The SaaS Podcast - Real Lessons on Growing Profitable SaaS

He walked into his first pricing call asking for $100,000 and walked out with $10,000 a year. Shahar Azulay had never sold anything before co-founding groundcover, and the product he was selling had no user interface yet, just a sensor and some dashboards. He took it anyway, and still argues that was the right call.

Shahar explains why founders should close their first dozen customers at almost any price, how he published a per-host list price and then discounted it up to 70 percent, and how his first 50 to 100 customers came from his own network plus 10 to 15 LinkedIn messages a day.

Plus: the moment customers started ripping Datadog out on their own, before groundcover had built anything to help them do it.

Shahar Azulay is the co-founder and CEO of groundcover, an eBPF observability platform that lets engineering teams monitor production without changing their code. He has grown it to eight-figure ARR with more than 250 customers, competing head-on with Datadog and New Relic.

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He closed most of the deals himself. It took him ten years to stop. Ross Andrew Paquette bootstrapped Maropost to around $50 million in ARR, and for most of that run he was the discovery call, the demo and the follow-up. It worked well enough to take the business from $300,000 to $27 million in 28 months with six or seven people. Then the thing that built the company became the thing capping it.

Ross breaks down how he won accounts paying $10,000 a month on a five-minute response time rather than features, how two people signed brands like Rolling Stone and Mercedes off a conference floor, and why seven or eight experienced sales leaders all failed at Maropost before he changed what he hired for.

Plus: why he took investor money he did not need, and what it felt like to write a $37 million check three years later to buy it back.

Ross Andrew Paquette is the founder and CEO of Maropost, a commerce and marketing platform with roughly 300 people and 5,000 customers. He started it in 2011 out of his apartment while still selling Oracle ERP software full time, planning on ten customers and a quieter life.

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He never bought a keyword, never ran content marketing, and the big outbound sales force he tried did not work. Rodney Robinson still grew TabaPay to $100 million in revenue, almost entirely through inbound, on a single $2.5 million seed round that stayed the company's only outside money for nine years.

Rodney explains how he found a problem Mastercard could not solve, why he chased small fintechs instead of big logos, how his inbound marketing came from banks and the card networks rather than ads, and why he believes outbound sales no longer works in B2B.

Plus: the six-month lawsuit that cost TabaPay its sponsor bank, and what Rodney had personally put on the line to get that bank in the first place.

TabaPay is payment processing infrastructure that gives fintechs one API to move money instantly in both directions, and processes payments for companies like Dave. The company runs at $100 million in revenue with about 150 people, profitable, growing 35 to 40 percent a year. On the day this interview was recorded, Rodney announced a $155 million raise and the acquisition of a bank.

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He needed a big retailer's data to build the product. No big retailer gives data to a company with no product. Felix Hoffmann solved it sideways: 7Learnings sold a paid consulting project, kept the right to use the data, and built its predictive pricing product on top of it. Ten customers later it was at $1M ARR, and he had closed every one himself.

Felix explains why a demand forecasting product cannot start with a small customer, how he structured the first pilot as an A/B test so a retailer could hand over half its prices without betting the business, and what happened when the first run came back far too expensive.

Plus: how a pricing optimization company prices itself, and why he refuses success-based fees even though he can prove the uplift.

7Learnings is a Berlin company whose software forecasts demand for each product at each price, then sets the price that hits a retailer's goal. It is now at multiple seven figures in ARR with around 40 customers. Felix spent six years as a pricing consultant at Kearney and two years running price optimization at Zalando before founding it.

This episode is brought to you by:

🤖 Hobbes → Don't book a demo. Take one.

🔑 Key Lessons

• 🎯 Solve the data cold start by selling something else first: 7Learnings could not train a forecasting model without a large retailer's sales history, so it sold a paid consulting project and kept the right to use that dataset.
• 🤝 Shrink a scary ask into a reversible test: Retailers would not hand pricing to an algorithm outright, so 7Learnings ran an A/B test on half the assortment while the retailer's own team priced the rest.
• 📉 Pick an early customer who can survive a failure: The first live pricing run was badly wrong on high-priced products. It survived because the buyer had a big enough problem, no alternative, and understood they were working with a startup.
• 💰 Price high enough to lose some deals: His test is blunt. If nobody is walking away because you are too expensive, you are too cheap, especially for a complex product carrying real delivery cost.
• 🚀 Founder-led sales lasts longer than founders expect: Felix closed all ten customers behind the first $1M ARR himself, and stayed closely involved through the next forty, because handing off enterprise sales is genuinely hard.
• ⚡ Pick the technology after the problem, not before: Felix argues founders are all digging in the same technical space, and that decisions needing determinism, low cost and explainability should not be handed to an LLM.

Chapters

• Where the idea came from: Kearney, then Zalando
• The hardest part was finding co-founders
• The consulting project that funded the product
• Finding the first paying customer
• Structuring the first deal as an A/B test
• The first upload was a disaster
• How a pricing company prices itself
• Ten customers to $1M ARR
• The price matching objection
• Why LLMs don't belong in the pricing decision

Resources

• Full show notes: https://saasclub.io/494
• Join 5,000+ SaaS founders: https://saasclub.io/email

Nine months in. Close to zero customers. He was ready to hand the money back to investors. Rick Knudtson had already sold one company, so Workshop started with the idea he found interesting: an intranet. Customers kept telling him to fix email instead. The rebuild took 30 days and brought in 10 customers.

Rick explains why big enterprises cannot run internal comms on a cheap marketing tool, how a year of newsletters and ungated resources filled the pipeline before Workshop had anything to sell, and what changed when the founding team stopped defending its own idea and started listening to customers.

Plus: why Workshop dropped per-user fees for audience-based pricing, and how that changed the way customers expand into new departments.

Workshop is an internal communications software platform based in Omaha with around 140 employees and just under 1,000 customers, including Capgemini. It is five years old and past $10M ARR. Rick previously co-founded Flywheel, a WordPress hosting platform sold to WP Engine in 2019.

This episode is brought to you by:

🤖 Hobbes → Don't book a demo. Take one.

🔑 Key Lessons

• 👂 The signal was in the sales calls all along: Prospects named email as their biggest internal comms pain for nine months while Workshop kept building an intranet. Listening to customers only started once the ego from a previous exit got out of the way.
• 🎯 Finding product-market fit was obvious when it finally arrived: Nine months of selling the intranet earned about three customers. Thirty days on the email product brought ten. That gap told the team exactly where to go all in.
• 🧱 Pick a first problem you can ship fast: An intranet cannot be built iteratively, so feedback loops stall for months. Email analytics was small enough to ship in 30 days and grow into a wider platform.
• 🔒 Enterprise email is not a MailChimp problem: Security layers, IT governance, and getting a message into 100,000 inboxes in minutes are why large companies cannot run internal comms on an off-the-shelf marketing tool.
• 📣 Market for a year before you sell anything: Workshop launched a weekly newsletter on day one, now at 50,000 subscribers, alongside ungated resources and monthly webinars that grew from five attendees to five hundred.
• 💰 Audience-based pricing removes expansion friction: Workshop charges by employee audience size and by channel rather than per seat, so adding another department never triggers a procurement review or a new negotiation.
• 🧭 Write the mission first and the values later: A broad mission gave the team direction before the product existed. Values waited twelve months so they described what had actually kept the company alive.

Chapters

• How selling Flywheel led to the internal comms idea
• Writing the mission statement before the product
• The intranet bet and why it never found a through line
• Why enterprise email is harder than founders assume
• Building a newsletter and resource library before selling
• Nine months, near-zero customers, and the plan to return the money
• The bar conversation that led to the 30-day email rebuild
• Ten customers in 30 days and what product-market fit felt like
• Audience-based pricing and dropping per-seat fees
• Lightning round

Resources

• Full show notes: https://saasclub.io/493
• Join 5,000+ SaaS founders: https://saasclub.io/email

Ten thousand ads, all built by hand. Julius Körfgen left that grind to build Uplane, software that automates it, then sold to his first customers before writing a line of code. Uplane reached a million dollars in ARR in about six months.

Julius makes the case for selling before building: the cold outreach that got strangers on calls, the one-week sprint from discovery call to working demo, and why he refuses to run a free pilot. Without a dollar attached, he argues, you cannot tell a real business case from a polite conversation.

Plus: why Julius threw out per-seat pricing and now charges a share of ad spend, so Uplane only earns more when the customer's campaigns do better.

Uplane runs around twenty people across San Francisco and Berlin. Julius and his two co-founders raised their first funding round close to a year before the product existed, AG1 is a customer, and a project with Deutsche Bahn is underway.

This episode is brought to you by:

🤖 Hobbes → Don't book a demo. Take one.

🔑 Key Lessons

• 🤝 Sell before you build: Julius closed customers before writing a line of code. His discovery calls ended with a promise to return in a week with a solution, which forced both a real deadline and a real answer about demand.
• 🎯 Frame outreach as learning, not selling: His cold LinkedIn messages said he had just left his job and was exploring an idea, and asked for a few questions. People opened up about problems they would never have shared with a pitch.
• 💰 Never run a free pilot: Without a dollar attached you cannot tell a business case from a polite conversation. Julius has watched founders stay attached to an idea for months because nobody ever asked them to pay for it.
• ⚡ A week is long enough to build the thing you promised: Three founders and one week produced demos that won real customers. Scrappy was fine; fake was not, and he argues AI removes the excuse for a mock-up that does nothing.
• 💰 Align pricing with the outcome you claim: Uplane charges a fixed fee covering costs plus a variable share of ad spend. Julius says it makes the pitch easier, because he only earns more when the customer's campaigns do better.
• 🏢 Be reachable faster than an agency can be: Uplane answers customers within 120 seconds. Julius treats speed of response as the main structural advantage an early-stage company has over an incumbent agency.
• 🧠 Volume is not the constraint anymore: AI made producing ads nearly free, so the bottleneck moved to picking the roughly ten percent that perform. Companies pushing more output without connecting it to analytics are solving the wrong half.

Chapters

• Introduction
• What Uplane does and the problem it solves
• Ten thousand ads by hand
• Deciding to leave and build it
• The cold LinkedIn outreach that worked
• Standing out when everyone uses AI to personalise
• The first customer
• Why free pilots are a trap
• The one-week sprint from call to demo
• The 120-second response rule
• Throwing out per-seat pricing
• Attribution and charging on ad spend
• Guardrails and atomic content
• Lightning round

Resources

• Full show notes: https://saasclub.io/492
• Join 5,000+ SaaS founders: https://saasclub.io/email

Two founders. Two engineers. No product. Christian Lund closed one of the Big Four accounting firms as Templafy's first customer before the software existed, by selling a point of view instead of a demo. When that customer asked to start with ten people, he didn't say no. He said "yes, if."

Christian breaks down his approach to selling to enterprise without a product, why he answered every ten-person pilot request with "yes, if," and how fixing the proof criteria upfront turned trials into company-wide deals. He also explains why disqualifying prospects beats trying to convince them.

Templafy now runs at eight figures in revenue with a couple of hundred employees. Christian and his co-founder spun it out of an on-premise document business, raised their first funding round close to twelve months before the product existed, and are now rebuilding the company again for the AI shift.

This episode is brought to you by:

🤖 Hobbes → Don't book a demo. Take one.

🔑 Key Lessons

• 🏢 Sell your point of view before you sell product: During a technology shift, large enterprises buy people who understand the transition. Templafy won a Big Four firm on domain expertise alone, then co-created the product with them.
• 🤝 Answer pilot requests with "yes, if" rather than no: Christian never refused a proof of concept. He attached conditions on proof criteria, budget, timeline, and the rollout that follows, and walked away when they were missing.
• 🎯 Define what you are proving before any trial starts: A POC to see whether someone likes the product proves nothing. Agreeing the exact pass conditions upfront turns a trial into a decision rather than an experiment.
• ⚡ Setting the criteria shapes the competition: Because Templafy defined the proof points first, prospects who later ran competitive evaluations often used Templafy's criteria to score every vendor in the process.
• 🧠 Disqualify rather than convince: Christian's team filters for buyers who already accept the market is changing. He argues sales has nothing to do with convincing people, and that defending buyers cost too much time to pursue.
• 🚀 Land wide, then go deep: Enterprise security and procurement cost the same for ten users or a hundred thousand, so Templafy pushed for company-wide rollouts first and expanded into specific team use cases afterwards.
• 📉 Being too far ahead is a real cost: Templafy's AI messaging ran ahead of what buyers wanted. Christian's rule is that you can be fifteen percent ahead of the market but not eighty, or you lose the conversation entirely.

Chapters

• Introduction
• What Templafy does and the size of the business
• Seeing the cloud shift and spinning out of the on-premise business
• Two founders, two engineers, and a year of unlearning
• Selling thought leadership instead of product
• Targeting 800 people with specific messaging
• Raising funding twelve months before the product
• Why every enterprise customer is its own market
• The ten-person pilot problem
• "We didn't say no, we said yes if"
• Writing the criteria your competitors get scored on
• Disqualification as a sales strategy
• Resetting the company again for AI: fifteen percent ahead, not eighty
• Uphill skiers, downhill skiers, and the lightning round

Resources

• Full show notes: https://saasclub.io/491
• Join 5,000+ SaaS founders: https://saasclub.io/email

He spent two years building his own AI model. Over one launch weekend, a side experiment out-earned it. Eugene Cheah killed the original product and rebuilt Featherless AI around what customers actually paid for. He explains why he concluded people wanted these models more than they wanted his, and how he made the call to walk away from two years of work.

Eugene breaks down how GPU hot-swapping changed the unit economics of AI inference, why he charged a flat monthly rate while the rest of the AI industry billed per token, how stripping the technical explanation off the homepage kept improving conversion, and why Reddit and Discord drove his earliest customers.

Featherless AI now provides instant access to more than forty thousand open source AI models, on the way to a target of all three million on Hugging Face. It reached multiple seven figures in ARR within about a year, and has since raised a Series A led by Airbus Ventures and AMD Ventures.

🤖 Hobbes → Don't book a demo. Take one.

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Resources

Five years at $50K ARR. Ten failed projects. Lending the business money out of his own bank account. George Georgiadis came close to shutting Happier Leads down. Instead he broke through the revenue plateau and reached $1.5M ARR with zero employees.

George explains what moved the number: an end-to-end platform instead of a narrow point tool, cold email as his cheapest channel because he owns the mailboxes and the data, and running a SaaS with AI agents he built himself to handle support and bug fixing around the clock.

Plus: why he turned down a $1M offer to sell, and why he is hiring again after reaching seven figures alone.

Happier Leads identifies anonymous website visitors, qualifies them with AI, and engages them by email. George Georgiadis bootstrapped it from a $50,000 AppSumo campaign to $1.5M ARR with no outside capital.

This episode is brought to you by:

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🔑 Key Lessons

• 📉 A plateau is a depth problem, not an effort problem: George wore every hat for five years at $50K ARR and never went deep enough on one channel to make the unit economics work.
• 💰 Own the infrastructure your channel depends on: Building his own mailboxes and using the 175-million-contact database he already owned pushed cold email costs low enough to send millions profitably.
• 🎯 The point tool that felt like a mistake became the moat: Building identification, qualification, enrichment, and email sending into one platform took seven years, but no competitor covers the full path.
• 🤝 Cold email works on precision, not personalization theater: He picks the exact company and job title, keeps the message short, and withholds links until the prospect replies to protect deliverability.
• 🛠️ AI replaces a team only when the data lives in one place: Ripping out HubSpot, Intercom, and Pipedrive for self-built tools gave his AI brain the visibility it needs to fix bugs unattended.
• 🚀 Lifetime deals buy time, not revenue: The $50,000 AppSumo campaign got consumed by server and data costs within two years, but the reviews, word of mouth, and runway were worth more.
• 🧠 A solo operator owns a job, not a company: Even at $1.5M ARR with AI doing the heavy lifting, George is hiring because a business that stops when he stops cannot be sold.

Chapters

• Cold open: five years stuck, then $1.5M
• What Happier Leads does
• $1.5M ARR with zero employees, bootstrapped
• From Greece to London and 10 failed projects
• Where the Happier Leads idea came from
• Clearbit quoted $20,000 so he built his own
• Funding the product with AppSumo lifetime deals
• Buying the data, building the business on top
• The real cost and hidden upside of lifetime deals
• Five years stuck at $50K ARR
• 80% development, 20% marketing and sales
• Building end to end instead of a point tool
• Nearly quitting and lending the business his own money
• What finally changed: going deep on unit economics
• Cold email becomes the main acquisition channel
• What makes cold email work at scale
• Running the business with self-built AI agents
• Self-healing software and KPI monitoring
• Why he's hiring again after zero employees
• Lightning round

Resources

• Full show notes: https://saasclub.io/489
• Join 5,000+ SaaS founders: https://saasclub.io/email

Ron Hash bootstrapped Skimmer, software for pool service companies, to over $1 million in ARR and 1,500 customers with zero paid marketing, then sold it. His SaaS pricing was the engine: 50 cents per serviced pool with a $29 minimum, when every competitor charged per seat.

Ron shares how he validated the idea with one cold call, why his SaaS pricing aligned revenue with each customer's growth, how he cut churn from 6% to 2% by fixing onboarding, and why he never regretted the exit. His SaaS pricing chose a value metric close to the money instead of per-seat pricing, which made adding customers feel good and kept churn low.

Ron Hash built Skimmer with no prior SaaS experience and got it to 1,500 customers on SEO and word of mouth alone. That SaaS pricing model kept churn low and made the business acquirable; he sold to Unbundled Capital in 2020, after which the company raised $79 million and grew past 100 employees. He is now building QuickFax.

This episode is brought to you by:

🍎 Product Fruits → Book a demo tailored to your product

🔑 Key Lessons

• 💰 Usage-based SaaS pricing aligns revenue with customer success: Skimmer charged 50 cents per serviced pool, so a customer's bill rose only as their business grew, making them happy to pay more.
• 📉 Per-seat SaaS pricing punishes growth and drives churn: Ron priced on serviced pools instead of seats, so customers never hesitated to add users and the product became stickier across the whole team.
• 🎯 Validate with one real conversation, not a survey: Ron cold-called a single pool pro who said "the paper game is killing me," and that one honest answer was enough proof the problem was real.
• 🚀 SEO plus word of mouth can replace an ad budget: Ranking for "pool service software" and delighting customers got Skimmer to 1,500 users with zero paid marketing.
• 🔄 Churn is usually an onboarding problem: Ron cut churn from 6% to 2% with a simple onboarding flow that pulled new users to their first win, not by adding features.
• 🛠️ Build for the user doing the work: A fast, low-tap, offline-capable mobile app for field techs beat the web-based tools competitors built for office staff.

Chapters

• 00:00 50 cents a pool
• 00:30 Introduction
• 01:46 What Skimmer is and who it's for
• 03:46 Where the idea came from
• 07:05 Going all in on nights and weekends
• 08:16 Deciding what to build first
• 08:53 Welcome calls and learning from customers
• 11:36 The first customer and teaching himself SEO
• 13:32 Inbound vs the people he cold-called
• 15:00 The long slow ramp to 76 customers
• 16:39 Pricing at 50 cents a pool, not per seat
• 20:32 Explaining usage-based pricing to customers
• 22:41 Pen and paper vs software
• 25:19 Why Skimmer got so much traction
• 31:00 Cutting churn from 6% to 2%
• 36:25 The hard days of bootstrapping
• 39:17 Selling Skimmer
• 43:26 No regrets on the exit
• 44:53 QuickFax, his new project
• 47:56 The biggest lesson: solve small problems
• 49:35 Lightning round

Resources

• Full show notes: https://saasclub.io/488
• Join 5,000+ SaaS founders: https://saasclub.io/email