The SaaS Podcast - AI, Growth & Product-Market Fit for SaaS Founders

Omer Khan
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Every week, SaaS founders share how they found product-market fit, got their first customers, scaled to $1M+ ARR, and navigated pricing, sales, churn, and AI. Host Omer Khan has interviewed 500+ founders and coached 150+ through revenue milestones. Whether you're bootstrapping to $10K MRR or scaling past $1M+ ARR, The SaaS Podcast delivers proven growth strategies - not theory. Join 5,000+ founders at SaaS Club. New episodes weekly.

  1. 6D AGO

    AI Startup Hits $8.6M ARR With V0 MVP and €85 Pricing

    Hadn't coded in four years. No team. No idea. Marius Meiners launched his AI startup, Peec AI, with a V0 prototype built in 1.5 days and 8 letters of intent. 14 months later: $8.6M ARR, 55 employees, and a competitor with 5x his funding chasing enterprise. Marius shows how to validate an AI startup before coding, win the mid-market while competitors chase enterprise, and price your AI startup at €85 a month against incumbents charging €500+. He breaks down the V0 build, the LOI playbook, and how 20% of conversions now come from AI search itself. Peec AI is an AI startup that launched in February 2025 from Antler's Berlin cohort. Marius previously transitioned from professional esports through software engineering and venture capital at PwC. This episode is brought to you by: 🔍 Respona → Get featured in AI answers on ChatGPT and Google AI Overviews 🔑 Key Lessons 🚀 Use AI to compress validation timelines: Marius built the Peec AI MVP with V0 in 1.5 days and signed 8 letters of intent before writing production code. Modern AI tools turn idea-to-validation from months to days. 💰 Mid-market pricing wins when competitors fight enterprise: Peec priced at €85 a month while competitors charged €500+. AI search optimization at the mid-market price point captured 2,000 customers competitors ignored. 🎯 Letters of intent beat verbal validation: Asking "would you sign an LOI?" filters out polite enthusiasm. Marius signed 8 LOIs from a V0 prototype - real signal that the AI startup problem was acute enough to pay for. ⚡ Speed is the moat for AI-era SaaS: Idea in October 2024, launch in February 2025, $8.6M ARR by April 2026. In emerging categories, the founder who ships weekly outpaces the founder who polishes. 🧠 Scrappiness has a shelf life: Eating €2 canned food works at zero revenue. At $8.6M ARR with 55 employees, scrappiness becomes a bottleneck. Most founders break their company by clinging to it past its expiration date. 🚀 Build with AI search optimization in mind from day one: 20% of Peec's new conversions now come from AI search itself. Founders who do not structure content for AI assistants are leaving meaningful pipeline on the table. Chapters What Peec AI does From esports to PwC to startups ChatGPT search and the aha moment for an AI startup Validating ideas in days, not months Knowing AI search optimization was the bet How AI search optimization actually works Free GEO tactics for founders without budget Building the V0 prototype in 1.5 days Getting the first 8 letters of intent The pitch that won early adopters Advice for founders chasing early traction Pricing at €85 vs competitors at €500+ Scaling from LOIs to $8.6M ARR Lightning round Where to find Peec AI Resources Full show notes: https://saasclub.io/481 Join 5,000+ SaaS founders: https://saasclub.io/email

    36 min

  2. APR 28

    The 8-Figure Open Source SaaS Playbook

    He built a free tool as a lead magnet. Then customers started calling his cell phone, begging to pay for it. Ev Kontsevoy turned an open source SaaS side project into Teleport, now an 8-figure ARR business with 500+ customers. Founders will hear how a free GitHub project became an open source SaaS business worth eight figures - and why selling to the wrong buyer persona nearly capped growth. Ev reveals how he spotted the signal that his side project was more valuable than his flagship product, why shifting from engineers to VP buyers nearly tripled average deal size, and how open source monetization built trust closed-source competitors could never match. Teleport started as one component of Gravity, which was doing $4M ARR. COVID killed Gravity's pipeline while accelerating Teleport demand. The company now serves 500+ customers in 8-figure ARR, with AI agent identity emerging as a major growth driver. This episode is brought to you by: 🌎 ThreatLocker → Book a demo 🔍 Respona → Get featured in AI answers on ChatGPT and Google AI Overviews 🔑 Key Lessons 🛠️ Your open source SaaS lead magnet might be your real product: Teleport was built as free demand generation for Gravity, but customers wanted to pay for it instead - listen when the market tells you where the value is. 🎯 Ask customers to sell your product back to you: Ev discovered most customers used a tiny fraction of Teleport by asking them to describe it, revealing a buyer persona mismatch that was capping growth. 🤝 Match your sales motion to your buyer's expectations: Shifting from engineers to VPs of platform engineering nearly tripled average deal size because the new buyer expected a sales-led conversation. 🔄 Focus is not a pivot - it is subtraction: Ev stopped four of five things Gravitational was doing and concentrated entirely on Teleport, which was already generating equal revenue with fewer engineers. 💰 Price with confidence even when improvising: The first Teleport enterprise deal closed at $25,000/year because Ev said "thousand" instead of "hundred" on a cold call - then built the enterprise product around real customer requests. 🚀 Open source SaaS builds trust faster for security products: Public code audits and community reviews gave Teleport credibility closed-source competitors could not match - a natural open source lead generation advantage. 🧠 Find startup ideas in the support queue: Ev found both Mailgun and Gravitational by listening to customer problems at his day job. This open source business model started from real pain, not brainstorming. Chapters What Teleport does and the infrastructure identity problem Founding Mailgun and the Rackspace acquisition How Teleport started as a free open source SaaS component COVID kills Gravity pipeline and accelerates Teleport demand The first enterprise deal - improvised on a cold call Why open source SaaS builds trust for security products Discovering they were selling to the wrong buyer persona Shifting from engineers to VPs - 3x average deal size AI as COVID 2.0 - identity for AI agents Lightning round Resources Full show notes: https://saasclub.io/480 Join 5,000+ SaaS founders: https://saasclub.io/email

    1h 9m

  3. APR 16

    The Risky AI SaaS Rebuild That Broke a $2M ARR Ceiling

    Most SaaS onboarding is terrible - rigid, pushy, and forgettable. Karel Papik spent 15 years designing video games before he looked at B2B software and thought: this is hopeless. He co-founded Product Fruits, a digital adoption platform that now serves over 1,300 paying customers. Founders will hear how gaming psychology transformed their SaaS onboarding and helped them break through the $2M ARR ceiling. Karel shares how Product Fruits grew from 6 customers to $50K MRR in 12 months using PPC as the sole acquisition channel, why their product-led growth strategy stopped working at $2M ARR, and how rebuilding the entire platform around AI turned their SaaS onboarding tool into something competitors can't match. Plus the "diamond axe" technique from gaming that drove 24-25% free trial conversion. Product Fruits is based in Prague, Czech Republic, with 25 team members and over 1,300 paying customers including KPMG, universities, and stock exchanges. The company has raised venture funding from Lighthouse Ventures and Reflex Capital, with the US as its biggest market. This episode is brought to you by: 🌎 ThreatLocker → Book a demo 🔍 Respona → Get featured in AI answers on ChatGPT and Google AI Overviews 🔑 Key Lessons 🎮 Gaming psychology transforms SaaS onboarding: Karel applied the "diamond axe" technique from video games - give users the premium experience free, let them feel the value, then ask them to pay. Product Fruits used this to achieve 24-25% free trial conversion. 🎯 Test your biggest market from day one: Product Fruits targeted the US market immediately from Czech Republic instead of starting locally. Karel wanted to know as fast as possible if they could compete globally - and if not, fail fast rather than waste years on small markets. 💰 PPC works when you have the right operator: Most founders say PPC doesn't work, but Product Fruits scaled it to $1.5M/year with 8-9 month payback. The difference was hiring a PPC expert and optimizing landing pages rather than treating ads as a side project. 📉 PLG breaks down as onboarding products get complex: Product Fruits hit a growth wall at $2M ARR when the platform outgrew self-serve. Customers could not discover capabilities on their own, forcing a shift to sales-assisted growth with bigger tickets. 🐯 Rebuild before the decline forces your hand: Karel told investors he was pausing the current product to rebuild around AI - before revenue declined. Investors backed the move within 20 minutes, seeing it as a sign of a winning team rather than a distress signal. 🤖 Ship AI that solves real problems, not investor checkboxes: Product Fruits' AI copilot resolves 80% of support tickets without humans. Karel's test for any AI feature: can we sell it today? If it does not deliver measurable value, it does not ship. 🧠 Stop talking to customers when you need to dream: Karel's contrarian take - over-relying on customer feedback produces small improvements but blocks breakthrough innovation. Customers do not know what is possible in your domain. Sometimes you need to disconnect and imagine the future. Chapters Introduction What Product Fruits does and who it serves 1,300 customers across industries - not just SaaS Riding the tiger - the company philosophy Karel's video game background and meeting co-founder Ladislav Gaming psychology applied to SaaS onboarding The diamond axe technique - let users feel value before paying Growing from 6 to 1,300 customers with PPC Why PPC worked when most founders say it doesn't Pricing strategy and the "too cheap" problem PLG hitting a wall at $2M ARR The AI pivot - rebuilding the platform from scratch How investors responded to the rebuild decision AI features that actually deliver value 80% of support tickets resolved by AI What AI feature they decided NOT to build Lightning round Resources Full show notes: https://saasclub.io/479 Join 5,000+ SaaS founders: https://saasclub.io/email

    55 min

  4. APR 9

    Finding Product-Market Fit After 3 Years of Failed Ideas

    Three years. Zero traction. Then product-market fit hit - twice. Girish Redekar taught himself to code at 28 and spent years on failed ideas before B2B product-market fit clicked with RecruiterBox. Customers endured a broken PayPal payment hack just to keep using the product. He bootstrapped to 2,500+ customers, sold it, then found product-market fit again with Sprinto by paying for 10 audits before writing code. Girish shares how he validated demand using The Mom Test, why 17 of 20 GTM channels failed, and the 3 that drove Sprinto to 8-figure ARR with 3,000+ customers. Sprinto is an autonomous compliance platform with $32M raised and 350 people. AI is changing the business from three directions - product, customer operations, and external threats. This episode is brought to you by: 🌎 ThreatLocker → Book a demo 💖 Gearheart → Book a free consult and get the first 20 hours free 🔑 Key Lessons 🎯 B2B product-market fit shows up in customer behavior, not metrics: RecruiterBox knew it had something real when customers kept paying through a broken PayPal system with daily-depleting credits. The pain they tolerated was the signal. 💰 Sell a profitable business when you become the bottleneck: Girish sold RecruiterBox at single-digit millions ARR because growth had plateaued and the founders were not the right people to scale it further. 🔄 Eliminate product risk before writing code: Sprinto's biggest question was whether a consulting service could become software. Ten paid audits answered that before a line of code was written. 🚀 Harvest existing demand instead of creating it: Sprinto's first customers came from founder Slack groups, VC portfolio programs, and Google - places where people already looked for answers. 📉 Expect 85% of your GTM channels to fail: Girish tried 20 channels and 17 did not work. Partner co-selling and conferences only started working after Sprinto had brand recognition. 🧠 Founder-product fit matters as much as product-market fit: Girish passed on a WordPress competitor because the GTM required developer evangelism - not a strength. Pick the right problem for your skills. 🛠️ AI is hitting compliance from three directions: Product capabilities, customers running AI internally needing governance, and attackers using AI for sophisticated threats - creating compounding demand. Chapters What Sprinto does and key business metrics Failed ideas before RecruiterBox What kept them going through 2-3 years of no traction The PayPal payment hack that proved product-market fit Why they sold a profitable, growing business Finding product-market fit the second time with The Mom Test Paying for 10 audits to validate the product Product risk vs market risk framework 20 GTM channels tried, 3 worked How AI impacts the business from three directions Resources Full show notes: https://saasclub.io/478 Join 5,000+ SaaS founders: https://saasclub.io/email

    54 min

  5. APR 2

    Bootstrapped SaaS Growth When AI Took Over the Market

    His competitors have raised hundreds of millions. ChatGPT can do the basics of what his product does. Sylvestre Dupont's entire company is six people. His competitive differentiation strategy - that most businesses want something simple that works in minutes, not enterprise complexity - is what keeps Parseur alive and growing 60% year over year. Founders will hear how Dupont rebuilt from rule-based to AI-powered parsing while bootstrapped, why simplicity is a stronger competitive advantage than features or funding, and how a tiny team's SaaS positioning bet is beating players with 100x the resources. Parseur generates 7-figure ARR with 1,000 customers in 70+ countries. Competitive differentiation through simplicity keeps them growing - bootstrapped, six people, 100% founder-owned. This episode is brought to you by: 💖 Gearheart → Book a free consult and get the first 20 hours free 🌎 ThreatLocker → Book a demo 🔑 Key Lessons 🎯 Competitive differentiation through simplicity beats enterprise complexity: Parseur's 10-minute self-serve setup wins against competitors requiring sales calls and hundreds of millions in funding. 🧠 AI commoditizes features, not end-to-end solutions: ChatGPT can parse one PDF, but it can't handle pre-processing, routing, compliance, and integration at scale - that's where the real product value lives. 💰 You can fund an AI rebuild from revenue, not investors: Parseur rebuilt from rule-based to AI-powered parsing using customer revenue, keeping 100% ownership and avoiding dilution. 📉 Launch failures don't kill the product - bad positioning does: Sylvestre launched to crickets, dropped price 80%, and rebuilt his approach from scratch. The product was fine - the go-to-market was the problem. 🚀 Integration partnerships pre-qualify customers: Parseur's Zapier connector converted at 20-30% because those users were already automation buyers looking to connect tools. 🎯 Horizontal SaaS works when your competitive differentiation is use-case specific: Parseur is generic, but their SEO targets individual use cases - making them appear vertical to each customer segment. 🤝 Genuine community engagement beats marketing at the start: Answering real questions on Quora without being promotional built trust and attracted Parseur's earliest paying users. Chapters Introduction and quote - keep it simple, stupid What Parseur does - automating data extraction from documents Business overview - 7-figure ARR, 1000 customers, 6 people Origin story - from travel map side project to SaaS The failed launch - a year of building, zero marketing Finding first customers on Quora Pricing mistake - dropping from $49 to $9 How simplicity became the competitive differentiation moat The Zapier integration that converted at 20-30% SEO as the 95% acquisition engine AI disruption - rebuilding from rule-based to AI-powered Managing AI costs on a bootstrapped budget Standing out against VC-funded players with simplicity Why horizontal SaaS worked instead of going vertical Adapting for the AI search era Lightning round Resources Full show notes: https://saasclub.io/477 Join 5,000+ SaaS founders: https://saasclub.io/email

    43 min

  6. MAR 26

    Vertical SaaS: $0 to $10M ARR With Flat Pricing for Everyone

    Five years to the first million. Zero dollars raised. NFL teams pay the same price as high school teams. Hewitt Tomlin built TeamBuildr into a $10M ARR vertical SaaS company by focusing on one job function and refusing to charge enterprise customers more. Founders will hear why flat pricing drove more growth than premium tiers ever could. Hewitt shares how a single conversation with a college strength coach pivoted TeamBuildr from a social app to industry-specific SaaS, why founders who plateau at $500K ARR have a product-market fit problem, and how building for a job function instead of a market segment unlocked every customer from high schools to the NFL. Plus: Hewitt's take on why he won't build AI features until his customers ask for them - even as his biggest competitor bets on replacing coaches with AI entirely. TeamBuildr has 45 employees, has never raised funding, and still operates on the same co-founder agreement from 2012. This episode is brought to you by: 💖 Gearheart → Book a free consult and get the first 20 hours free 🌎 ThreatLocker → Book a demo 🔑 Key Lessons 🏢 Build vertical SaaS around a job function, not a market segment: TeamBuildr focused on the strength coaching workflow rather than targeting colleges or pro teams separately. This unlocked every segment from high schools to NFL teams with a single product. 💰 Flat pricing can drive niche SaaS growth through social proof: Hewitt charges pro teams the same as high schools, trading premium revenue for NFL logos that validate TeamBuildr to the volume market. As a bootstrapped company, this was more pragmatic than building enterprise tiers. 🎯 Stalling at $500K ARR signals a product-market fit problem: Hewitt advises that founders putting in full-time effort but plateauing for consecutive years should stop tweaking their go-to-market and reexamine whether their product actually solves what the market needs. 🤝 Treat early users as partners, not beta testers: Hewitt didn't send logins and wait for feedback. He showed up at conferences, called coaches personally, and built relationships. His first customer Dr. Steve Smith is still someone he stays in touch with 13 years later. 🧠 Listen to what customers want, not what they say they want: Customers describe missing features because they can't articulate the outcome they need. Hewitt's job is to peel back the request and identify the real workflow improvement, then decide what to build independently. 🛠️ Don't build AI features for the sake of building them in vertical software: While competitor Volt bets on AI replacing coaches, Hewitt waits for actual customer demand. He uses AI internally for developer productivity but won't ship customer-facing AI without conviction it enhances the profession. 🚀 Inbound marketing gets stronger as your niche SaaS customer base grows: Hewitt transitioned from cold calling to inbound by telling customer stories. Following HubSpot's principle that the best inbound originates with customers, a growing base made content and social proof more potent over time. Chapters What TeamBuildr does and who it's for How the idea started as a social app in college Revenue, team size, and business structure today Pivoting from athletes to coaches The conversation that changed everything Building the MVP and making the first dollar Getting free users to actually use the product Listening to what customers really want Competing with Excel in a market that didn't know SaaS existed Five years to the first million in ARR How Hewitt knew he had product-market fit Outbound vs inbound on the way to $1M Why half the customers are high schools Charging NFL teams the same as high school teams Building vertical SaaS around AI without replacing coaches Why customers aren't asking for AI yet Lightning round Resources Full show notes: https://saasclub.io/476 Join 5,000+ SaaS founders: https://saasclub.io/email

    50 min

  7. MAR 19

    SaaS Product-Market Fit: Zero Code to 8-Figure ARR

    Sarah Ahmad offered her first product for free during COVID. Nobody signed up. Her next company hit 10,000 customers and 8-figure ARR. The difference was SaaS product-market fit - validated before writing a single line of code. Sarah shares how she and her co-founder tested demand with a landing page in the YC community, signed 100 paying customers using Google Drive and a Stripe link, and built Stable into the leading AI-powered virtual mailbox for businesses. She also explains why the SEO playbook that built the company stopped working and what replaced it. Stable serves over 10,000 companies - from solopreneurs to enterprises like DoorDash, GitLab, and Realty Income - with 50-60 employees and operations across 20+ US locations. This episode is brought to you by: 🌎 ThreatLocker → Book a demo 💖 Gearheart → Book a free consult and get the first 20 hours free 🔑 Key Lessons 🎯 Test SaaS product-market fit before writing code: Sarah's first startup Mistro failed because she built the full product before validating demand. With Stable, she validated with a landing page and manual operations - signing 100 paying customers before writing any software. 📉 Zero signups at zero price means no product-market fit: During COVID, Mistro couldn't get users even for free. That signal was clearer than any metric - if people won't use it for nothing, the problem isn't pricing, it's relevance. 🛠️ Use embarrassingly manual MVPs for market validation: Stable's first version was Google Drive, Zoom, and Stripe. Customers sent IDs via email. It was embarrassing, but it captured real demand while the team figured out what to build. 💰 Spend enough on paid ads to get real signal: Sarah's team spent only a few hundred dollars per week on ads - not enough to know if the channel worked. She now recommends spending thousands to saturate high-intent searches before optimizing. 🚀 Word of mouth scales when you solve a real pain point: Stable reached 1,000 customers before hiring anyone for growth, with a team of just 6-7 people at $1M ARR. Genuine product-market fit drove organic referrals without a marketing budget. 🤝 Compensate for a rough product with exceptional customer experience: Sarah and her co-founder personally onboarded every early customer via Zoom and handled all support. People forgive a rough product when you solve a real problem and show up for them. 🏢 Physical operations create a moat AI can't easily replicate: Stable's processing centers and logistics network across 20+ locations give it a defensibility layer that pure software companies don't have. Chapters Introduction First startup Mistro and why it failed Discovering the virtual mailbox opportunity Validating demand with a landing page The no-code MVP with Google Drive and Stripe How Stable differentiated from legacy incumbents Getting to 1,000 customers with a team of 6 The paid ads mistake most early founders make From manual operations to building software How AI is changing the product and industry Testing SaaS product-market fit versus building blind Shifting from product builder to CEO Resources Full show notes: https://saasclub.io/475 Join 5,000+ SaaS founders: https://saasclub.io/email

    39 min

  8. MAR 12

    SaaS Distribution Channel: Partner Deals to $100M ARR

    100 restaurants. Every order processed manually. Zero lines of code. Zhong Xu built Deliverect by turning integration partners into a SaaS distribution channel that scaled his product 10x faster than direct sales. Here's how he reached 80,000 restaurants and nearly $100M ARR through partnerships instead of cold outreach. Zhong shares why he launched with a Wizard of Oz MVP, how he convinced competing software companies to distribute his product, and why he opened 10 offices in a single quarter during COVID to block local incumbents before they could form. Plus: Zhong's take on why AI might turn his platform into commodity infrastructure - and his strategy to stay ahead. Deliverect connects delivery platforms like Uber Eats and DoorDash to restaurant systems across 50 countries. Zhong previously co-founded a restaurant software company that merged with Lightspeed, which IPO'd in 2019. This episode is brought to you by: 💖 Gearheart → Book a free consult and get the first 20 hours free 🌎 ThreatLocker → Book a demo 🔑 Key Lessons 🚀 Build a SaaS distribution channel through integration partnerships: Zhong partnered with 10+ software companies who each brought 100 restaurants monthly, reaching 80,000 locations across 50 countries faster than any direct sales team could. 🛠️ Launch with a Wizard of Oz MVP before writing code: Deliverect signed up 100 restaurants and manually processed every order before building anything, proving demand without wasting months on unvalidated features. 🤝 Attribute leads to distribution partners to avoid conflict: Zhong always credited partners for deals regardless of how customers arrived, eliminating the channel conflict that destroys most partnership-driven growth programs. ⚡ Enter every market before local incumbents emerge: Deliverect opened 10 offices in one quarter during COVID, betting that being number 1 or 2 early was cheaper than displacing entrenched local competitors later. 💰 Always charge early customers - free users give less feedback: Zhong found that non-paying customers feel guilty requesting help and stay silent, while even $50/month customers actively engage and provide honest product feedback. 🧠 Deep domain expertise creates unfair SaaS distribution advantages: Zhong's 12+ years in restaurant tech meant he had every partner CEO's phone number at launch, turning cold outreach into warm partnership conversations. 🎯 Build the intelligence layer before you become commodity infrastructure: Deliverect is racing to add AI-powered menu optimization and agent commerce because connectivity alone is replicable, but owning the restaurant intelligence layer is a defensible moat. Chapters Introduction What Deliverect does and how it works 80,000 restaurants and approaching $100M ARR How Zhong's father inspired his entrepreneurial journey Building one of the first tablet-based restaurant platforms Where the idea for Deliverect came from Why four co-founders and why distribution beats product The Wizard of Oz MVP - manual orders for 100 restaurants Resources Full show notes: https://saasclub.io/474 Join 5,000+ SaaS founders: https://saasclub.io/email

    50 min
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4.8
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Every week, SaaS founders share how they found product-market fit, got their first customers, scaled to $1M+ ARR, and navigated pricing, sales, churn, and AI. Host Omer Khan has interviewed 500+ founders and coached 150+ through revenue milestones. Whether you're bootstrapping to $10K MRR or scaling past $1M+ ARR, The SaaS Podcast delivers proven growth strategies - not theory. Join 5,000+ founders at SaaS Club. New episodes weekly.

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