You used to be able to revisit pricing once every 18 months. Now it could be out of date within 6 months.

I’ve advised more than 100 SaaS and AI founders on their pricing and monetization, including more than a dozen already in 2026. Pricing anxiety is higher than I’ve ever seen it before and nobody seems to be happy with their pricing. Even a giant like Salesforce just announced their fifth AI pricing model (🤯) in fewer than two years.

Each pricing model comes with so much baggage. Flat-fee subscriptions block upsell. Seat-based pricing isn’t future-proof. Hybrid pricing is too difficult to explain. Token pricing is getting commoditized as enterprises shift away from tokenmaxxing.

Up today: 4 questions to ask to see if your pricing model needs to change. Plus, proven tools, Claude skills, and frameworks to improve your pricing strategy.

🔓 Bonus for paid subscribers: Tools to run your own pricing project and a new Claude skill to design a willingness-to-pay survey.

Perhaps the most important reason to change pricing is that your goals have changed.

You’ve moved from prioritizing growth-at-any-cost to profitability. You’ve pivoted from SMB to Enterprise customers. Or you’re shifting gears from new logo acquisition to building an expansion motion.

Show me what you’re optimizing for, I’ll show you the right pricing strategy.

One small problem: most leadership teams I’ve worked with haven’t clarified their objectives (aside from perhaps an ARR target). Each function focuses on different metrics. Pricing gets caught in between.

How to do it: The pricing trade-off exercise

Start by writing down the 3-5 most important business objective decisions that relate to your pricing. My go-to’s are:

Send this exercise to your executive team as a poll. Each leader should fill it out without being biased by what others think — this is what gives you a clean baseline.

Map how everyone voted on the slide above. Then discuss the discrepancies in your next exec meeting. This gives you the North Star for your next pricing decision.

Pricing can look great on paper. But what matters is how prospects perceive it.

My rule of thumb is that you should aim to lose 20% of deals due to pricing.

Why 20%? It filters out prospects who don’t have budget or didn’t see much value in your product — these wouldn’t be great customers anyway. But it’s not so high that pricing is a major barrier to winning deals.

If you barely get any pushback on pricing, that’s a powerful signal on its own. The signal: your prices are probably too low.

If you’re losing 40% or more of your deals due to pricing, that’s a red flag. Something changed in the market and what you’re doing isn’t working.

How to do it: Win-loss pricing analyzer

If you have an external win-loss program or great CRM hygiene, start here. Track your win rate by quarter and the percentage of losses due to pricing versus other factors.

In my opinion, your best pricing data is usually buried in your call recordings. I’ve turned this into a win-loss pricing analyzer Claude skill. Upload it into Claude and then you can directly reference the skill in the prompt bar by tagging the file name.

📄 Download the skill: Win-loss pricing analyzer skill

What it does:

The narrative includes a drill-down into whether pricing-related losses are isolated in a specific segment or product, what the prospect actually said (verbatims), and what’s changed over time.

I recommend running the analysis at least quarterly, although monthly would be even better for mature or competitive categories.

If you’re flying blind on any CRM or call recording data, here’s a fallback: see what Claude or ChatGPT think of pricing in your product category. Ask LLMs to summarize pricing sentiment from sites like Reddit, G2, and LinkedIn, and explain trends over time. Pro-tip: use my deep GTM research Claude skill to get analyst quality research that cites its sources.

The favorite VC pricing advice du jour is to “get in the token path”. Said differently, tie your monetization to token consumption since tokens are growing exponentially (and will presumably keep growing).

I find this advice to be overly simplified, and it’s probably not very resilient as the pendulum swings from tokenmaxxing to AI cost optimization.

But the fundamental lesson is a good one. All else equal, tie your monetization to a metric that’s growing. (There’s a reason why cell phone companies no longer charge based on minutes of talk!)

Seats used to be a reliable metric, particularly for companies selling to startups. That’s far less certain today. Would you be better off with a new value metric?

How to do it: Cohort-based value metric analysis

Brainstorm a list of potential value metrics that could conceivably be part of your pricing. This should include headcount-based metrics (pulled from LinkedIn), seat-based metrics, usage-based metrics (ex: tokens, tasks, API calls), product-based metrics (ex: features used), and outcome-based metrics.

Then pull your 10 best ICP-fit customers who’ve been with you for at least 6 months. For each of these customers, track their starting metrics and then how those metrics trended after their first 3 months, their first 12 months (if applicable), and all time.

Aggregate the data across these customers and compare which metrics are growing the fastest within your best accounts. Look to see metrics that are stable in aggregate (i.e. not subject to huge swings or seasonality) and where behavior is consistent across your top accounts.

Armed with this data, feed the results to Claude and run my /pricing-metric-selector skill. Again, you just need to upload the skill file into Claude and then you can directly reference it in the prompt bar by tagging the file name.

📄 Download the skill: Pricing metric selector skill

What it does:

This is actually the first question I ask in my consulting work. Tell me about your last pricing change: what did you change, why did you do it, and what happened afterwards?

It astounds me how many companies have invested so much time and effort into a pricing change, but nobody can agree on whether it delivered what they hoped for. This analysis gets overlooked because there isn’t a single pricing owner at most startups (or the owner swings across teams).

If the pricing change faced no pushback, that’s a sign that it probably didn’t go far enough. If the pricing change did face pushback, it’s worth digging into what exact problems came up and how to fix them (which could mean another pricing change).

How to do it: Pricing post-mortem analysis

Run a retrospective of your last major pricing change (and document it).

I recommend the first retrospective after about 1.5-2 turns of your median sales cycle, i.e. if your sales cycle is 45 days, do it between 60-90 days after the pricing change went live. Then refresh the analysis once that cohort has gone through a renewal cycle.

The metrics to include in your analysis:

Some of these metrics will likely improve. Others will be flat or down. The ultimate verdict comes down to what you decided to optimize for in the first place.

Pricing is ultimately about positioning. You want to tell a story about who your product is for and why it’s better than the alternative.

None of these four questions have a permanent answer. That's the whole point.

The teams that win in 2026 will be the ones who built the muscle to revisit pricing before the market forces their hand.

If pricing can go stale in six months, when’s the last time you actually checked?

Paid subscribers can go deeper with tools for running a pricing project including (a) a 34-step pricing project checklist, (b) my new Claude skill for designing a pricing feedback and willingness-to-pay survey, and (c) the full library of 15+ Growth Unhinged Claude skills for GTM and pricing analysis.