I Studied Every SaaS That Hit $1M ARR by Selling to Other SaaS Companies. The Meta-Game Is Absurd.

There's a category of SaaS company that grows faster, churns less, and reaches profitability sooner than almost any other type. They don't sell to restaurants. They don't sell to real estate agents. They don't sell to e-commerce brands.

They sell to other SaaS companies.

And when you start mapping these businesses out — the ones doing $1M, $5M, $10M ARR by serving the SaaS ecosystem itself — a pattern emerges that's almost too clean. The same entry points. The same pricing psychology. The same expansion mechanics. And a massive number of gaps that nobody has filled yet.

I went deep on this. I looked at infrastructure tools, developer experience platforms, analytics layers, compliance utilities, billing systems, onboarding tools, and dozens of other categories where SaaS companies are the customer. What I found is that the "SaaS-for-SaaS" meta-game is one of the most reliably profitable spaces to build in right now — and it's shockingly underexploited in certain areas.

Let me walk you through what the data actually shows.

Why SaaS Companies Are the Best Customers on Earth

Before we get into the specific opportunities, it's worth understanding why this customer segment is structurally different from everything else.

SaaS companies have recurring revenue. That means they budget annually, they plan for tooling costs, and they don't cancel subscriptions because of a bad quarter the way a local business might. When a SaaS company with $500K ARR pays you $200/month for a tool that saves their engineering team four hours a week, that's not a discretionary expense. It's infrastructure. It gets baked into the P&L and forgotten about.

SaaS companies also grow. A customer that starts at $49/month when they're pre-revenue can become a $499/month customer eighteen months later when they've hit product-market fit. This is the expansion revenue dynamic that makes tools like Stripe, Segment, and LaunchDarkly so valuable — their revenue grows automatically as their customers grow.

And SaaS founders talk to each other constantly. They're in Slack communities, on Twitter/X, in Discord servers, at conferences. Word of mouth travels faster in this ecosystem than in almost any other B2B vertical. If you build something that genuinely solves a pain point for SaaS teams, distribution can be almost organic.

The data backs this up. When you look at SaaS companies doing $1M+ ARR with tiny teams, a disproportionate number of them are selling developer tools or SaaS infrastructure. The reason is simple: the buyer understands software, values software, and pays for software without needing a six-month sales cycle.

The Five Layers of the SaaS-for-SaaS Stack

Every SaaS company, regardless of what it does, has roughly the same operational needs. I mapped these into five layers, and each layer has both well-served categories and gaping holes.

Layer 1: Build (Development and Deployment)

This is the most crowded layer, and for good reason — it's where the biggest companies live. Vercel, Railway, Supabase, PlanetScale, Neon. The infrastructure that SaaS companies use to actually ship their product.

But even here, there are gaps. The explosion of AI-native SaaS tools has created entirely new deployment patterns. Companies building with Claude, GPT-4, and open-source models need infrastructure that handles prompt versioning, model fallback routing, cost tracking per API call, and latency monitoring across multiple LLM providers. Tools like Helicone and Braintrust are early movers, but the space is still nascent.

The specific gap I keep seeing: LLM cost attribution at the customer level. If you're running a SaaS product that makes API calls to Claude or GPT-4 on behalf of your users, you need to know exactly how much each customer costs you. Not at the aggregate level — at the per-user, per-feature, per-session level. Most teams are hacking this together with custom logging and spreadsheets. A clean, drop-in SDK that attributes AI costs to individual customers and ties into billing systems could charge $200-500/month easily and would have near-zero churn once integrated.

Layer 2: Monetize (Billing, Pricing, and Payments)

Stripe handles payments. But Stripe doesn't handle the increasingly complex pricing models that modern SaaS companies use. Usage-based pricing, hybrid models, seat-based with overage charges, AI credit systems, per-feature gating — the pricing landscape has gotten dramatically more complex in the last two years.

Orb, Metronome, and Lago are attacking this space, but they're all targeting mid-market and enterprise. There's a massive gap for early-stage SaaS companies that need usage-based billing but can't justify $1,000+/month billing infrastructure.

The more interesting gap, though, is pricing experimentation infrastructure. SaaS companies know they should be testing their pricing. Almost none of them do it systematically. I've written about how pricing pages reveal revenue ceilings — and the core problem is that changing pricing is terrifying because most teams have no way to A/B test it safely.

Imagine a tool that lets you run controlled pricing experiments: show different pricing pages to different segments, track conversion through to actual payment, measure revenue impact, and roll back instantly if something tanks. Optimizely for pricing pages, essentially, but built specifically for SaaS billing flows. The willingness to pay here is enormous because pricing optimization directly impacts revenue. Even a 5% improvement in conversion at the pricing page is worth thousands per month for any SaaS doing meaningful volume.

Layer 3: Grow (Acquisition, Activation, and Retention)

This is where the most interesting gaps are hiding.

Every SaaS company needs to convert trial users into paying customers. The activation flow — that critical period between signup and "aha moment" — is where most SaaS companies lose the majority of their potential revenue. And yet the tooling for this is shockingly bad.

Product analytics tools like Amplitude, Mixpanel, and PostHog tell you what happened. They don't tell you what to do about it. You can see that 67% of trial users drop off before completing onboarding step 3, but you still have to figure out why and build the intervention yourself.

The gap: AI-powered activation copilots. A tool that ingests your product analytics data, identifies the specific friction points in your activation funnel, and automatically generates interventions — targeted in-app messages, email sequences, UX change suggestions — based on patterns across thousands of SaaS products. This isn't hypothetical; the data to train these models exists in aggregate across the analytics platforms. Someone just needs to build the intelligence layer on top.

Another massive gap in the Grow layer: churn prediction that actually works for early-stage SaaS. Every enterprise churn tool (Gainsight, ChurnZero, Totango) requires a customer success team and months of implementation. But a solo founder running a $30K MRR product needs churn signals too — they just need it delivered as a simple dashboard that says "these 12 customers are likely to cancel next month, here's why, here's what to do." Priced at $99-199/month, integrated with Stripe and your product database, this could serve the massive long tail of SaaS companies between $5K and $100K MRR that the enterprise tools ignore.

Layer 4: Comply (Security, Privacy, and Legal)

This layer is about to explode, and almost nobody is positioned for it.

SOC 2 compliance used to be something only enterprise SaaS companies worried about. Now, any SaaS company selling to mid-market B2B customers gets asked about it. The current solutions — Vanta, Drata, Secureframe — are excellent but expensive, typically starting at $10,000+/year. That's fine for a Series A company, but brutal for a bootstrapped SaaS doing $15K MRR that just got their first enterprise prospect asking for SOC 2.

The gap: compliance-as-a-service for early-stage SaaS, priced at $200-400/month, that handles the 80% of SOC 2 (or ISO 27001, or GDPR) that's actually just documentation, policy templates, and evidence collection. Not the full audit — just getting you audit-ready. The companies that killed agency retainers did it by automating the predictable parts of expensive services. Compliance consulting is the next domino.

But the really untouched opportunity is AI compliance for SaaS companies. If your SaaS product uses AI — and increasingly, most do — you're entering a regulatory minefield. The EU AI Act is already in effect. State-level AI regulations in the US are multiplying. Your customers are starting to ask: "What AI models do you use? Where does our data go? Do you have an AI impact assessment?"

There is no good tool for this yet. An AI compliance platform that helps SaaS companies document their AI usage, generate impact assessments, maintain model cards, track data flows through AI pipelines, and produce the reports that enterprise buyers are starting to require — this is a $100M+ category that barely exists. The timing is almost identical to where GDPR compliance tools were in 2017, right before the market exploded.

Layer 5: Operate (Internal Tools, Workflows, and Team Coordination)

The final layer is about how SaaS companies run internally. And the most interesting gap I've found here is surprisingly specific.

Feature flag and release management for non-technical stakeholders.

LaunchDarkly and similar tools are built for engineers. But in a modern SaaS company, product managers, marketers, and customer success teams all need to control feature rollouts. "Turn on the new dashboard for Customer X because they're in our beta program." "Disable the AI feature for customers on the free plan." "Roll out the new pricing page to 10% of new signups."

Right now, these requests go through engineering. Every single time. A tool that gives non-technical team members a clean interface to manage feature access, with appropriate guardrails and approval flows, would save engineering teams hours per week. And it would sell into the exact same companies already paying for LaunchDarkly — as a complement, not a replacement.

I track these kinds of infrastructure gaps at SaasOpportunities, and the SaaS-for-SaaS category consistently produces the highest-quality opportunities because the buyer is sophisticated, the pain is measurable, and the willingness to pay is proven.

The Pricing Sweet Spot That Keeps Showing Up

Across the SaaS-for-SaaS companies I analyzed, there's a pricing pattern that's remarkably consistent among the ones that reached $1M ARR fastest.

They don't start cheap. The entry-level plan is typically $49-99/month — not $9, not free-with-upgrade. And they scale to $500-2,000/month for growth-stage customers.

This makes sense when you think about the buyer. A SaaS company evaluating a tool doesn't think "is $99/month a lot of money?" They think "does this save us more than $99/month in engineering time or lost revenue?" The bar for justification is completely different than selling to a solopreneur or a local business.

The companies that charge over $500/month almost always sell to other businesses that can directly calculate ROI. SaaS-for-SaaS takes this to an extreme because SaaS operators are inherently numerate about their own costs. They know what an engineering hour costs. They know their churn rate. They know their CAC. If your tool moves any of those numbers, the sale is almost mathematical.

The Distribution Advantage Nobody Talks About

Selling to SaaS companies has a distribution mechanic that doesn't exist in most other verticals: the integration marketplace.

When you build a tool that integrates with Stripe, Vercel, Supabase, or any other platform that SaaS companies use, you get listed in their integration directory. This is free, high-intent distribution. Someone browsing the Stripe app marketplace is already a SaaS operator looking for tools. The conversion rates from integration marketplaces are dramatically higher than from cold outbound or even content marketing.

This is the same distribution pattern that early-stage SaaS companies use to get to $10K MRR without traditional marketing. Build a deep integration with a platform your target customer already uses, get listed in their marketplace, and let the platform's existing traffic do the work.

The second distribution channel that works unusually well for SaaS-for-SaaS: open-source-to-paid. Release the core functionality as an open-source library. Let developers discover it, integrate it, depend on it. Then offer a hosted version with dashboards, team features, and support as the paid product. PostHog, Cal.com, Lago, and dozens of others have proven this model. It works because SaaS developers discover tools through GitHub, npm, and technical blog posts — not through Google ads.

Seven Specific Gaps You Could Build Into Right Now

Let me get concrete. Based on everything I've mapped, here are the specific SaaS-for-SaaS opportunities with the most signal and the least competition.

1. LLM Cost Attribution and Margin Calculator What it does: Drop-in SDK that tracks AI API costs per customer, per feature, per session. Shows you which customers are profitable and which are costing you money. Why now: Every AI-native SaaS company has this problem. Most are flying blind on unit economics. Pricing: $149-499/month based on API call volume. Competitors: Helicone (focused on observability, not cost attribution). Mostly DIY solutions.

2. Pricing A/B Testing for SaaS What it does: Run controlled experiments on your pricing page — different plans, different prices, different packaging — with statistical rigor and Stripe integration. Why now: Pricing is the highest-leverage growth lever, and nobody tests it because the tooling doesn't exist. Pricing: $199-799/month. Competitors: Essentially none purpose-built for SaaS pricing experimentation.

3. AI Compliance Documentation Platform What it does: Helps SaaS companies document their AI usage, generate impact assessments, maintain model cards, and produce compliance reports for enterprise buyers and regulators. Why now: EU AI Act enforcement, proliferating state regulations, enterprise procurement requirements. Pricing: $299-999/month. Competitors: The big compliance platforms (Vanta, Drata) haven't built AI-specific modules yet.

4. Churn Intelligence for Sub-$100K MRR SaaS What it does: Connects to Stripe + your product database. Uses behavioral signals to predict which customers will churn and suggests specific interventions. Why now: Every churn tool targets enterprise. The long tail of SaaS companies between $5K-100K MRR has nothing. Pricing: $99-299/month. Competitors: Baremetrics has basic churn data. Nothing with predictive intelligence at this price point.

5. SOC 2 Lite for Bootstrapped SaaS What it does: The 80% of SOC 2 readiness that's documentation, policies, and evidence collection — without the $10K+/year price tag. Why now: Mid-market buyers increasingly require SOC 2 even from small vendors. Bootstrapped SaaS companies can't afford Vanta. Pricing: $199-399/month. Competitors: Vanta and Drata (10x the price). Some consultants. No affordable self-serve option.

6. Customer-Facing Feature Changelog That Actually Drives Engagement What it does: Not just a changelog page — a system that segments announcements by customer plan, tracks which features drive re-engagement, and triggers targeted notifications when features relevant to at-risk customers ship. Why now: Every SaaS company has a changelog. Almost none use it as a retention tool. Pricing: $79-249/month. Competitors: Beamer, LaunchNotes (basic changelogs without the retention intelligence layer).

7. Integration Health Monitoring What it does: If your SaaS product integrates with Slack, HubSpot, Salesforce, etc., this tool monitors the health of those integrations across your customer base. Alerts you when an integration breaks for a specific customer before they notice, tracks integration adoption rates, identifies customers who haven't connected key integrations. Why now: Integration failures are a top driver of churn and support tickets. Nobody monitors this proactively. Pricing: $149-499/month. Competitors: Merge.dev handles integration building but not health monitoring. This is largely a greenfield category.

Why This Meta-Game Gets More Valuable Every Year

The number of SaaS companies in the world is growing exponentially. Every developer who fires up Cursor or Claude Code and ships a product in a weekend becomes a potential customer for SaaS-for-SaaS tools. The rise of AI-native development means more SaaS products are being created faster than ever before, and each one needs billing, analytics, compliance, and operational tooling.

This is a compounding market. As the number of SaaS companies grows, the addressable market for tools that serve SaaS companies grows proportionally. You're not betting on a single industry or a single trend — you're betting on the continued growth of software itself.

And because your customers are SaaS operators, they understand the value of software in a way that no other customer segment does. You don't have to convince them that paying for tools is worthwhile. You just have to show them the ROI.

The Moat Question

The obvious concern: if these tools are easy to build, won't they get commoditized immediately?

Two things protect you.

First, integration depth. Once a SaaS company has integrated your SDK into their codebase, connected their Stripe account, and started relying on your dashboards for decision-making, the switching cost is real. Not insurmountable, but real. Every week that usage data accumulates in your platform, the moat deepens.

Second, data network effects. A churn prediction tool gets better as it sees more churn patterns across more SaaS companies. A pricing experimentation tool gets better as it accumulates data about what pricing structures convert best in which categories. The first tool to aggregate this data across hundreds of SaaS customers has a structural advantage that a new entrant can't replicate on day one.

This is the same dynamic that made Stripe so dominant. Payments processing is theoretically commoditizable. But Stripe's integration depth and data advantages made switching irrational for most companies, even when competitors offered lower fees.

How to Enter This Market If You're Starting From Zero

The playbook is straightforward, and it's different from entering a vertical SaaS market.

Start with a specific pain point in a specific layer. Don't try to build a platform. Build the smallest possible tool that solves one acute problem for SaaS operators. The LLM cost attribution SDK. The pricing A/B test widget. The integration health monitor.

Build the core as an open-source library or a free tool. Get it into the hands of developers. Let them integrate it, depend on it, and tell their friends about it. The SaaS developer community is incredibly efficient at spreading useful tools through Twitter threads, Hacker News posts, and GitHub stars.

Once you have adoption, add the paid layer: dashboards, team features, historical data, alerts, integrations with other tools in the stack. This is where the $149-499/month plans live.

Then grow with your customers. The SaaS company that's paying you $149/month at $10K MRR will be paying you $499/month at $100K MRR — if you've built usage-based pricing into your own model. This expansion revenue dynamic is one of the most powerful mechanics in SaaS, and it's even more powerful when your customers are themselves SaaS companies on growth trajectories.

The Bottom Line

The SaaS-for-SaaS meta-game is one of the most rational places to build right now. Your customers understand software, pay for software, grow predictably, and talk to each other. The market expands every time someone launches a new SaaS product. The pricing tolerance is high because the ROI is directly measurable.

And the gaps are real. AI cost attribution, pricing experimentation, lightweight compliance, churn intelligence for the long tail, integration health monitoring — these aren't theoretical problems. They're the things SaaS operators complain about in every Slack community and Twitter thread, every single day.

The question isn't whether these tools will exist. It's whether you'll be the one building them.

Pick a layer. Pick a pain point. Ship the smallest version that's useful. The meta-game rewards the people who move first.