What software to build to make money, ranked by what actually sold

Search this question and you get forum threads from 2018 and idea lists built on single-founder stories. One founder's monthly revenue tells you what worked once. It does not tell you which category pays most often.

This page does the category-level version. It starts with 12 months of sales from one of the largest lifetime-deal software marketplaces, published on October 21, 2026 with the marketplace's own search data for the last 90 days. Then it checks the ranking against our own data: verified subscription revenue for thousands of startups. Two different measures. One consistent answer.

Median sales per product over 12 months, from the marketplace. The right-hand columns are our separate cross-check: subscription MRR for keyword-matched startups in our revenue index. A dash means fewer than 30 earning products, too few to publish a median.

Read the shares in the last column with their sample sizes in mind. Eight of the ten buckets track fewer than 80 products, and the "At $1K+" share is measured against all tracked products in that bucket, not only earning ones. The overall baseline for everything else in our index (8,000+ startups tracked, 3,400+ earning) is a $136 median MRR, with 10.0% at $1K+ and 2.6% at $10K+.

Why what sold beats what is trending

Trend lists measure attention. Sales measure payment. A category can dominate launch feeds for a year and still produce nothing a buyer will pay for twice.

Three rules shaped this ranking. First, the unit is the median product, not the best one. Every category has a winner. The median tells you what a typical entrant earned. Second, we report how many products sit behind each number, because a median over 6 products moves a lot when one product changes. Third, we cross-check with a second dataset that measures a different thing. Lifetime-deal sales are one-time payments from deal hunters. Our revenue index tracks monthly subscription revenue verified through payment providers. If both agree, the pattern is more than an artifact of one audience.

They do not agree on dollar amounts, and should not. A $257K median for a category is gross sales over a year. A $721 MRR median is a single month of recurring revenue per product. Compare the order of the categories, not the figures.

1. Getting found in ChatGPT and AI search

$257K median sales (6 products). $721 median MRR in our data (34 earning of 77 tracked). It is the top category in both datasets.

The pitch is simple. Buyers now ask an AI assistant who to hire and what to buy. A business that does not appear in the answer loses leads it never knew existed. These products show a business what AI assistants say about it, where it is missing and what to change. The buyer has a measurable pain and a number to move.

The pain is concrete in the threads we read. A business owner on r/smallbusinessowner, writing about ChatGPT recommending their business, put it plainly:

"I know these AI tools are sending people my way but I have basically zero control over what they say about me."

– r/smallbusinessowner

Zero control is a product brief. The owner can see the traffic and cannot see the answer. Visibility tools give them the answer, then give them a to-do list.

What to build: a monitor for one buyer type, not for everyone. Local service businesses, agencies and ecommerce stores ask different questions and need different reports. Show the exact prompt, the exact answer and the exact competitor named instead. Ship a weekly change report before you ship a dashboard.

Caveat: six products is a thin base, and the $721 MRR median is the highest of the buckets we can publish, not a promise. Eighteen percent of the 77 tracked tools reach $1K MRR. Most do not. If you build here, build with a specific buyer in mind and expect incumbents in adjacent software to add a visibility feature. Our own most profitable SaaS niches page shows how categories hold up over time.

2. LinkedIn automation that books meetings

$138K median sales (6 products). $288 median MRR (70 earning of 126 tracked). The second category also sits in the top group of our data: 19.0% of tracked LinkedIn tools reach $1K MRR, the highest share of any bucket where we have a median.

The marketplace's read on why this category sells: the tool that books meetings gets paid. A tool that sends connection requests gets compared on price and cancelled in a quiet month. A buyer who can attach a tool to a booked calendar slot stops thinking about cost per month.

The gap between the two medians is wide: $138K in one-time deal sales against a $288 MRR median. Part of it is the audience. Deal hunters buy many tools once. Part of it is the category being large: 126 tracked products is the second biggest bucket in our data, so a typical product shares the market with a lot of siblings.

What to build: pick the output, not the input. Reply handling, meeting booking and warm-lead routing are the parts buyers pay to skip. A narrow tool that takes a reply and books a call beats a broad tool that scrapes profiles. Our guide to finding customers on Reddit covers the same output-first logic for a different channel.

Caveat: platform rules can end a LinkedIn product overnight. A tool that depends on one network's tolerance has a ceiling you do not control.

3. AI video

$60K median sales (12 products). $346 median MRR (38 earning of 76 tracked). Third in sales and second by median MRR in our data, ahead of LinkedIn tools. But only 14.5% of tracked tools reach $1K MRR and just 1.3% reach $10K.

The logic is blunt. Video is the hardest content to make, and people pay to skip it. Editing, captions, clips and talking-head generation each remove a task the buyer already dislikes. The cost of the alternative, a freelancer or an afternoon, is easy to price against.

The ceiling is visible too. A $346 median MRR with 1.3% reaching $10K means most products stay small. Model providers can add video features at any time, and each one narrows what a standalone tool can charge for.

What to build: finish a job end to end for one use case. "Turn a webinar into ten clips with captions" is a product. "AI video generator" is a feature request to a model provider. Choose a format buyers already publish weekly, so the tool earns a place in a routine.

4. A cheaper CRM

$43K median sales (8 products). 43 tracked in our data, 16 earning, 16.3% at $1K+ and 7.0% at $10K+. We withhold the MRR median because only 16 products earn. The $10K+ share is 7.0%, nearly three times the 2.6% baseline, which says a few CRM products do very well while most do not.

The buyer logic from the sales data: people switch CRMs when theirs gets expensive, so be the cheaper one. That is an unusual rule for software, because it says the winning feature is a lower number on the invoice. It works when the incumbent's pricing has drifted away from what a small team uses.

We see the demand side in our own complaint data. Small teams describe CRMs as too much product for their size, and we cover that in CRMs that are too complicated for small business. For founders building for software companies specifically, see the CRM comparison for SaaS startups.

What to build: a CRM for one trade or one team size, with the five fields that team uses and a flat price. Import from the incumbent in one click. The switching cost is the only thing protecting the expensive product, so remove it.

The gap: HR software has the demand and no winner

Here is the most useful number in the marketplace data. "HR" was the number one search term: 640 searches in 90 days. The number of HR management tools selling was 1. It sold $465 all year.

The rule of thumb that falls out: lots of searches and few products means go. Search volume is demand you can count. A near-empty shelf is supply you can count. When the two disagree this hard, either buyers want something no one has built or the thing is much harder than it looks. Our data helps tell which.

Our revenue data says HR is hard to win, not easy to ignore. We tracked 38 HR tools. 14 earn anything. Two of 38 (5.3%) reach $1K MRR and none reach $10K. That is the lowest $1K+ share of any bucket in this study. Searches are high and winners are absent in both datasets.

Our complaint data says what buyers want fixed. We index 1,100+ HR software, payroll and HRIS complaints from software reviews. The systemic ones, those that hit multiple vendors in the same category, cluster in three themes:

• Reporting. Limited reporting in HR analytics tools affects 10 companies in the category, and limited reporting across platforms in payroll affects 25.
• Integration gaps. Integration gaps with third-party systems hit 33 companies in payroll, the widest systemic pain we found there.
• Manual work. Manual vacation management and manual data entry appear in both categories, 7 to 21 companies each.

The same story shows up in a small-business thread on r/workforcemanagement about the best HR software for a small team:

"managing everything manually is becoming a headache" and "they either felt too bloated for our size."

– r/workforcemanagement

Two phrases, two products. "Managing manually" is the unmet need. "Too bloated for our size" is why the suites on offer do not fit.

What to build: not an HR suite. Pick one of the three themes and sell it to one kind of small employer. Time-off tracking that syncs with payroll. A reporting layer that sits on top of the payroll tool a business already pays for. An onboarding checklist for a 10-person company. Each is a narrow job with a named buyer, and each avoids a head-on fight with a suite.

Honest limit: one product selling is a statement about one marketplace's audience, mostly deal hunters, agencies and small business owners. It may be that HR buyers do not shop there. The search count says they looked, not that they would pay. Treat the gap as a hypothesis to test with a deposit, not a finding to build on.

The trap: AI writing and AI agents

AI writing: $34K median across 33 products. AI agents: $17K across 15. The source's read is that both are fun to demo, crowded and low-median. AI writing has the biggest product count of any category in the marketplace data, 33. Crowding sets a ceiling on what any one product sells.

Our revenue index has the sharper version of this story. AI agents are the most crowded bucket we measured: 188 tracked products, 2.4 times the 77 in AI search visibility. They also have the lowest median MRR we can publish, $186 against $721, about 3.9 times lower. Only 9.6% reach $1K MRR. Crowded and low is the same finding in both datasets, from two different payment types.

Why does a demo not turn into revenue? A founder on r/SaaS wrote about week one of building an AI voice agent business: 143 cold emails, 0 replies, $0 revenue, about 60 hours. Their summary:

"The gap between 'this is cool' and 'here's my credit card' is way wider than I expected."

– r/SaaS

Another builder on r/SideProject ran an autonomous AI agent experiment and reached day 4 with zero sales. Their conclusion, in their own words: "I read that as 'the product is weak', not 'no traffic'." Both stories end at the same place. Impressive output does not equal a buyer with a budget line.

An agent is not a bad idea. A generic agent is a bad category. The ones that earn do one named job for one named buyer, like the meeting-booking tools in the LinkedIn section. If you are weighing an agent product, read our AI wrapper ideas ranked by revenue and do vibe-coded apps make money first. For the integration end of the agent market, MCP server ideas and ChatGPT app ideas go deeper.

The bottom: social media management, WordPress plugins and cold email

Social media management: $3.7K median (12 products). WordPress plugins: $3.5K (19). Cold email: $1.8K (8). The source's diagnosis is too many tools chasing the same buyers. That fits what we see.

Cold email is the clearest case. It sits at the bottom of the sales ranking, and in our data only 7 of 14 tracked tools earn anything. Fourteen is too few to publish a median, so we do not. What we can say is the share at $1K+ MRR is 14.3%, close to the all-startup baseline of 10.0%. Not a graveyard, just not a premium category.

Social media management has 22 tracked tools in our data and 18.2% at $1K+ MRR, which is respectable, and 9.1% at $10K+. One plausible reading is that a few large winners absorb most buyers and the rest split what is left. WordPress plugins have a similar shape: 33 tracked, 17 earning, 9.1% at $1K+ and none at $10K. A plugin lives inside someone else's platform, which plausibly limits what it can charge. We walk through that economics in how to sell a WordPress plugin.

The lesson is not that these markets are closed. It is that a typical new entrant lands in a crowd and splits a small median. If you pick one, you need a specific buyer the incumbents ignore.

The pattern: customers or visibility

Lay the ranking out and one thing separates the top four from the rest. Getting found in AI search, LinkedIn meetings, video and a cheaper CRM all attach to a buyer's revenue or a task they hate. AI writing, agents, social scheduling and plugins attach to a feature.

That gives a three-question screen you can run on any idea:

1. Does it bring the buyer a customer, or take away a task they avoid? If neither, expect a $3K median.
2. How many products already sell it? Counts in this study run from 1 to 33. The crowded end earned least.
3. Is the buyer already searching and paying? Search with no sales is the HR case. It is either a gap or a trap, and a small test tells you which.

The ordering is not perfect. AI writing has 33 products and a $34K median, well above the bottom three, so crowding alone does not predict outcome. Our own look at 20 revenue categories in someone already built my idea found almost no correlation between crowding and median MRR. Crowding is one input, not the whole answer.

How to find your own gap in one afternoon

The marketplace did the supply and demand count for its own shelf. You can run the same check for any category with three sources.

1. Count demand. Search the complaint index for your category. The question is how many separate buyers describe the same missing thing. 1M+ complaints are indexed, so most categories have a count. Review-site pain points show which complaints hit several vendors, which is the sign of a gap rather than one bad product.
2. Count supply. List how many products sell the thing today, then look at what they earn in revenue intelligence. Revenue is verified through payment providers, not self-reported. If the median is under $200 MRR and the category has 100+ products, you are looking at the AI agent shape.
3. Compare. High demand with few earning products is a go signal. High demand with many earning products means you need a wedge. Low demand with few products means no one wants it. Write down which of the three you are in before you build.

Then test the buyer, not the product. Ask ten people to put a small deposit down on a one-page offer. For the full method, read our profitable SaaS and micro SaaS ideas ranking and SaaS ideas that make money, then run your own category through BigIdeasDB discover.

Methodology and data sources

Every source below is dated. Where a source is a third party, we quote its figures unrounded and attribute them. Where it is ours, we show the sample size and withhold medians under 30 earning products.

What this cannot tell you. It cannot tell you profit, because neither dataset includes costs. It cannot tell you whether a lifetime-deal buyer would pay monthly. And it cannot rank any category with certainty: with 6 to 33 products per category in the marketplace data, the order of neighbours (AI writing and AI agents, or WordPress plugins and social media management) could swap with a different year of data. The top and bottom groups are the part to trust.

FAQ

BigIdeasDB is an AI research suite that finds, validates and sizes software opportunities from 1M+ real complaints and data points. See revenue intelligence, compare plans or start with discover.