I Studied Every SaaS That Grew Entirely Inside Someone Else's Ecosystem. The Leverage Is Unfair.

There's a category of SaaS companies that skip almost every hard part of building a business. They don't build landing pages for months. They don't run paid ads. They don't cold email. They don't even have to explain what problem they solve — because the customer already has the problem open on their screen when they discover the tool.

These are ecosystem-native SaaS products. Apps, plugins, extensions, and integrations that live entirely inside someone else's platform — Shopify, Figma, Notion, Slack, Salesforce, Obsidian, VS Code, even Google Sheets. And the economics of building inside these ecosystems have gotten so good that it's now one of the most reliable paths to $10K, $50K, or even $100K+ in monthly recurring revenue for a small team.

I dug into the data on marketplace-native SaaS companies — the ones that generate the vast majority of their revenue through a platform's app store or ecosystem — and the patterns are striking. The distribution advantages are obvious. But the specific types of tools that win, and the strategies that separate the $500/month plugins from the $50K/month ones, are more nuanced than most people realize.

The Core Advantage Nobody Talks About Enough

When you build a standalone SaaS, you have to solve two problems simultaneously: building something valuable, and getting it in front of people who need it. Most founders underestimate how brutal that second problem is. You can build the best tool in the world, and if nobody finds it, you're dead.

Ecosystem-native tools collapse those two problems into one. The platform's marketplace becomes your distribution channel. The platform's existing user base becomes your top-of-funnel. And the platform's context — the fact that someone is already inside Shopify trying to manage inventory, or inside Figma trying to hand off designs — means the user arrives pre-qualified with a specific pain point.

This is why you see Shopify apps hitting $1M+ ARR with two-person teams. It's why Figma plugins get tens of thousands of installs in their first month. It's why a well-positioned VS Code extension can become a real business.

But the advantage goes deeper than just distribution. There are four compounding benefits that make ecosystem-native SaaS uniquely powerful:

1. Trust is inherited. When someone installs a Shopify app from the Shopify App Store, they're extending trust from Shopify to you. The platform has already done the work of establishing credibility. This is why conversion rates on app store listings tend to be dramatically higher than conversion rates on standalone SaaS landing pages.

2. The workflow is already established. You don't have to convince someone to adopt a new tool and change their behavior. They're already using the platform. Your job is to make something they're already doing work better. That's a much easier sell than asking someone to switch contexts entirely.

3. Retention is structural. If your tool is embedded in someone's daily workflow inside a platform they depend on, switching costs are high. They'd have to find an alternative within the same ecosystem, which is a much smaller competitive set than the entire SaaS market.

4. Pricing anchors to the platform. If someone is already paying $79/month for Shopify, paying $19/month for an app that solves a critical problem within Shopify feels trivial. The platform's pricing sets the anchor, and your tool benefits from being a fraction of that anchor.

This combination of advantages creates a situation where small teams can build disproportionately profitable businesses. I've tracked tools across multiple ecosystems, and the pattern holds regardless of the platform.

The Ecosystems Where the Math Works Best Right Now

Every platform with a marketplace or extension system is technically an ecosystem you could build in. But the revenue potential varies wildly depending on a few factors: how many users the platform has, how willing those users are to pay for add-ons, how much competition exists in the marketplace, and how well the platform supports third-party monetization.

Based on the data, here's where the opportunity is richest heading into 2026.

Shopify: Still the King of Ecosystem SaaS

Shopify's app ecosystem generates billions in revenue for third-party developers. The platform has over 2 million active merchants, and the average Shopify store uses between 6 and 8 apps. That's an enormous addressable market with a built-in habit of paying for add-ons.

What's changed recently is the type of app that wins. Five years ago, the top Shopify apps were basic utilities — SEO optimizers, review widgets, email popup builders. Those categories are now saturated beyond belief. The new winners are AI-native tools that do things that weren't possible before: AI-powered product description generators that match a store's brand voice, intelligent inventory forecasting that learns from seasonal patterns, automated product photography enhancement, and dynamic pricing tools that adjust based on competitor data and demand signals.

The pricing sweet spot for Shopify apps has also shifted upward. The most successful new entrants are charging $49-$149/month, not $9.99. Merchants who are serious about their business will pay for tools that demonstrably increase revenue or save significant time. The days of racing to the bottom on price are over for the apps that actually deliver value.

One pattern I keep seeing: the Shopify apps that grow fastest are the ones that target a specific type of merchant rather than all merchants. An AI tool for fashion e-commerce stores that generates outfit-based product bundles is more compelling than a generic "AI assistant for Shopify." Vertical specificity within the ecosystem is the unlock.

Figma: The Design Ecosystem With Untapped Depth

Figma's plugin ecosystem is younger and less monetized than Shopify's, which means the opportunity for new entrants is significantly higher. Most Figma plugins are still free, which sounds like a problem but is actually a signal: the ecosystem hasn't been fully commercialized yet.

The plugins that are charging money are doing remarkably well. Tools that automate design-to-code handoff, generate design system documentation, or handle accessibility auditing within Figma are charging $10-$30/user/month and growing rapidly. As design teams standardize on Figma, the willingness to pay for plugins that save hours of manual work is increasing.

The biggest gap I see in Figma's ecosystem right now is AI-powered design assistance that goes beyond simple generation. Think: a plugin that analyzes your existing design system and flags inconsistencies, suggests improvements based on conversion data from similar products, or automatically generates responsive variants of your components. These are workflows that designers currently handle manually or with expensive external tools.

Notion, Obsidian, and the Knowledge Tool Ecosystem

Notion's API and integration ecosystem has matured significantly. There are now Notion-native tools generating serious revenue by solving specific workflow problems: project management overlays, CRM systems built on Notion databases, client portals powered by Notion pages, and automated reporting dashboards.

Obsidian is the more interesting play for indie builders. The Obsidian community is passionate, technical, and willing to pay for quality plugins. The plugin ecosystem is still small enough that a well-built tool can become the default solution for a specific use case. AI-powered plugins for Obsidian — smart note linking, automated literature review, research synthesis — are an almost untouched category with clear demand. If you browse the Obsidian subreddit and Discord, you'll find people constantly asking for tools that don't exist yet.

VS Code and Developer Tool Ecosystems

The VS Code extension marketplace is massive, but monetization has historically been weak because developers expect tools to be free. That's changing. The success of tools like GitHub Copilot has normalized paying for AI-powered developer tools, and there's now a clear path to building paid VS Code extensions that developers will actually pay for.

The key is targeting specific development workflows rather than general-purpose coding assistance. An extension that handles database migration management with AI-powered schema suggestions, or one that automates API documentation by analyzing your codebase, or a tool that generates comprehensive test suites based on your code patterns — these are specific enough to justify a subscription and different enough from Copilot to avoid direct competition.

Salesforce, HubSpot, and the CRM Ecosystem

This is the enterprise end of the spectrum, and the revenue potential per customer is dramatically higher. Salesforce AppExchange apps routinely charge $25-$100+ per user per month, and enterprise customers have budgets for tools that solve real problems.

The opportunity here is less about building something clever and more about finding a specific workflow gap that enterprise teams deal with daily. AI-powered lead scoring that actually learns from a company's specific sales patterns (rather than generic models), automated competitive intelligence that pulls data into CRM records, or compliance monitoring tools that flag risky communications — these are the kinds of tools that command premium pricing in the CRM ecosystem.

The Pattern That Separates $500/Month Plugins From $50K/Month Businesses

Across every ecosystem I analyzed, the same pattern separates the tools that stay small from the ones that become real businesses. It comes down to three factors.

Factor 1: Solving a workflow problem vs. adding a feature. The low-revenue plugins tend to add a single feature — a button that does one thing. The high-revenue tools solve an entire workflow. In Shopify, the difference is between a plugin that resizes product images (feature) and a tool that manages your entire product content pipeline from photography to SEO-optimized descriptions to multi-channel publishing (workflow). Workflow tools are stickier, more valuable, and justify higher pricing.

Factor 2: Data accumulation as a moat. The most successful ecosystem-native tools get more valuable over time because they accumulate data specific to each customer. A Shopify inventory forecasting app that has six months of a merchant's sales data is almost impossible to replace — the new tool would have to start learning from scratch. This data moat is the single biggest driver of retention in ecosystem SaaS, and it's something you should design for from day one.

Factor 3: Expansion revenue within the ecosystem. The best ecosystem-native businesses don't just sell one tool. They build a suite of related tools within the same ecosystem, cross-selling to their existing customer base. If you have a Shopify app that handles product descriptions, your next app might handle email marketing copy, and your third might handle social media content — all powered by the same AI engine, all sold to the same merchants. This is how you go from $10K MRR to $50K MRR without dramatically increasing your customer acquisition costs.

This expansion pattern is similar to what I found when I looked at SaaS companies that hit $1M ARR selling to other SaaS companies — the meta-game of building tools for people who are already inside a specific software environment is incredibly powerful.

7 Specific Ecosystem-Native SaaS Opportunities That Are Wide Open Right Now

Let me get concrete. Based on the gaps I've identified across these ecosystems, here are specific opportunities that are either underserved or completely unaddressed.

1. AI Brand Voice Engine for Shopify (All Content, One Voice)

The problem: Shopify merchants use multiple tools to generate product descriptions, email campaigns, blog posts, and social media content. Each tool produces content in a slightly different voice. The result is a fragmented brand experience that erodes customer trust.

The opportunity: A Shopify app that learns a merchant's brand voice from their existing content and then serves as the AI layer for all content generation within their store. Product descriptions, automated email flows, blog posts, even customer service responses — all consistent, all on-brand.

Existing solutions are generic AI writing tools that don't integrate deeply with Shopify's data model. They don't know what products the merchant sells, what their customer reviews say, or what tone their best-performing content uses.

Pricing potential: $49-$99/month. At 2,000 paying merchants, that's $100K-$200K MRR.

2. Figma-to-Accessible-Code Pipeline

The problem: Accessibility compliance (WCAG) is increasingly required by law in the US and EU. Designers create beautiful mockups in Figma, but the handoff to developers rarely includes accessibility specifications. The result is expensive remediation work after the fact, or worse, lawsuits.

The opportunity: A Figma plugin that audits designs for accessibility issues in real-time, suggests fixes, and generates accessibility-compliant code snippets that developers can use directly. Think of it as an accessibility co-pilot that lives inside the design tool where decisions are actually made.

The competitive landscape is thin. There are standalone accessibility auditing tools, but almost nothing that works inside Figma at the design stage. The timing is perfect given the wave of regulatory changes creating new SaaS markets.

Pricing potential: $15-$30/user/month for design teams. A mid-size agency with 20 designers would pay $300-$600/month.

3. Obsidian Research Synthesis Engine

The problem: Researchers, academics, and knowledge workers use Obsidian to manage thousands of notes, papers, and references. But synthesizing insights across hundreds of notes is still entirely manual. You have to remember what you wrote, where you wrote it, and how different ideas connect.

The opportunity: An Obsidian plugin powered by AI that can synthesize insights across your entire vault. Ask it "What are the main arguments for and against X in my notes?" and it pulls together a coherent synthesis from dozens of scattered notes. It could also suggest connections you haven't made, identify contradictions in your thinking, and generate literature review drafts from your annotated sources.

The Obsidian community is vocal about wanting this. The plugin ecosystem is small enough that a quality tool would become the default quickly.

Pricing potential: $8-$15/month. The Obsidian user base skews toward people who value their knowledge management enough to pay for quality tools.

4. Salesforce Deal Intelligence Autopilot

The problem: Sales reps spend hours manually updating Salesforce records, researching prospects, and trying to figure out which deals are actually likely to close. Most "AI for sales" tools are standalone platforms that require yet another login and another tab.

The opportunity: A Salesforce-native app that automatically enriches deal records with competitive intelligence, suggests next actions based on deal stage and historical patterns, and predicts close probability using the company's own historical data (not generic models). Everything lives inside Salesforce — no context switching.

The key differentiator from existing tools is that this would be trained on each company's specific sales patterns, not generic models. A SaaS company's sales cycle looks nothing like a manufacturing company's, and the AI should reflect that.

Pricing potential: $40-$80/user/month. A 50-person sales team would pay $2,000-$4,000/month. This is the kind of high-ARPU opportunity I track at SaasOpportunities — enterprise ecosystem tools that justify premium pricing because they're embedded in mission-critical workflows.

5. Notion Client Portal Generator

The problem: Freelancers and small agencies use Notion internally to manage projects, but they need a professional way to share project updates, deliverables, and invoices with clients. Right now, they either give clients direct access to Notion (messy, unprofessional) or maintain a separate client-facing tool (redundant, time-consuming).

The opportunity: A tool that turns Notion databases and pages into branded client portals with custom domains, access controls, and automatic syncing. The freelancer works in Notion as usual, and the client sees a polished, professional portal that updates in real-time.

There are early attempts at this, but nothing that handles the full workflow: project tracking, file sharing, approvals, invoicing, and communication — all powered by Notion data.

Pricing potential: $19-$49/month. The target market is the millions of freelancers and small agencies already using Notion. This is adjacent to the pattern of SaaS tools that replace freelancer workflows, but instead of replacing the freelancer, you're empowering them.

6. Google Sheets AI Analyst

The problem: Millions of businesses still run on Google Sheets. Their data lives there, their reports live there, their dashboards live there. They don't want to migrate to a BI tool. They just want their spreadsheets to be smarter.

The opportunity: A Google Sheets add-on that acts as an AI data analyst. You highlight a range of data and ask questions in natural language: "What's driving the decline in Q3 revenue?" or "Forecast next quarter based on the last 12 months." It generates charts, writes formulas, identifies anomalies, and produces narrative summaries — all within the spreadsheet.

Google has added some AI features to Sheets, but they're basic and generic. A purpose-built add-on that specializes in business data analysis would be significantly more powerful. The market of businesses running on spreadsheets is enormous — and as we've seen, replacing spreadsheets is one of the most reliable paths to $1M ARR. But this approach is even smarter: instead of replacing the spreadsheet, you supercharge it.

Pricing potential: $29-$79/month. The willingness to pay is high because the alternative is hiring a data analyst or buying a $500/month BI tool.

7. Slack Async Decision-Making System

The problem: Remote and hybrid teams make decisions in Slack, but those decisions are buried in channels, lost in threads, and impossible to find later. "Didn't we already decide this?" is the most common question in every Slack workspace.

The opportunity: A Slack-native app that provides a structured decision-making workflow. When a decision needs to be made, someone initiates a decision thread. The app collects input from relevant stakeholders, tracks who's voted or commented, sets deadlines, and records the final decision in a searchable archive. AI summarizes the discussion and rationale so anyone can understand why a decision was made months later.

This is a workflow that every remote team needs and nobody has built well inside Slack. The existing solutions are standalone tools that require people to leave Slack, which defeats the purpose.

Pricing potential: $5-$10/user/month. A 100-person company would pay $500-$1,000/month. The distribution advantage is massive because Slack's App Directory puts you in front of millions of teams.

The Risks You Need to Understand

Ecosystem-native SaaS isn't without risks. The biggest one is platform dependency. If Shopify changes its API, raises its revenue share, or builds your feature natively, you can be wiped out overnight. This is the "platform apocalypse" scenario, and it's real — I've written about the SaaS companies that survived these events and what they did differently.

The mitigation strategy is straightforward: build on data accumulation, not just feature access. If your tool gets more valuable over time because it learns from customer-specific data, you have a moat that the platform can't easily replicate by shipping a native feature. A platform might build a basic version of your tool, but it won't have six months of each customer's historical data powering its recommendations.

The second risk is marketplace dynamics. App stores can change their ranking algorithms, their review policies, or their revenue share at any time. You're playing on someone else's field. The way to mitigate this is to use the marketplace as your acquisition channel but build direct relationships with customers through email, content, and community. The marketplace gets them in the door; your product and your relationship keep them.

The third risk is ceiling effects. Some ecosystems simply don't support businesses above a certain size. If the platform's user base caps out, your growth caps out too. The answer is to choose ecosystems that are themselves growing, and to build with the option to expand to adjacent ecosystems later. An AI brand voice engine that starts on Shopify could expand to WooCommerce, BigCommerce, and eventually become a standalone product.

How to Pick Your Ecosystem and Build

If you're a solo developer or small team looking at this opportunity, here's the framework I'd use:

Step 1: Pick an ecosystem you already use. Your intuition about what's missing will be sharper if you're a genuine user of the platform. You'll know the pain points from experience, and you'll be able to build faster because you understand the platform's data model and API.

Step 2: Audit the marketplace for pricing gaps. Look at the existing apps in your chosen ecosystem. Where are there free tools with thousands of users but no paid alternative? Where are there paid tools with terrible reviews? Where are there categories with only 1-2 competitors? These gaps are your entry points.

Step 3: Target a workflow, not a feature. The single biggest predictor of revenue in ecosystem SaaS is whether you're solving a complete workflow or just adding a button. Workflows justify subscriptions. Features get commoditized.

Step 4: Design for data accumulation from day one. Every interaction a customer has with your tool should make the tool more valuable for that specific customer. This is your moat against both competitors and the platform itself.

Step 5: Launch fast, iterate based on reviews. Ecosystem marketplaces give you something standalone SaaS doesn't: immediate, public feedback. Your first reviews will tell you exactly what to build next. The tools that dominate app stores are the ones that respond to user feedback fastest. With modern AI development tools, you can ship updates in hours, not weeks.

The Leverage Is Real

The reason I keep coming back to ecosystem-native SaaS is that the leverage ratio is unlike almost anything else in software. You're borrowing distribution from a platform with millions of users. You're borrowing trust from a brand that's already established. You're borrowing context from a workflow that's already happening. And you're building on top of infrastructure — authentication, payments, data storage — that the platform provides.

What you're adding is intelligence and specificity. The platform provides the general-purpose tool. You provide the specialized layer that makes it dramatically more useful for a specific type of user or a specific workflow.

With AI making it possible to build that specialized layer faster than ever, and with platforms increasingly opening their ecosystems to third-party developers, the window for ecosystem-native SaaS is wider right now than it's ever been.

The builders who recognize this aren't competing in the crowded market of standalone SaaS tools fighting for attention. They're showing up exactly where their customers already are, solving problems those customers are already experiencing, and growing at rates that would be impossible with a traditional go-to-market approach.

Pick your ecosystem. Find the workflow gap. Build the intelligent layer. The platform will do the rest.