Monetizing AI — Alvaro Morales, Orb

Channel: aiDotEngineer

Published at: 2025-07-23

YouTube video id: 6WQYLQB0odc

Source: https://www.youtube.com/watch?v=6WQYLQB0odc

[Music]
Hi everyone, my name is Alvo and I'm
excited to chat today with you all about
pricing strategies and how to think
about monetiz monetization
uh specifically around AI. This
conference has and continues to be
amazing where I'm walking down the hall
and seeing innovation everywhere. And an
idea that I think is really important is
for there to be a successful innovation
ecosystem. We also have have to think
about how we capture that value and
monetize effectively to reach the right
group of folks and make sure that this
becomes a self- sustaining and u
effective ecosystem.
I am the co-founder and CEO at Orb and
we specialize in billing for AI and SAS
companies and we've been immensely lucky
to partner with companies all across the
AI stack and we've been uh a small part
in helping some of these amazing
companies ship incredible product
experiences like Verscell's voter replet
agent and perplexities API. So with this
experience, the goal of my talk today is
to share some stories from the field and
bring forward uh some of this bleeding
edge uh experimentation and ideas that
are forming on the world of AI pricing.
And I think a good place to start is to
really think about what is unique and
complex about AI pricing specifically. I
think software pricing has always been a
little bit of an art and a science and
there's always been some considerations
around how to get it right. But there
are three things in particular that make
considerations around AI pricing perhaps
more challenging than they've been
before. Well, first of all, things are
changing dramatically quickly in terms
of model model and inference costs, in
terms of capabilities, in terms of new
products being launched. So, uh just the
the pace of this evolution makes it hard
to keep pace on the pricing side.
Second, these AI technologies create
some real pressures on margins and cogs.
So the thing that we could do maybe 10
years ago of like shipping our products
and we'll figure out how to price them
down the line is much harder to do when
that anthropic or open a open AI bill is
very steep and significant. And then
third amidst all this experimentation
exploration I think customers are really
really hungry to understand what is the
ROI that they are getting from from some
of these technologies. So this makes AI
pricing uniquely challenging and um this
is even greater so when we keep getting
blown away and surprised by the kind of
um adoption trends and just uptick that
a lot of these products have. When I say
that AI pricing is is challenging, I
really mean that it's kind of
challenging for everybody where um
notably we've seen even cases where uh
ChiBBD Pro at $200 turns turned out to
be a loss driver for the company. So I
come today to you with an idea that
perhaps as an industry we should not be
pricing on vibes and there is a
potentially better way to do this. So to
drive my point forward, I have come to
you with three simple frameworks and one
tool to share with you some ideas and
and and frameworks for how to think
about AI pricing and uh share with you a
tool we've built at orb that is really
helping some teams make the right
decisions around their monetization. So
well first and foremost like should you
monetize AI? Um and how should you think
about uh that uh that experience? So
over here um I have a kind of simple
framework uh that is one of my
favorites. It's it was created by the
folks at Simon Kutcher and it really
starts asking about this idea of how um
should you think about monetization?
Should it be a direct monetization
strategy where you are either selling
something as a standalone product or
charging it for it as an add-on? Or
should it be an indirect monetization
strategy where perhaps you're using it
to drive upsells into existing products
or tiers that you have or maybe even
bundling it for free because it's
incentivizing the right behavior you
want to see in your customers. So, let's
look at some examples. This go GitHub
co-pilot uh they launched it as a
separable monetizable add-on on top of
the base GitHub seat. An important
consideration here is is this this new
product is this new feature adding value
to everybody in your audience or just a
niche group of folks. Um and when it's
maybe not everybody with a GitHub seat
is going to get value out of this, maybe
an add-on monetization strategy is a
really good one.
Second second example here is notion uh
and notion AI. Notably they were first
to market in their category and
initially launched notion AI as an
add-on like GitHub copilot still is. But
recently they've bundled this into their
business and enterprise tiers to kind of
encourage um more adoption there. And as
part of that u they've done some price
increases on those tiers. So they're
they're finding ways to kind of
encourage the the adoption that they
want to see at those higher tiers.
Finally, this this is a little bit of a
more off the-beaten path example. A
month ago, Expedia launched a new AI
powered feature that lets you turn
Instagram reels into bookable trips. And
they're not charging for this. They have
like released this out for free.
They're doing this while swallowing
those like cost to serve and model
inference costs to power this capability
because likely they are hoping to see a
lot more bookings of travel as a result
of this capability. So again, this is an
example of cases where you might even
want to indirectly monetize where you're
not charging for this right away, but
you are incenting the behavior that then
will continue to fuel your revenue
growth.
Having explored like should you even
monetize this how do we actually do it
and I think one of the most important
considerations here is to make sure that
you are picking and selecting the
correct value metric so let's take the
space of AI agents where uh literally AI
agents are redefining the way that we
get value out of software where before
we measured the value of software by
getting a login into a web application
that we could like spend our days in now
we have AI agents that are out there
doing work for us or achieving
automation or achieving outcomes for us.
How do we price that? Um, I think what
we're seeing out in the field,
especially over the last six months, is
that there's a little bit of a spectrum
in terms of how tightly aligned to
discrete units of value or more closely
aligned to ROI do you want to be? And
there's sort of a spectrum between very
resource-based or tokenbased
monetization models, proxies of value
like steps in a workflow, maybe entire
workflows, perhaps even more direct
labor replacement type strategies where
just like you would hire a consultant
per hour, maybe you would hire an agent
per hour all the way to true
outcomebased pricing. Let's run through
some of these examples again. So here's
Versels V0ero. They have a token-based
model uh based on very granular
capabilities and I think this aligns
really closely with their audience with
a developer that I think really wants to
understand some of the capabilities that
they can drive over there. On the other
hand, here's Zapier. They uh deliver
automation at scale and they've gone
with a taskbased pricing strategy where
various tasks in in an entire workflow
or zap are the unit of value that you
end up paying for and you actually
subscribe to a certain number of tasks
per month or can go over if you are
exceeding that. And then finally there
is true outcomebased pricing. So this is
intercom's fin where the way Finn is
priced is it charges 99 cents per
successful customer support ticket
resolution where literally if the end
user at the end of that support
interaction says yes that answered my
query and I don't need to escalate to a
human support agent that's where Finn
charges for 99 cents. And if you've been
on LinkedIn, you've probably seen a
thing or two about outcomebased pricing
because I think it is very much actively
being discussed and actively being
explored. Maybe the hidden secret is
that outside of this customer experience
category, customer support space, it's
really hard to find u many examples of
this working really well quite yet. So
perhaps this is still kind of more on
the emerging side. And why is that?
Because ultimately customer and vendor
need to align on what the definition of
the outcome is and need to furthermore
be able to measure it in a somewhat
objective way. So when you're dealing
with a customer support ticket
interaction that's very clear and
simple, you can ask, hey, did this
resolve your question? And you can see
whether this needed to be escalated. But
whenever you're trying to proxy things
that are maybe more um creative or
perhaps more um you know less directly
tied to uh dollars and cents as as can
be measured by the cost that support
teams incur for businesses that's that's
been a little harder. So ultimately what
I find very inspiring and exciting about
outcomebased pricing is that it's not a
proxy for ROI. It's literally ROI. So I
think as an industry this year and and
and for many years thereafter I think
we're going to continue trying to find
ways to crack this code and find um the
right way to align outcomes and measure
outcomes such that um this aligns with
everybody.
So lastly having explored like a couple
of frameworks and ideas for how to price
uh I think what's really important is to
build a muscle around thinking about
pricing strategy as something that needs
continual evolution and experimentation.
Maybe 10 years ago when we were building
SAS, what we could do is like ship a
product, pick or guess a price point and
then spend, you know, a year, two years,
three years building and adding value to
that product and then we might realize
like, oh, you know, you know what, we
actually have to catch up to all this
value that we've created and and think
about doing a big large painful um price
increase. But nowadays with AI when um
you know new model drops can reduce cost
by 10x or increase cost by 10x literally
overnight like cloud 4 did I think this
uh kind of one two threeear cycle of
pricing iteration just doesn't cut it
anymore. So what we see is that the best
teams out there are able to find ways to
um really drive a lot of experiment
experimentation and continual evolution
in their pricing.
And as we've been talking about pricing
strategy, I think it's important to
remember that it all comes down to not
just the price point, but rather the
cohesive entire experience around
pricing. Everything from how you package
features into different tiers or how you
think about rate limits or volumes or
even custom terms. How you design this,
but more importantly, how you
communicate this to customers
contributes to that overall alignment of
price to value and should be something
that uh that we find teams need to and
should be continually experimenting
around.
So, how is this actually done and and
and how are we finding um how are we
helping teams do this? I wanted to share
a little demo with you of Orb
Simulations which is a product that
we've built in around this idea of
helping um customers find their right AI
pricing strategies. So, let me tell you
a little bit of a story here for
context. This is Orb. We are connected
and integrated with a raw and real-time
stream of product usage events where
what you're measuring and instrumenting
in your application you can send into
orb for billing and you know we've
created a very flexible billing offering
where um where like folks can come come
in and um be able to like monitor in
real time what their acred costs are and
get a sense of um how much chargers our
customers racking up. So again, this
this idea is like the marriage of an
analytics product with a billing product
connected together to really drive a lot
of that real-time insights.
But last uh last year in in about the
last six months, what we were noticing
was a lot of our customers were using
our product in a little bit of an
unexpected way. What they were doing is
they were sending data into Orb and then
they were setting up shell pricing
structures on top of that data, but not
actually billing their customers for it.
and we didn't understand why they were
doing that. So, we called a few of them
up and turns out that they were running
the closed beta periods of a few
upcoming product launches and several of
those were the the closed beta periods
of uh AI agent products coming out to
market. So these teams had a product
build. They had a group of customers
that they they wanted to start to get
feedback from but weren't fully pencils
down on what exactly was the right
pricing strategy and they were trying to
hack orb into figuring that out. So what
we did is we built a whole product
experience around helping that that
workflow. So what simulations let you
lets you do is back test or or or test
out alternate pricing strategies on top
of that data platform of of product
usage. And it's designed to help teams
really answer that what if question.
What if we tried pricing in that way or
what if we experimented with a different
model? Let me show you how that works.
So in Orb, you can come in and define a
simulation. So maybe for our our demo
over here, we're going to pick a
simulation time period that perhaps is
going to span the period of our closed
beta. And we're going to pick a cohort
of customers. This might be just
customers that are not yet paying in
that closed beta period or might even be
a group of existing customers that are
um paying for a previous or older
version of of your pricing and that
they're about to get uh a new kind of
value. And what we enable you to do is
to build out scenarios for alternate
ways to price. So maybe we can start
with our baseline and what we're going
to do is try more of an add-on based
pricing strategy where what we'll do is
um uh maybe come in and say we want to
add a fixed fee. We're going to charge
everybody $20 for um AI agent access.
Alternatively, we might want to add a
more uh granular or discrete pricing
strategy where we can come in and
actually charge for tokens. And perhaps
for those tokens, we want to charge a
simple unit price or maybe more of a
tiered fee where some tokens come
included for free um and others are
charged on top of that. Again, the idea
here is to really help you build a
different set of pricing possibilities
so that you can help answer your ideas
about how to price in a rich data
informed way. So when we run that
simulation, what we'll generate is a
report that shows you uh a lot of
insights and and and angle points into
this decision of how you should price.
So first and foremost, between these
scenarios that we've outlined, we'll
show you what the biggest impact to
topline revenue is. So you can kind of
understand based on the actual
consumption usage patterns of your
customers, uh which uh pricing scenario
might u be the most effective one. But
if anybody who's lived in this world
knows and understands that it's not just
about topline revenue when you're
dealing with existing customers that are
paying you a certain way or a certain
amount, it's kind of hard just to hike
up pricing on them. So getting an
understanding of what the lowest average
change to existing customers will be is
also really important. And so what we
give you is a very detailed view into
the revenue mix that you might expect
based on a particular pricing strategy
and even a scatter plot that shows you
like percentage magnitude change as well
as revenue impact for different
customers. Uh obviously with with an
exportable data set of impact to each
individual customers. I think the big
idea here is to break through a lot of
this fear, uncertainty and doubt of how
should I price with data and help teams
uh you know if you will travel time or
or really help explore the space of
pricing possibilities so that when you
get to that moment where you're ready to
launch you're doing so very confidently
and with certainty of what that impact
is going to be to your existing customer
base. So that's Orb simulations and
again really the idea here is if we
really want want to help you not have to
price on vibes or uh ideally will give
you some tools and our perspective is
that um uh AI builders should always
simulate first before putting something
out to market
to close up over here you know we've
explored some uh ideas around how to
price first of all thinking about should
you even monetize directly or indirectly
then explor explored the value metric
selection question. Just making sure
that the way your pricing really aligns
to the value that you want your
customers to perceive from your product
and to incent the behavior that you want
them to to take. And then thirdly, um
really explored how uh experimentation
and thinking about pricing evolution in
a continual way is not just valuable but
really critical and important um for for
these kind of AI products. So, you might
be thinking that this is um hard or
tricky. Well, it is hard or tricky, but
uh what we've seen with our customers is
that they've been able to achieve truly
headline inspiring results on their
revenue front and get their amazing
products to the right audiences by
finding that uh aligned and and and
correct pricing strategy. Um, and we've
really been able to see up close that
when you can get it right or when you
can converge to write, the impact can be
huge and uh, valuable.
We are going to be hanging out today on
booth G17. So, if you're curious about
how you should bring your AI your new AI
products out to market or if you want
more data points or ideas about how
folks are thinking about AI pricing, um,
we'd love to chat. Thank you very much.
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