# Pricing and packaging, from a first business model to AI agents — Summary The Pricing work group takes on pricing in group chats, from a first business model and SaaS tiers to AI features. Members also cover what gets people to pay at a paywall and how to move existing customers to a new price. Updated: 2026-10-13 HTML page: [Pricing and packaging, from a first business model to AI agents]() Transcript: [Full Chat Transcript]() Work group: [Pricing]() ## Questions ### [Retainers, project fees or upside: pricing consulting work]() Pick the fee by the shape of the work, and add a performance component only once the metric, the baseline and the client's part are written down. - **Match the fee to the work.** Kyle Poyar charges a retainer for ongoing advising and a fixed fee for workshops and defined projects. Hourly can work for a small, uncertain request, but the client pays more when the work runs long. Japna Sethi sells the first phase, research and a funnel diagnosis, as a fixed-scope project, so the client gets a deliverable even if a later experiment loses. - **Be careful with outcome fees.** An agency's revenue result also depends on the client's sales team, budget and execution, Kyle says. Dan Hockenmaier adds a performance fee only after a fixed-fee diagnostic finds a lever the consultant can change, and ties the upside to incremental conversions against an agreed baseline, with a control group where one is feasible. - **Size the fee against the client's upside.** Kyle uses a client's estimated upside to judge whether a fixed fee is proportionate, and keeps the fee fixed whatever the result. When the estimate rests on shaky assumptions, he sells a smaller diagnostic first. Alvaro Morales wants the baseline, qualifying events, data source and a dispute window in the agreement. - **Prepaid units for variable requests.** Alvaro suggests a prepaid block of work that draws down against priced units, and says it is his recommendation rather than standard agency practice. Ben Williams would use units when requests vary month to month, define each unit by its deliverable, and show units bought, used and remaining on every invoice. ### [Choosing a business model while a startup or community is young]() Find the behavior that creates value, put the charge where it does the least damage to it, and test with real buyers before you commit. - **Leave the scarce action free.** Dan Hockenmaier asks whether the scarce input is contributors, people finding each other, or work done for members. eBay waived listing fees for sellers' first 250 items a month because charging first held back supply. Kyle Poyar would leave that contribution free and test a paid offer with whoever benefits from it. He points to Expensify, which charged only for people who submitted an expense report that month. - **Tie repeat use to a way to pay.** Elena Verna follows one user from first value through return visits to a purchase or a sales call. Free access needs a job: at Miro, free users could collaborate on three boards, which brought in more people. She also checks that users know a paid offer exists. In one case she cites, 70 to 80% of free or trial users did not. - **Watch what happens after the first purchase.** Dan reads repeat purchases moving off-platform as a sign the service on later purchases may be worth less than the fee. Ben Williams, drawing on Snyk Advisor, warns that a free resource for a nearby use case can bring lower-intent signups, so he compares those cohorts with users who came for the paid problem. - **Run the math, and keep the model easy to change.** Kyle asks how many paying organizations it takes to cover the cost of serving everyone. Alvaro Morales says to put the proposed charge in the product brief before launch, and wants billing cheap to change; at Asana he saw pricing ideas stall on six to twelve months of engineering work. ### [Setting a per-transaction fee: flat, percentage or volume tiers]() Charge for a completed transaction customers see as value, size the fee against the work the platform takes on, and judge it by what customers do after the first paid transaction. - **Pick the structure by what grows.** Kyle Poyar uses a fixed fee when the work and value per transaction are similar, a percentage when value rises with the amount, and volume tiers when each added transaction is worth less. Dan Hockenmaier adds that a percentage fits when the platform takes on cost or risk that grows with the order, such as financing, returns or delivery. - **The ceiling is the work you remove.** Dan says to model the fee against the seller's next-best way to win and serve the customer: if the seller saves $20, a $25 fee needs another source of value. Faire's net payment terms and free returns give its fee a different basis than an introduction. - **Agree on what counts.** Alvaro Morales says to replay real transaction events through the billing rules, count each order's first successful completion once, and link refunds and reversals to the original order. Product, engineering and finance should sign off on the rules, and customers should see a running count and projected charge. - **Test past the first transaction.** Elena Verna pilots one fee against another on the same journey and tracks second and third transactions. A fee that lifts first-order revenue while cutting later orders prices away growth. Ben Williams models the monthly bill for a small and a high-volume account, since a rate that works self-serve may need a volume commitment once procurement is involved. ### [Taking a pricing proposal to the executive team]() Put the decision on the first slide, show the evidence and the exposure account by account, and ask for a staged test with agreed stop rules rather than a full rollout. - **Open with the decision.** Elena Verna's first slide names the use case being charged for, the value metric, and the proposed price and package. Her evidence slide separates awareness of the paid offer, friction in buying it and the offer itself. Kyle Poyar would show a pricing-page usability test and buyer interviews before arguing the offer has to change. - **Show every account's change.** Kyle gives the migration its own slide: each account's current price, new plan, new price and delta, overlaid with customer health. A 100% increase for an account already at risk of churn calls for an exception or a different path. - **Make the trade-off explicit.** Dan Hockenmaier puts two prices side by side and asks whether the next dollar of margin does more in the company's hands or the customer's pocket, marking which estimates come from observed behavior. Japna Sethi brings a decision table by segment; at Productboard, the fear that a free plan would cost sales pipeline decided which experiments ran first. - **Ask executives to approve a pilot.** Kyle's final slide names the segment, the package, the holdout, the return date, a break-even threshold and a pipeline stop rule. Alvaro Morales adds a report that replays real usage under the new rates and limits rollout to cohorts whose bills pass. ### [Designing SaaS tiers around a value metric]() Choose the value metric before you draw tiers, start from Good-Better-Best, and plan for the packaging to change. - **Value metric first.** Alvaro Morales asks which unit of usage tracks the behavior you want customers to repeat; the wrong one rewards the wrong behavior. At Amplitude, Elena Verna paired one usage metric, events, with more advanced features up the plans. Kyle Poyar gates outputs and leaves inputs open, as SurveyMonkey caps the responses you can see while a survey keeps running. - **Start from Good-Better-Best.** Kyle says about two-thirds of SaaS companies end up there. A feature belongs in an add-on when it is polarizing, reaches a different buyer, is usually adopted 6-12 months after purchase or costs real money to serve. Japna Sethi sorts features by the segment they move, and asks sales which deals a feature shows up in. - **Put capped AI in every tier.** Elena argues against premium-only AI: give every tier some AI, cap it by usage, and let the cap prompt the upgrade. Alvaro, who first offered premium-only AI as an option, comes round for products where AI is the core value. Lauryn Motamedi lands there too, because new users need to try it during onboarding to activate. Kyle still sells AI as an add-on when it draws on a different budget. - **Ship changes fast, judge them slowly.** Alvaro ships a change as a new plan version for new signups. Japna warns that free-to-paid conversion takes months to read. Kyle's survey found that at $5-20M ARR, 21% of companies say nobody owns pricing, so he names one owner. Elena gives model changes to a monetization council and lets product teams ship the rest. ### [Tearing down a competitor's pricing, and the business behind it]() List what the competitor charges and how its plans step up, then work out why: the value metric, where the revenue sits and whether a high price pays for sales or is margin. - **Rebuild the plan logic.** Ben Williams asks three questions of each plan: what problem it solves, who it is for and what moves a customer to the next one. Kyle Poyar puts every competitor in one spreadsheet with the same columns, from pricing model and value metric to free plan, public prices and promos. Across 40 leading AI apps, he found 70% used subscriptions and 64% published their prices. - **Use the product, then read the page's history.** Elena Verna signs up with spare Gmail accounts to see where the first paywall and upgrade prompts land, then checks the Wayback Machine for what changed since last year. She warns against copying what you find, and re-checks AI competitors monthly because their pages change so often. - **Test the metric, then find where the money is.** Kyle asks whether a customer gets twice the value when the metric doubles: seats suit a horizontal tool like Slack but can cap expansion in vertical products. Ben looks for where a competitor's revenue is concentrated. Alvaro Morales reads paired moves: within 10 days in mid-2025, OpenAI cut its most powerful model's price by 80% and moved ChatGPT Enterprise to prepaid credits, which he reads as protecting enterprise revenue while competing on API price. - **Ask what a high price pays for.** Dan Hockenmaier writes the competitor's business as an equation to estimate what each customer costs to acquire, then checks whether a high price funds work you can see. DoorDash charged restaurants more than Grubhub because it ran delivery itself. In SaaS he looks for a demo gate with a large sales headcount, discounts off list and a cheaper self-serve tier. If none turn up, the gap is margin, and that is the opening. ### [Pricing AI features and agents by the work they do]() Bill for a unit customers can understand and verify, check it against the cost of serving heavy users, and let people finish a real task before they pay. - **Define the billable event.** Kyle Poyar cites Intercom's Fin at $0.99 per resolution, where the definition of a resolution matters as much as the rate. Dan Hockenmaier would define an outcome customers can verify, then test adoption at different prices alongside margin after delivery costs. Where outcomes are hard to verify, Alvaro Morales says tasks and workflows can be practical billing proxies. - **Test how people buy.** At Lovable, Elena Verna's team charges for building with credits and has no per-user fee in self-serve. It tested one-off top-ups at 20% and 40% premiums: 20% raised engagement and bookings, 40% had too few buyers, and paid retention improved 7% after the change. - **Give buyers a budget.** Kyle would sell an agent to a department head as a monthly package of work, such as the accounts an AI SDR researches, so the buyer can compare it with hiring. Alvaro points to Notion's then-$20 AI add-on, a simple price that let people try the feature, and tracks cost per customer behind it. - **Let users finish a task first.** Lauryn Motamedi would give a limited trial allowance so a customer can complete a real task, drawing on Airtable's reverse trial. Elena says to check first that users can see what the paid agent does and what it costs, so low conversion isn't blamed on the price. ### [Moving existing customers to new pricing, one cohort at a time]() Simulate each account's first bill under the new model, prove the model on new buyers, then move existing customers in cohorts with notice and a fallback. - **Simulate the first bill.** Lauryn Motamedi would group accounts by the size of the bill change, the features they use and whether new limits would interrupt an active team. She says a customer facing a steep increase or a lost workflow deserves a direct conversation and a transition option before any automatic switch. She would watch downgrades and paid retention next to revenue. - **Prove it on new buyers first.** If new-customer cohorts are growing fast, Kyle Poyar would launch the new model there first while existing customers keep their plans, and give a change of billing unit more testing and lead time than a price change. Elena Verna would follow new-buyer cohorts for at least three to six months and pause the next cohort if churn, downgrades or lost engagement erase the projected gain. She plans for roughly 6% higher churn in a price-increase cohort, as a planning check. - **Roll out by cohort, with a fallback.** Alvaro Morales suggests versioning the plans, choosing when each cohort moves and telling each one plainly what changes, when and why. Kyle would give CSMs a recommended plan and a real fallback, such as 12 more months at the current price. For usage pricing, he would show each buyer a sample invoice from their recent usage and make sure the in-app usage view matches the invoice. - **Let renewals set the date.** If a renewal comes before the new-buyer test matures, Japna Sethi would renew on current terms, write the migration date into that agreement and avoid switching mid-term. She would give an account facing a large increase or a lost workflow a direct review before its next renewal. ### [Sizing a free trial to the time retained customers need]() Measure how long retained customers take to reach the paid value, treat 14 days as a starting test for simple self-serve products, and compare lengths on revenue and retention at the same cohort age. - **Time it to the paid value.** Ben Williams would count the days retained accounts take to reach a team-level milestone, including setup and inviting colleagues. Snyk's activation metric used a 30-day window, which he calls a measurement choice that doesn't prescribe a 30-day trial. - **Start at 14 days and test 30.** Elena Verna would start a simple self-serve product at 14 days as a hypothesis. Kyle Poyar found trials often convert at 2 to 3 times the freemium rate. He would compare 14 and 30 days on revenue per signup at a fixed cohort age, because conversion on the expiry date favors the shorter trial. - **Choose when the clock starts.** Elena would start access at signup if the paid feature helps during onboarding, or let users opt in once they have a workflow. For a collaborative product, Lauryn Motamedi would start premium access when the creator invites the first teammate and test 14 days from there. - **Plan for teams and slow calendars.** Japna Sethi would track self-serve trials apart from sales-assisted evaluations and let sales extend one that is progressing. Ben suggests a usage limit or a reverse trial when a fixed clock could run out before a colleague reviews the work, but says a reverse trial fits poorly without a useful free plan. ### [Where a paywall should stop people, and what it should offer]() People pay when the wall stops them partway through a job they care about and offers to sell them the thing they're stuck on. - **Limit the unit people value.** Japna Sethi would put the limit on the metric that tracks value, the way Dropbox's free plan limits storage. Elena Verna says free users should keep running into paid features so the wall never feels like an ambush, and sets a benchmark of at least 15% of pricing-page visitors clicking through to checkout. - **Sell a top-up for a burst.** Elena says that when Lovable added top-ups next to subscriptions, upgrades fell about 50% while total revenue rose 20%+ and paid retention improved 7%. Kyle Poyar would offer a top-up when a paying or clearly engaged user runs out mid-project for the first time and the next tier is far more than they need to finish the task, and push the plan once they hit the wall two or three cycles in a row. He points to Figma, whose pay-as-you-go AI credits cost 20% more than credits bought through a plan. - **Fix the copy and checkout first.** Lauryn Motamedi suggests paywall copy that says what the user will get done by upgrading, in place of a list of feature names. Working on trial conversion at Dropbox, she saw a ready buyer lost to an unsupported payment method, so she would break conversion down by country, payment method and plan before touching price. Japna passes on a tip to rename "overages" to "on-demand spend". - **In dev tools, find the buyer.** Ben Williams recalls a buyer who had the budget but couldn't create a Snyk API key without talking to sales. Snyk's self-serve channel, which let ready teams pay on the spot, grew 65% QoQ. He would judge readiness by usage across an account instead of one engineer, and suggests paywall copy an engineer can forward to whoever approves spend. ## Participants - [Kyle Poyar]() — Founder of Growth Unhinged. Former Operating Partner at OpenView - [Elena Verna]() — Leads growth at Lovable. Former SVP Growth at SurveyMonkey - [Alvaro Morales]() — Co-founder and CEO of Orb. Former Engineering Manager at Asana - [Dan Hockenmaier]() — Partner at Reforge. Former Chief Strategy Officer at Faire - [Japna Sethi]() — Director of Product at Atlassian. Former Head of Product-led Growth at Productboard - [Ben Williams]() — Runs The Product-Led Geek. Former VP of Products at Snyk - [Lauryn Motamedi]() — Former VP Growth and Product at Notion and Head of Growth at Airtable --- Copyright © 2026 Super Me Network, Inc. All rights reserved. | [Terms of Service](https://www.superme.ai/terms) | [Privacy Policy](https://www.superme.ai/privacy)