In this episode of Thank You for Vibe Pricing, Chargebee's series on teams scaling AI monetization, we're joined by Akshay Sharma, co-founder at Morph Systems and a former pricing leader at Miro, Splunk, and McKinsey.

Akshay shares instructive notes on:

How every pricing choice informs user behaviour (featuring a story on why Miro originally instituted a five-seat minimum on a paid plan and removed it post product-market fit), a framework for assessing why pricing based on usage is the right answer for most companies today, how differently Splunk and Miro each navigated the shift towards pricing based on consumption, the necessary frictions of the emerging credits model, a systems view on running effective pricing experiments, a case for low-margin business models, why he sees an AI monetization future where “you would literally be changing your price per second, as if you’re selling a commodity,” and much more.

Chapters:

00:00 - Episode intro

02:24 - Akshay's journey in tech

05:20 - Pricing as a product feature

09:03 - The (newish) value of software

14:45 - Monetization meets org charts

18:39 - Usage-based pricing's big unlock

23:38 - Transitioning to consumption models

27:58 - Credits and user momentum

31:12 - Low-margin software

36:01 - A system for pricing experiments

42:48 - Seats, hybrids, and pricing agents

45:03 - On pricing frameworks

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Referenced:

Miro

How to Leverage Pricing and Packaging to Drive Growth, Revenue, and Profit

Should Open AI switch to Credits for it's subscription?

Splunk

Salesforce

Clari

LinkedIn

Cursor

Replit

Lovable

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Connect with Akshay:

LinkedIn

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Connect with Vinay:

LinkedIn

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Connect with Ariela:

LinkedIn

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About Chargebee:

Chargebee builds foundational billing and monetization infrastructure for the dynamic, fast-scaling AI-era business models — from seats and usage, to agents, outcomes, and hybrid pricing structures. Built for product, finance, and GTM teams that treat pricing as a growth lever.

In this episode of Thank You for Vibe Pricing, Chargebee's series on teams scaling AI monetization, we're joined by Annika Schmid, PM for PostHog AI.

Annika shares instructive notes on:

PostHog's cost-proximate, yet unconditionally value-first pricing philosophy, why PostHog treats pricing first and foremost as a product feature rather than a revenue lever, how they designed PostHog AI's free tier by working backwards from persona and beta usage data, how they navigated the question of whether AI should be priced as a horizontal platform layer or rolled into individual product pricing, how the billing team and product PMs divide ownership on monetization bets, and Annika's predictions on the two paths that AI monetization models can take — both centred on consumption — in the near future.

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Chapters:

00:00 - Episode highlights

02:16 - What's PostHog?

03:11 - Annika's role at PostHog AI

04:41 - (Principally) pricing closer to cost

06:37 - "Default alive" monetization

08:58 - A methodical view of costs

11:39 - Designing PostHog AI's free tier

14:38 - Factoring in LLM volatility

18:51 - Charging for consumption

21:54 - Pricing as a product

23:05 - Usage and spend reporting

25:53 - Monetizing a multi-product AI layer

32:18 - Where pricing hypotheses originate

35:14 - MoM growth metrics that matter

37:07 - Pricing PMs and the billing team

39:47 - What's more exciting than pricing AI

42:17 - The habit of changing prices often

44:37 - Two (near-future) pricing predictions

47:02 - AI pricing as an existential question

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Referenced:

PostHog

AWS

Cloudflare

PostHog's pricing principles

8 learnings from 1 year of agents – PostHog AI

Non-obvious pricing advice for startups

Glue teams vs back-office teams

AMA w/ James & Tim (founders of posthog)

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Connect with Annika:

LinkedIn

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Connect with Vinay:

LinkedIn:

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Connect with Ariela:

LinkedIn

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About Chargebee:

Chargebee builds foundational billing and monetization infrastructure for the dynamic, fast-scaling AI-era business models — from seats and usage, to agents, outcomes, and hybrid structures. Built for product, finance, and GTM teams that treat pricing as a growth lever.

In this episode of Thank You for Vibe Pricing, Chargebee’s new series on scaling AI monetization, we're joined by Zona Zhang, who leads monetization at Clay.

Zona walks us through Clay's deeply value-led credit-based pricing model (among the 6 consequential GTM bets that helped them grow from $1m-$100m ARR in 2 years)—why monetization sits at the intersection of finance and product, how they’ve designed credits as a currency for unlocking modules of usage and exceptional, full-funnel growth, why and how they iterated on differentiating Clay’s enterprise pricing from the very early days, how they introduced credit rollovers after observing high "restart rates" from episodic users, why engineers at Clay review AI P&Ls as a core part of the product development process, and Zona's thesis on how AI monetization will evolve in the coming years, and much more.

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Chapters:

00:00 - Episode highlights

00:57 - Episode intro

03:37 - Zona’s career path

04:47 - Why pricing sits under finance at Clay

06:37 - AI credits as “currency”

07:20 - Reverse trials and fixing the education gap

08:57 - Pricing “modules of usage”

10:40 - What sets Clay’s PLG motion apart

13:48 - The thesis behind credit rollovers

15:45 - Strategies for solving usage friction

18:58 - Formulating Clay’s enterprise pricing

24:05 - Scaling pricing across GTM motions

25:30 - Two things that matter for discounting

27:42 - Unit economics and engineers

31:24 - The billion-dollar AI pricing question

34:36 - “We look at dashboards a lot”

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Referenced:

Introducing Clay Pricing 3.0

Varun Anand (Co-founder, Clay)

Kareem Amin (Co-founder, Clay)

The GTM Inflection Points That Powered Clay to a $1B+ Valuation

Asana

Custom Signals (a Clay feature)

Snowflake

Databricks

OpenAI

Everett Berry (Head of GTM engineering, Clay)

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Connect with Zona:

LinkedIn

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Connect with Vinay:

LinkedIn

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Connect with Ariela:

LinkedIn

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About Chargebee:

Chargebee builds foundational billing and monetization infrastructure for the dynamic, fast-scaling AI-era business models — from seats and usage, to agents, outcomes, and hybrid structures. Built for product, finance, and GTM teams that treat pricing as a growth lever.

In this inaugural episode of Thank You for Vibe Pricing, Chargebee’s new series on scaling AI monetization, we are joined by Aisling O’Reilly, Head of Pricing at Intercom.

Aisling talks about: Intercom’s complete pricing overhaul and why they risked multi-millions in ARR in the short run to chalk out a path of durable, customer-first growth, how they designed Fin’s pioneering outcome-based model (and chose it over a usage-based variant), the operational readiness that allowed them to price for AI resolutions, how and why Intercom PMs are evolving into more commercial GMs, how the future of outcome-based pricing will likely capture even more nuanced forms of value, and so much more.

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Chapters:

(00:00) Episode highlights

(01:41) Episode intro

(03:53) A “sink-or-swim” pricing moment 

(06:55) Origins of Fin’s pioneering model

(09:56) A CTO-led transformation

(13:16) Being ready for pricing outcomes

(15:55) Is a dollar too expensive?

(18:48) Inventive predictability unlocks

(22:57) A bet on falling infra costs

(26:15) “It took that one PM”

(30:27) No perfect pricing model

(32:51) Outcomes beyond outcomes 

(37:25) The intersection of product and pricing

(41:34) Strong-minded finance teams

(45:09) The problem with (cost-plus) AI credits

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Referenced:

Fin AI

LinkedIn

Darragh Curran:

Eoghan McCabe:

Pricing AI agents: What does ‘value-based pricing’ really mean for AI?

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Connect with Aisling:

LinkedIn

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Connect with Vinay:

LinkedIn

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Connect with Ariela:

LinkedIn

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About Chargebee:

Chargebee builds foundational billing infrastructure for the dynamic, fast-scaling AI-era business models — from seats and usage, to agents, outcomes, and hybrid structures. Built for product, finance, and GTM teams that treat monetization as a growth lever.

This is Thank You for Vibe Pricing — a new Chargebee series chronicling field notes from teams at the frontier of AI monetization. Tune in for sharp, in-the-thick-of-it operator breakdowns of what it really takes to price, package, and scale AI products in the real world.