Saagar was interviewed by Rory Woodbridge at “The ProductMarketer” to discuss all things Pricing & AI. Part 1 is about thefundamentals - how pricing works as a system, who should own it, and how to getstarted. Part 2 is about what AI is doing to pricing and the practical trendsreshaping how companies charge for things.

What are the trends you're seeing acrosspricing projects? What's changing because of AI?

Seats-based pricing is becoming an existential issue for alot of businesses. There are three key reasons why.

One is that with AI, we can now get closer to measuringvalue based on the work done, instead of just access to the tool. The second isthe variable cost piece - how can you have a flat seat price when the variablecost means different users will use things at different intensities, andthere's pressure on margins? And the third is more fundamental, which is thatyour AI products might - by their nature - be inherently trying to reduce thenumber of seats a customer has. So how can you tie your revenue model to ametric you're trying to reduce? You might be selling 10 seats today, but you'reusing AI to replace some of those seats. When you reduce from 10 to five, youmake half the money while delivering double the value. That equation isexistential.

But we don't say it's one-size-fits-all. Seats aren't wrongfor everyone. Take Slack - they still use seats. Their AI isn't trying toreduce the number of people you need. It's a collaboration tool whereeveryone's on there, and more people being on there makes everyone a bit moreproductive. That's what we call more of a co-pilot type of AI, and the unit ofvalue is still a human or a seat. The difference is you've still got tomonetise the premium version with AI versus the base version without it.

People are talking about outcome-basedpricing, especially with AI products. Is that realistic?

Some folks see the world moving towards outcome-basedpricing, where you're creating a P&L outcome for the customer - costsavings, revenue generated, whatever it is - and your AI is helping to getthere. That's a good aspiration. But, at Northlane, we think the world is along way from that as a default pricing strategy.

The reason is mainly attribution. You've got a BDR tool thatyou think helps close five more deals per month. You can make a business casethat your product has driven 100K of value in a year. But for you to then say20% of that is coming back to you - a lot of things must go right for thatattribution to hold. And what CRO is going to give that money back? They're atthe board meeting getting congratulated, and then you're saying 20% of thisgrowth was down to the tool. That's a tough political conversation.

There are a few situations where it can work, though. One iswhere you're connected to the payment loop. Companies like Chargeflow, forexample, who go out and collect payments that have bounced. Their agents dothat, and they come back and say they've found £10,000 you were never going toget. So, they keep £2,000 of that - because ultimately you would have got zerowithout them. When you're close to the payment loop, it makes a lot more sense.It's a narrow win-no-fee setup.

So usage and consumption pricing is themiddle ground. How sustainable is that?

Credits-based models have always been part of pricing. Thinkof Audible, or old mobile phone contracts - you'd top up 10 pounds and draw itdown based on a minute of phone time or a couple of text messages. It's notnew.

The difference is that credits are now a way to communicatesomething with a variable cost that might also have variable value. Each unitcan be assigned a specific value. But the challenge is that the variable costbasis is continually changing. If Anthropic or whoever changes their pricingstructure, you must keep rebasing your credit system when different actionscost different things and you're trying to preserve a certain margin.

What I see as more of a risk is that ‘cost-plus’ thinking isback in vogue. We spent a lot of the SaaS era talking about value-basedpricing, and token-plus pricing gets us back to cost-plus. For an early-stagebusiness, you've got to stay alive and cover your costs. But as soon as you'vegot product-market fit and you're clearer on your value proposition, you've gotto orient the model to communicate value. And credits can obscure that - youcloud the specific value props within the credits system.

Clay is a good example. One of the key reasons they changedtheir pricing recently was to elevate the value proposition of theorchestration layer they're providing. Before, it felt like credits connectingyou to various systems, and they came across as a data infrastructure player.When really, where the value sits for them is in the connective tissue acrossall those systems. Being able to distinguish value from cost in your model isgoing to become even more important.

It sounds like we're watching the SaaS story- working out pricing from scratch - play out again, just faster.

Exactly. Moore's law would have us think that compute costsand token prices are going to trend down. But that's only true once we'veexhausted the quality constraint. In the SaaS and cloud world, when it was asgood as it was going to get, costs started trending to zero. But we're still ina world where everyone is trying to make better models, make them run faster.There's so much efficiency growth at the language model level. So, we're goingto see significant fluctuations in price, and because the timelines are so muchquicker, the fluctuation gets compressed.

The best trend we're seeing is people treating pricing likea product. You iterate, you get feedback from the market, your customers, yourfinance team. A lot of companies were sleeping on their pricing model for awhile, maybe doing price increases occasionally, but not thinking about it as adesign system with loads of different inputs. Treat pricing like a product andmake sure you're still engaged with customer value.

The data shows companies made 3+ pricingchanges on average in 2025. Is that a good trend?

We're seeing that. Some indices show companies are changingprices about three times a year now. And that might be price points, which aremore nimble changes. But it's also packaging structures and price models, whichare more fundamental shifts.

That shows what we've been saying - treat pricing like aproduct - is now happening. If you're getting feedback from the market on yourproduct, you'd be more than happy to ship a new version when the time feltright. But you've got to have the dynamism both upstream and downstream to makethat work. There's the tooling side - billing systems, data, telemetry. There'sa whole new ecosystem of tools recognising you need to be nimble. And upstream,you need to set expectations with the execs that this is not set-and-forget.Set up a governance and pricing committee. Bring data every month. Here's whatwe're getting from the market, what the sales team thinks, what customerresearch shows. And then you say, all right, stick or twist. What are wechanging?

Companies like Clay, Notion, and Figma are experimentingwith their pricing as a point of pride. It's almost their strategy. I believe alot of product marketing thought went into how Clay changed their pricing,because it was all about positioning. They were being viewed as a data layer,when they said our value is in this orchestration layer. And that's very muchlinked to positioning. The most impressive companies have that constantiteration loop between who they want to be and how pricing evolves with it.

When pricing changes, there's usually amountain of product marketing work. What do the companies that handle rolloutswell do differently?

Getting buy-in and not having it be a big surprise. Somefolks have this idea that they're going to build a new pricing model in stealthand launch it, because they can't be bothered taking everyone on the journey. Idon't think that's the right way at all. It's about bringing people along,getting trust and credibility, and then having the speed of iteration to takefeedback quickly. Recognising what's good feedback and what's noise is a muscleyou build over time.

We'd also recommend testing with new customers first if youhave a new business engine firing. That's your quickest way to get feedbackwithout being scarred by legacy packages. You can assess whether the newpricing model fits the new world. Then it's a separate question of how tomigrate the old world onto the new one.

Price transparency feels like it's becomingmore important, especially with AI-powered search. Are you seeing that?

This is becoming a hot topic again. With more answer engineoptimisation and agents doing procurement work, if they can't find your pricingon the website, you're going to get kicked out of a lot of conversations.That's going to be table stakes for go-to-market - having a clean, clearpricing page with strong messaging. And maybe even price points on the website,which is a perennial debate in the pricing world.

If you see that world moving in the direction of agentstalking to answer engines, then the shopping cart after the comparison isprobably an easy next step for the big tech companies. You see a comparison ofthree products in an AI overview, and you could easily see a buy button. That'salready happening in the consumer world, and there's no reason it can't happenin B2B software. So, price transparency is going to become increasinglyimportant.

For a product marketer who doesn't havebudget for a specialist - what can they do to get smarter about pricing?

There are some strong resources out there. Kyle Poyar'sGrowth Unhinged is excellent. The Pricing SaaS guys, Rob and Jon, are doinggood work. And Lenny's Newsletter often has people talking about pricing aswell. Madhavan Ramanujam's Monetising Innovation is worth reading - itbreaks pricing down to first principles with strong analogies and examples.

But what I would caution is that theory is one thing andpractice is another. Be wary of seeing hot takes on LinkedIn and applying themstraight into your business. There's so much more upstream and downstream workneeded to make a piece of theory apply to your specific situation. And that'swhere external help does add value - someone who can see the bigger picture andcome at it with a fresh perspective built on pattern recognition. No one hasdone your specific job at your specific company at this specific moment. Sodon't beat yourself up if the framework you're hearing doesn't quite make sensefor your situation.