becoming locked into a single vendor's technology stack and instead prioritize flexibility in where applications are deployed, ideally utilizing cloud-native technologies like Kubernetes. This approach ensures that companies can expand into new markets with low friction without facing penalties for moving data or being restricted by proprietary infrastructure limitations.
A crucial aspect of this strategy is addressing elasticity and scalability, as many applications perform well during initial demos but struggle significantly when user bases grow from dozens to thousands or even tens of billions. Enterprises must design architectures that allow them to scale up dynamically rather than provisioning massive capacity upfront, ensuring they can handle sudden surges in demand without performance degradation. This capability is vital for maintaining operational efficiency as the application becomes wildly successful and reaches a much wider audience.
Furthermore, AI should be viewed fundamentally as a data and context management problem rather than just a computational one. It is essential to ensure that the correct context is loaded into memory at the right time and remains accessible regardless of where the application runs. Companies must avoid tying themselves to storage or database infrastructures that are only accessible through a single provider, thereby preserving the ability to manage data contexts effectively across different environments.
Finally, performance considerations such as latency and cost management are paramount for sustainable growth. While controlling costs is important to prevent uncontrolled spending on token budgets, it must be balanced against strict latency targets required as the system scales. Leaders need to anticipate how their infrastructure will perform when moving from a single-server deployment to a widely adopted solution, ensuring that performance goals are met even under heavy load and across diverse geographic locations.
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Before enterprises commit to large scale
long-term inference deployments, what
should they be expecting and demanding
from their AI infrastructure?
>> It's an important uh question. And so
like generally I think sort of some of
the uh kind of
cloud buying criteria are as important
here. And so think think carefully about
sort of portability.
um think about like how how you can
actually um have uh flexibility around
where you deploy uh applications. So you
don't want to tie into one vendor's um
technology stack running on top of um uh
cloudnative technologies like Kubernetes
obviously uh important that gives you
flexibility around where where you
deploy um and then making sure that
you've got um uh elasticity. um think
about how it's going to scale up. Um
often a lot of applications work great
for that demo and then as soon as you
got a 100 users, a thousand users,
10,000 users, it starts to look very
very different. And eventually when it's
wildly successful and you got tens of
billions of users, um that's when things
can get really challenging. And so think
about how can you have um that
architecture that will uh scale. So you
don't want to be provisioning a huge
amount of capacity up front, but you
want to have flexibility to grow. Um you
want to have the ability to expand into
uh to new markets. Um and you want to be
able to do that with low friction. You
don't want to be tied into um one
infrastructure that's going to give you
penalties for moving the data. Um so
again, a lot of the way I think about
this is AI is really a data problem. Um
it's it's a context management problem.
It's making sure you got the right
context at the right time loaded into
memory. Um, but having that um available
to wherever you want to run uh the
application is super important. So you
don't want to tie into I don't know a
database or a storage infrastructure
that can only be accessed through uh
through one uh one uh provider. Um and
then I think um last piece is think
about the the performance. We talked a
lot here about latency. Um cost is an
important dimension. A lot of people
think about like managing costs. A lot
of uh horror stories uh kind of going
around at the moment about people
blowing their entire token budgets uh in
a few days with kind of uncontrolled
costs. Obviously that's uh that's an
important factor. But thinking about the
performance um budget that you have,
what are the latency targets that you
need to hit as scales out and how are
you going to achieve that? um because a
lot of people um are starting again uh
just simple deployment one one location
maybe running on a kind of a local
machine or just on on a single server
everything works great uh when it's one
user how's it going to work when it's uh
much uh more um widely adopted that's uh
kind of a key area to think about how do
you scale out um to achieve that uh
performance