If you want alpha - find it at https://www.artisangrowthstrategies.com/

Joseph W.

Cofounder of PricingSaaS, a pricing intelligence platform. We help SaaS leaders make smarter pricing and packaging decisions through data, research, and community.

Rob Litterst

We've built the largest SaaS pricing dataset in the world. 
Today we're plugging it into your AI.

PricingSaaS has been tracking pricing changes across thousands of SaaS companies every single week for two years. Every plan. Every tier. Every change.

Until now, that data lived privately in our platform.
Today we're putting it wherever you already work.

Yes 👉 The PricingSaaS MCP is live.

It plugs directly into Claude, ChatGPT, and Gemini - so when you ask your AI a pricing question, it pulls from our comprehensive dataset (instead of something scraped from a blog post in 2022.)

Ask it questions like:

❓ "What are the different ways to offer credits, with examples?"
❓ "How should I price and package my new AI feature?"
❓ "How does Monday’s pricing stack up against Asana?"

No more checking competitor pages, digging up screenshots from last quarter, or maintaining spreadsheets that are already out of date by the time you finish them.

Plus, along with our data, the MCP pulls thought leadership from our awesome partners, including DigitalRoute, Metronome, Nue.io, Paid, Schematic, Teneo, and WillingnessToPay.com 🙌🏼

MCP is becoming a critical distribution channel; if your users are doing their work inside AI tools, that's where your data needs to be.

And we are VERY pumped to see how you guys use ours.

Live now - go try it: https://lnkd.in/d7Auuv-J

Building the first stop for pricing and packaging.

Every AI company is trying to figure out pricing right now. Most of them are getting it wrong in the same ways.

Scott Woody ran Metronome (now part of Stripe) -- the billing platform behind OpenAI, Anthropic, and Nvidia. He's seen how the best AI companies actually operationalize pricing, and where everyone else stumbles. 

We brought Scott into Office Hours last week to unpack what he's seeing on the ground. Here are 4 things from the session I'd want to know if I was figuring this out right now:

1️⃣ Unpredictability is the price of growth - and it's worth paying.

Companies launching AI products can't forecast usage the way they used to. Scott's take: stop trying. Anthropic's CFO shortened planning cycles from yearly to weekly. If you're optimizing for predictability right now, you're probably leaving growth on the table.

2️⃣ Credits are becoming the default in B2B - but they're a vendor tool, not a customer feature.

Credits simplify billing when you have multiple products or SKUs. But Scott was blunt: they're generally vendor-friendly and can be customer-hostile. If your product isn't complex enough to justify credits, don't force it.

3️⃣ Outcome-based pricing is mostly a buzzword right now.

Everyone's talking about it. Almost nobody is actually doing it. Scott pointed out that what companies call "outcome pricing" is usually just work-based pricing with better marketing. True outcome pricing requires fully autonomous processes - and we're not there yet for most use cases.

4️⃣ You need a "Seal Team 6" with executive air cover to actually change your pricing model.

Bottom-up pricing transitions almost always fail. Scott's recommendation: isolate a small team (~7 people), give them autonomy and budget, have a senior exec sponsor them, and prove the new model in a narrow segment before scaling. Without that top-down mandate, internal resistance kills it.

If you missed this one - DM me and I'll ship the recording ✌🏻

Credit systems are everywhere in SaaS right now. But when you look closely, the complexity usually comes from one thing: 

Teams build the mechanism before defining what a credit actually represents.

There are three questions that cut through that quickly. 👇

P.S. These insights come from our PricingSaaS report on AI credit systems.

Next week we’re dropping the next one on PLG + SLG with our friends at Nue.io. AI is forcing a convergence between the motions, and we're noticing a trend towards hybrid-led growth. 

Keep your eyes peeled for the full report next Tuesday 👀

Designing Credit Systems

Triple Whale just published the best FAQ page I've seen.
And it's not about their product. It's about their pricing.

Specifically - their credit-based pricing model.

They didn't just say "here's how credits work" and leave you guessing.

They answered literally everything:

→ What uses credits. What doesn't. 
→ How many you get. What you can do with them. 
→ Whether they pool across your team. 
→ Whether they roll over. 
→ What happens when you hit zero. 
→ What happens if you run out mid-action. 
→ Whether credits burn in the background.
→ How to track usage. How to top up. 
→ What $500/month actually looks like in practice.

Every. Single. Question.

This matters because credit-based pricing is confusing.

Most companies launch it and leave buyers to figure it out themselves.

"How much will I actually spend?" No idea. 
"What happens if I run out?" You’ll just have to find out. 
"Do unused credits just disappear?" Maybe?? 😅

Triple Whale instead have showed a real breakdown of what $500/month gets you - down to the number of AI prompts, agent queries, image generations, and edits.

They built a rollover table so you know exactly which credits expire and which don't.

They even told you credits don't burn passively in the background.

That level of transparency isn't just good documentation. It's a trust play.

If you're running any kind of usage-based or credit model - study this page.

Not for the pricing, but for the excellent communication.

Because the fastest way to lose a buyer on consumption pricing is to make them feel like they can't predict their bill.

Triple Whale Credit Pricing

10 companies made AI pricing changes this week. All 10 took a completely different approach.

The honest answer to "how do you price AI?" remains: 

It depends. But the experiments are getting sharper.

This week, I broke down 10 moves worth exploring, including:

▶ Monday.com's Enterprise AI Bundle
▶ Notion's Custom Agents add-on
▶ Softr redenominating credit values
▶ Dialpad pulling from the Intercom playbook with per-conversation pricing

Full deep dive below 👇

For the past year, everyone’s been asking the same question: how do you price AI? I wish there was a silver bullet. The honest truth is it depends. It

Open article: 10 AI pricing moves you can steal this week by Rob Litterst • 4 min read

10 AI pricing moves you can steal this week

We price AI like electricity - by how much you use.

But what if the better model is pricing it like talent - by what it can do?

Rocketlane just launched an add-on that pushes in that direction. They still differentiate tiers by credit volume, but also by the jobs each agent can do. 

Pure credits puts it on the customers to figure out how much each job consumes. 

Pricing based on job complexity lightens the cognitive load.

Think this is where pricing is going 👇

Last week we launched the PricingSaaS MCP. 
This week, John created a PM agent (named Tom) and created a digest of activity. Some highlights 👇

🔍 Most Researched Companies:

1. Notion
2. OpenAI
3. Hubspot
4. Figma
5. Anthopic

📈 Trending Topics:

1. AI Credit Models
2. Credit-Based Pricing in General
3. Credit-Based Pricing
4. Packaging Frameworks
5. Volume Pricing Mechanics

🛠️ Most-used Tools

1. Search_Companies (find relevant companies)
2. Get_Company_Details (research companies)
3. Fetch_Diffs (find pricing changes)
4. Get_Company_History (deep research)

If you missed the announcement last week, check out the link below to get going:

Link 👉🏼 https://lnkd.in/egHuNRTd

How do you price and package an agent?
I broke it down recent examples from Notion and Hubspot in this weeks video 👇

PS. No walk and talk today. Just got back from a run, and didn't have it in me. Apologies in advance to all of you who are dissappointed in me. I'll be back on the Walking Pad next week 🫡

Welcome back to Good Better Best. Each week, we break down real pricing, packaging, and product moves from SaaS leaders and extract the ideas worth st

Open article: How to Package Agents by Rob Litterst • 3 min read

If you're building an AI product, you've probably got a credit model problem.

Not because credits are wrong. They're actually one of the better answers to AI monetization. It's because explaining credits to customers is hard.👇

Your buyer has spent the last decade purchasing software by the seat. A credit is an abstraction — and abstractions don't close deals.

❌ Say "1,000 credits are included in your plan" and you're asking someone to evaluate a unit they've never seen before. 

✅ Say "your plan includes enough credits to run 500 contract reviews a month, the equivalent of what a junior paralegal handles in a week" and suddenly the value is clear.

That gap — between a credit as a unit and a credit as an outcome — is where most AI pricing conversations break down.

Here's what's telling: The Paid team ran three monetization workshops in SF with 30+ AI companies last week. The question that came up in every single room wasn't about infrastructure or rate cards.

It was: "How do we explain the value of a credit to a customer?"

The challenge isn't technical. It's communicative. 

Most founders intuitively know credit models work — they're stuck on making them feel real to buyers who've never had to evaluate an abstract unit before.

Next week, we're running a small, hands-on session with Manny Medina from Paid to work through exactly this.

📅 Thursday, March 19 | 11am–12pm EST

Manny is building the infrastructure AI companies use to monetize — he lives this problem daily. Not a webinar. Real Q&A and workshopping your biggest questions around credits. 

Registration requires approval, so spots are limited. 

Comment "Credit" below and I'll DM you the registration link.

There’s no standard playbook yet for pricing AI agents:
👉 Do you bundle agents into the product?
👉 Gate them behind higher plans?
👉 Or charge for them separately?

Last week, Notion showed one way to approach it.

They launched Custom Agents, but instead of folding them into the core product, they positioned them as a separate add-on for Business and Enterprise customers.

Teams can start experimenting with agents now, with a free trial running until May. After that, usage shifts to a credit model priced at $10 per 1,000 credits.

Importantly, the base Business plan price isn’t changing.

And that positioning is deliberate. 
Agents aren’t being treated like another feature in the product. They’re being treated as a new capability layer. Which makes sense.

Some teams might create a couple of agents to help with internal workflows. Others might build dozens into how they run projects.
Usage will vary massively.

So instead of forcing everyone into a higher plan, Notion kept the core product as it is and introduced agents as a separate usage-based add-on.

It also gives teams time to figure out where agents actually fit into their workflows.

And once that usage becomes real, the pricing scales with it.

What are your thoughts on this?

If you're in SF next week, this is worth your time 👇

Our friends at Paid are running live monetization workshops with two of the best VCs in the game - Battery Ventures and General Catalyst. 

Pricing AI products is genuinely hard right now. The old models don't fit. Usage models are unpredictable. Outcome based pricing isn't quite there yet. 

Most teams I talk to are guessing, and looking for all the help they can get. 

If that sounds like you, consider this your bat signal. 

These aren't theoretical lectures. They're working sessions to solve the specific pricing hurdles your company is facing right now. 

If you're trying to figure out how to price an AI product in 2026, this is the room to be in. Learn more and register at the links below:

▶ Battery Ventures (Wed 3/25): https://luma.com/mv1gtwf9
▶ General Catalyst (Thu 3/26): https://luma.com/eort220s

Seats are limited. Grab yours and lock in 🔒

Open article: Monetization Workshop with Paid & General Catalyst · Luma by luma.com

Monetization Workshop with Paid & General Catalyst · Luma

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