I Tracked Every SaaS That Went From $0 to $10K MRR Without a Landing Page. They All Used the Same Distribution Trick.

There's a class of software product that reaches $10K in monthly recurring revenue before the founder even bothers to register a domain name.

No landing page. No Product Hunt launch. No Twitter thread announcing the build in public. Just revenue, growing quietly inside someone else's ecosystem.

I started noticing this pattern when I was analyzing SaaS tools doing $1M+ ARR with under 3 employees. A surprising number of them didn't start with a website. They started as a plugin, an integration, a bot, a template, or an extension — living inside a platform where customers were already spending money and already frustrated.

The more I dug, the more I realized this wasn't a fluke. It's a repeatable distribution strategy, and it's arguably the single best way to build a profitable micro-SaaS in 2025 and 2026.

Let me walk you through exactly how it works.

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The Core Insight: Borrow Someone Else's Traffic, Then Graduate

Most founders think about distribution backwards. They build a standalone product, put up a landing page, then try to figure out how to get people to visit it. That's the hard path. You're competing for attention in a world drowning in software.

The founders who reach $10K MRR fastest do something different. They identify a platform where their target customer already lives — Shopify, Figma, Notion, Slack, VS Code, WordPress, Zapier, Chrome, Excel, even Discord — and they build directly inside that platform's ecosystem.

This works for a specific structural reason: marketplace algorithms reward new entrants that solve unmet needs.

The Shopify App Store, the Chrome Web Store, the Figma Community, the VS Code Marketplace — these are all search engines. And unlike Google, they're search engines with relatively low competition and high purchase intent. Someone searching the Shopify App Store for "inventory sync" is a paying merchant with a problem they need solved today. Someone searching Google for "inventory management software" might be a student writing a report.

The distribution is built into the platform. The trust is inherited from the platform. The payment infrastructure often already exists. And the feedback loop — install, use, review, rank higher — compounds in your favor if you ship something genuinely useful.

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The Five Platform Categories Where This Works Best

After analyzing dozens of products that followed this pattern, the platform ecosystems break into five categories, each with different dynamics and different revenue ceilings.

1. E-Commerce Platform Extensions (Shopify, WooCommerce, BigCommerce)

This is the most proven category. The Shopify App Store alone has created hundreds of millions in annual revenue for third-party developers, and the dynamics are almost absurdly favorable for new entrants.

Why? Because Shopify merchants are already paying $39-$399/month for their store. They think in terms of monthly software costs. They understand recurring billing. And they have an immediate, measurable relationship between "tool I install" and "revenue I generate." If your app helps them recover abandoned carts, optimize shipping costs, or manage inventory across channels, the ROI calculation is obvious.

The sweet spot for new entrants right now is in the AI-adjacent layer. Merchants need tools that automatically generate product descriptions, optimize product images, personalize email flows based on browsing behavior, or dynamically adjust pricing based on competitor data. These are problems that were technically impossible to solve well two years ago and are now buildable by a solo developer using Claude or GPT-4 APIs.

A Shopify app that uses AI to automatically generate SEO-optimized product descriptions in a merchant's brand voice — trained on their existing copy — could realistically charge $29-$79/month and reach $10K MRR within 6 months through organic App Store traffic alone. The search volume for "product description generator" inside the Shopify ecosystem is substantial, and most existing solutions are template-based, not AI-native.

The key metric to watch: Shopify has over 2 million active merchants. Even capturing 0.01% of that base at $49/month puts you at $10K MRR.

2. Developer Tool Ecosystems (VS Code, GitHub, npm)

Developer tools have a unique distribution advantage: developers talk to each other. A genuinely useful VS Code extension gets shared in Slack channels, mentioned in blog posts, and recommended in Stack Overflow answers. The word-of-mouth coefficient is higher than almost any other category.

The VS Code Marketplace is particularly interesting because it has over 40,000 extensions but relatively few that charge money. Most developer tools are free, which means the ones that do charge — and deliver clear value — face almost no pricing competition.

The emerging opportunity here is in AI-powered code review and documentation. Extensions that automatically generate documentation from code, flag security vulnerabilities using LLMs, or provide context-aware refactoring suggestions are seeing rapid adoption. The willingness to pay is real: development teams already spend $50-$200 per seat per month on tools like GitHub Copilot, Linear, and various CI/CD services.

A VS Code extension that acts as an AI-powered "code explainer" for legacy codebases — you highlight a function and it generates a plain-English explanation, identifies potential bugs, and suggests modern alternatives — could charge $15-$30/month per developer. Get 400-700 paying users through marketplace discovery and you're at $10K MRR.

GitHub Actions and GitHub Apps are another underexplored vector. There are entire categories of CI/CD workflow — like automated dependency license auditing, or AI-generated changelog creation — where the existing solutions are either enterprise-priced or nonexistent.

3. Design and Creative Tool Plugins (Figma, Canva, Adobe)

Figma's plugin ecosystem is one of the fastest-growing distribution channels for micro-SaaS, and it's still early enough that a well-built plugin can rank on the first page of Figma Community search results within weeks.

The dynamic here is similar to Shopify: designers are already paying for Figma ($15-$75/month per editor), they're comfortable with software subscriptions, and they have specific workflow bottlenecks that a plugin can solve in seconds.

The AI angle is massive. Designers are spending hours on tasks that AI can now handle: generating placeholder content that actually matches the project context, creating color palette variations, resizing designs for multiple platforms, converting wireframes to high-fidelity mockups, or generating realistic user avatars and product photos.

A Figma plugin that uses AI to automatically generate realistic, contextually appropriate placeholder content — not lorem ipsum, but actual copy that matches the design's industry and tone — could charge $12-$25/month. Designers use placeholder content in every single project. The frequency of use is daily, which is the strongest predictor of willingness to pay for a tool.

Canva's app ecosystem is newer and less saturated, which makes it an even more interesting opportunity. Canva has over 170 million monthly active users, many of whom are non-designers (marketers, small business owners, social media managers) who are willing to pay for anything that makes their designs look more professional with less effort.

4. Productivity Platform Add-ons (Notion, Airtable, Google Workspace, Slack)

Notion's API and integration ecosystem has created a fascinating micro-SaaS category. Products like Notion-based CRMs, project trackers, and content calendars have reached significant revenue by essentially being a better interface layer on top of Notion's database.

The pattern that works: find a workflow that people are already doing inside Notion (or Airtable, or Google Sheets) with ugly, manual processes, and build a dedicated tool that syncs with their existing data but provides a purpose-built interface.

I've written about SaaS tools that replaced spreadsheets and crossed $1M ARR, and the dynamic is identical here — except instead of replacing the spreadsheet entirely, you're augmenting it. You're the specialized front-end for a general-purpose back-end.

Slack bots are another vector that's quietly producing revenue. A Slack bot that monitors specific channels for customer feedback, categorizes it using AI, and generates weekly insight reports could charge $49-$149/month per workspace. Product teams are desperate for this. They know their customers are talking in community Slack channels and Discord servers, but nobody has time to read thousands of messages a day.

Google Workspace add-ons are the most underrated distribution channel on this list. Google Workspace has over 3 billion users. The Google Workspace Marketplace gets meaningful organic traffic, and the add-ons that rank well — particularly for Gmail and Google Sheets — can generate substantial recurring revenue with zero marketing spend.

An AI-powered Google Sheets add-on that automatically cleans, categorizes, and enriches messy data (think: a list of company names that need to be matched to industries, employee counts, and domains) could charge $19-$49/month and reach $10K MRR purely through Marketplace discovery. Data cleaning is one of the most universally hated tasks in business, and most people doing it are stuck in Google Sheets.

5. Browser Extensions (Chrome Web Store)

Chrome extensions are the OG platform distribution play, and they're experiencing a renaissance thanks to AI. The Chrome Web Store gets billions of visits per year, and extensions that solve specific, frequent pain points can accumulate tens of thousands of users through organic search alone.

The monetization challenge with Chrome extensions has historically been conversion rate — free users don't want to pay. But the new generation of AI-powered extensions has changed this dynamic because the value delivery is immediate and obvious. An extension that rewrites your emails in a more professional tone, or summarizes long articles, or extracts structured data from any webpage — these provide value that users can see in seconds.

The most interesting current opportunity is in what I'd call "workflow-specific AI extractors." Generic AI summarizers are a commodity. But an extension built specifically for recruiters that extracts candidate information from LinkedIn profiles and formats it into their ATS — that's a $29/month tool that recruiters will pay for without blinking. An extension built specifically for sales reps that extracts company information from any website and auto-populates their CRM — same story.

The key insight: the same underlying AI capability (extraction and formatting) becomes dramatically more valuable when it's tailored to a specific workflow and integrated into the tools that workflow already uses.

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The Graduation Strategy: From Plugin to Platform

The smartest founders using this distribution trick don't stay inside the platform forever. They use the platform as a customer acquisition channel, then gradually build standalone value that justifies a direct relationship.

The playbook looks like this:

Months 1-6: Build inside the platform. Get to $5K-$15K MRR through marketplace organic traffic. Collect email addresses. Talk to every customer. Understand their workflow deeply.

Months 6-12: Launch a web dashboard that provides additional functionality beyond what the plugin can offer. Analytics, team management, advanced settings, integrations with other tools. The plugin still works, but the dashboard becomes the primary interface for power users.

Months 12-18: The standalone product now has enough value that new customers discover it directly — through SEO, word of mouth, or content marketing. The original platform integration becomes one of many integrations, not the entire product.

This is exactly how many of the SaaS tools that grew 10x faster than competitors got their initial traction. They didn't compete head-on for attention. They embedded themselves where attention already existed.

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Why This Works Better in 2025-2026 Than Ever Before

Three things have converged to make this strategy more powerful right now than at any previous point:

AI makes the plugin dramatically more valuable. Two years ago, a Shopify app that "optimized product descriptions" meant it ran them through a template. Today, it means it rewrites them using an LLM that understands SEO, brand voice, and conversion psychology. The jump in value delivery is enormous, which means the willingness to pay has jumped too.

AI makes the plugin dramatically faster to build. Using tools like Claude Code or Cursor, a solo developer can build a functional Shopify app, Chrome extension, or Figma plugin in days, not months. The barrier to entry has collapsed, but — counterintuitively — this benefits first movers because most developers are still building standalone apps and ignoring platform ecosystems.

Platform ecosystems are actively recruiting developers. Shopify, Figma, Notion, and others are investing heavily in their app/plugin ecosystems. They're improving APIs, reducing review times, promoting third-party tools, and in some cases offering revenue-sharing incentives. The platforms want you to build on them because it makes their product stickier.

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The Six Ideas I'd Build Tomorrow Using This Strategy

Let me get specific. If I were starting a micro-SaaS today with the goal of reaching $10K MRR as fast as possible, these are the platform-embedded products I'd consider:

1. AI Brand Voice Engine for Shopify — An app that learns a merchant's brand voice from their existing product descriptions, emails, and About page, then generates all future copy (product descriptions, collection pages, meta descriptions, email campaigns) in that voice. Charge $49/month. There are generic AI writing tools on the Shopify App Store, but none that truly learn and maintain a consistent brand voice across all content types.

2. Figma-to-Email Converter — A Figma plugin that takes any email design and exports it as production-ready, responsive HTML email code that works across all major email clients. This is a notoriously painful process that designers and developers fight about constantly. Charge $15/month per user. The total addressable market inside Figma's user base is massive.

3. AI Data Analyst for Google Sheets — A Google Workspace add-on where you highlight a range of data, ask a question in plain English ("What's the trend in Q3 sales by region?"), and it generates the analysis, chart, and written summary directly in your sheet. Charge $29/month. Every marketing team, finance team, and operations team lives in Google Sheets and hates building pivot tables.

4. Slack Customer Intelligence Bot — A Slack bot that monitors your community Slack or Discord, uses AI to categorize every message (feature request, bug report, praise, churn signal, competitor mention), and delivers a weekly digest with trends and recommended actions. Charge $99/month per workspace. Product teams at B2B SaaS companies would pay for this immediately. I track opportunities like this at SaasOpportunities — the demand signals for customer intelligence tools are everywhere right now.

5. AI Compliance Checker for WordPress — A WordPress plugin that continuously scans a site for accessibility violations (WCAG), privacy policy issues (GDPR/CCPA), and broken legal requirements, then provides one-click fixes. Charge $19/month. With the wave of ADA website lawsuits and increasing privacy regulation, every small business with a WordPress site needs this but doesn't know it yet. The regulatory shifts creating SaaS opportunities are only accelerating.

6. Context-Aware Code Review Bot for GitHub — A GitHub App that automatically reviews every pull request, but unlike generic AI code reviewers, it learns your team's specific coding standards, architectural patterns, and past review comments. Over time, it reviews code the way your senior engineer would. Charge $25/month per developer. The existing AI code review tools are generic. The opportunity is in personalization.

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How to Pick Your Platform: A Decision Framework

If you're sold on this approach but unsure which platform to build on, here's how to decide:

Follow the money. Pick platforms where users are already paying for software. Shopify merchants pay for apps. Figma teams pay for plugins. Free platforms (like Reddit or Twitter) have users who are much harder to monetize.

Check marketplace search volume. Most platform marketplaces have search functionality. Type in the problem you want to solve and see how many results come up. Fewer than 10 results for a problem you know is real? That's your opening.

Look at review complaints. Find the top-ranked existing solutions in your category and read their 1-star and 2-star reviews. These reviews are a product roadmap written by frustrated customers. Build the thing that the existing leader refuses to fix.

Evaluate API maturity. Some platforms have excellent APIs (Shopify, Notion, Figma). Others have APIs that are painful to work with or severely limited. Before committing, build a proof of concept to make sure the platform's API actually lets you do what you need.

Assess platform risk. The biggest danger of building on someone else's platform is that they can change the rules. Shopify can modify their App Store policies. Google can change their extension review process. Mitigate this by always collecting customer email addresses and building toward a standalone product that uses the platform integration as one channel among many.

This is the same risk calculus that SaaS founders hitting revenue ceilings have to navigate — the difference is that platform-embedded products hit their ceiling for structural reasons (marketplace ranking, platform limitations) rather than pricing reasons, which makes the graduation path clearer.

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The Math That Makes This Irresistible

Let me run the numbers on why this distribution strategy is so compelling compared to the traditional "build and pray" approach.

Traditional approach: Build standalone SaaS. Spend $0-$500 on a landing page. Write blog posts. Post on Twitter. Maybe try some cold outreach. Conversion rate from visitor to trial: 2-5%. Conversion from trial to paid: 10-20%. You need 2,500-25,000 website visitors per month to reach $10K MRR at a $50/month price point. Getting that traffic as an unknown product takes 6-18 months of consistent effort.

Platform-embedded approach: Build a plugin/extension. Submit to marketplace. Marketplace sends you traffic from day one — people actively searching for solutions to the problem you solve. Conversion rate from marketplace listing view to install: 5-15%. Conversion from install to paid: 3-10%. You need the marketplace to show your listing to 5,000-30,000 searchers per month, which happens automatically if you rank for relevant keywords. Time to $10K MRR: 3-8 months.

The platform approach isn't just faster — it's more predictable. You can estimate your traffic based on marketplace search volumes before you write a single line of code. You can validate demand by checking if people are searching for your solution category. And you can iterate based on direct user feedback that comes through the marketplace review system.

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The Mistake Most People Make With This Strategy

The most common failure mode isn't building the wrong product. It's treating the marketplace listing as an afterthought.

Your marketplace listing is your landing page, your sales page, and your first impression. The founders who reach $10K MRR fastest obsess over three things:

Screenshots that show the outcome, not the interface. Don't show a screenshot of your plugin's settings panel. Show a before/after of the problem it solves. A Shopify app that optimizes product images should show a blurry product photo on the left and a crisp, professional one on the right.

A description that leads with the pain point. "Tired of spending 3 hours writing product descriptions for every new collection?" beats "AI-powered content generation tool for Shopify merchants" every time. The first sentence should make the reader feel seen.

Aggressive response to reviews. Every negative review is an opportunity to show responsiveness. Every positive review is social proof that compounds. The founders who reply to every review within 24 hours — with genuine fixes for complaints, not canned responses — consistently outrank competitors with more features but worse support.

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Where to Start This Week

If this strategy resonates, here's what I'd do in the next 7 days:

Day 1-2: Pick a platform ecosystem based on the framework above. Browse its marketplace for 2 hours. Read 1-star reviews of the top 10 apps/plugins in categories that interest you. Write down every complaint that appears more than twice.

Day 3-4: Pick the single most common complaint that you could solve with AI. Build a minimal proof of concept — not a full product, just enough to prove the core value proposition works.

Day 5-6: Show the proof of concept to 5 people who match your target user profile. This could be in a relevant subreddit, Slack community, or Discord server. Ask one question: "Would you pay $X/month for this?"

Day 7: If at least 3 out of 5 say yes, start building the real thing. If not, go back to Day 1 with a different complaint.

The entire cycle from idea to validation takes a week. The cycle from validation to $10K MRR, if the market is real and the product is solid, takes 3-8 months.

That's faster than almost any other path to a profitable software business. And it all starts with a simple mental shift: stop trying to get people to come to you. Go where they already are.