Which data products make over $10K/month now?

Which data products make over $10K/month now?

Yes, data products are making well over $10,000 a month today, from small e-commerce databases at roughly $18,000 MRR to digital-intelligence businesses generating hundreds of millions of dollars a year.

At the small end, NicheScraper is currently tracked at $18,000 in monthly recurring revenue. The figure is self-reported by founder Eric Smith, but it was checked again only days ago and has held at the same level through more than a month of daily tracking.

Nomad List gives us another founder-scale example. Public revenue updates put the location database and community somewhere between roughly $12,000 and $22,000 a month depending on which recent disclosure is used. The exact figure moves, but the important part for this question does not: Nomad List still clears $10,000.

Then the scale jumps quickly. ScrapingBee had reached roughly $5 million ARR before joining Oxylabs. Similarweb generated $282.6 million of revenue in 2025 and has since passed $300 million ARR. In its latest quarterly results, Similarweb also disclosed three new multi-year enterprise agreements carrying seven-figure annual commitments.

So $10,000 a month is a meaningful milestone for an indie founder, while mature data companies can grow hundreds or thousands of times larger.

Data product	What people pay for	Latest useful revenue evidence	Confidence above $10K/month
Similarweb	Web, app and digital-market data	More than $300M ARR	Very high
DataForSEO	Search, keyword and commerce APIs	Multi-million-euro quarterly turnover	Very high
ScrapingBee	Web-scraping API	About $5M ARR before acquisition	Very high
BuiltWith	Website technology database	Passed $40K/month years ago; much larger today	Very high
Nomad List	City and remote-work database	Recent estimates/disclosures above $10K/month	High
NicheScraper	E-commerce product intelligence	$18K MRR, recently rechecked	High, self-reported
Mentions	AI brand-visibility monitoring	$20K MRR publicly disclosed	High, self-reported

What should we actually call a data product?

A data product is something customers primarily pay for because it collects, organizes, refreshes or gives them access to information they would otherwise struggle to get.

BuiltWith fits easily. It tracks which technologies websites use and turns those observations into searchable company lists, market data and sales intelligence.

DataForSEO also fits. Customers pay for programmatic access to search results, keyword data, shopping information and other constantly changing datasets.

Nomad List sits at the consumer end. Its core product grew from structured information about cities: prices, internet quality, weather, safety and other variables remote workers use when deciding where to live.

Some businesses sit close to the boundary. Data Fetcher reached more than $20,000 MRR by letting Airtable users pull information from external APIs. That's a good business, but most of the value comes from moving somebody else's data into Airtable. We would classify it as data infrastructure rather than a proprietary data product.

Keeping that boundary matters. If every SaaS product that reads or stores data qualifies, almost the whole software industry ends up in the category.

Why is $10K/month much easier with business data?

Business data can reach $10,000 a month with surprisingly few customers because the information can directly affect sales, investment or operating decisions.

Consider the arithmetic. A consumer database charging $10 a month needs 1,000 paying users. A professional product charging $300 needs 34. At $1,000 a month, ten customers get the business across the line.

Real pricing already sits in those ranges. BuiltWith's higher plans run into hundreds of dollars per month. Extra Points Library charges $300 a month for professional access to a collection of college-sports budgets, contracts and related documents. Enterprise intelligence products can go far higher.

Similarweb shows the extreme. Its latest filings say 454 customers were spending at least $100,000 annually at the end of 2025. One customer at that minimum is worth more than $8,000 a month. Two would already produce more revenue than the entire benchmark we are investigating.

Obscure B2B datasets can therefore outperform much more popular consumer databases. They need a handful of people who care a lot, rather than thousands who care a little.

Monthly price	Customers needed for $10K/month
$10	1,000
$25	400
$50	200
$100	100
$300	34
$500	20
$1,000	10
$2,500	4

Which kinds of data products keep showing up above $10K/month?

Search APIs, web intelligence, specialized professional databases, e-commerce intelligence and newer AI-monitoring products are the clearest groups we found above or around the $10,000-a-month line.

The biggest cluster is web and search data. DataForSEO sells search and commerce data through APIs. ScrapingBee sells reliable access to web pages without forcing each customer to manage proxies and anti-bot problems. BuiltWith continuously maps technologies to websites.

A second group turns a narrow dataset into business intelligence. NicheScraper helps merchants investigate products and competitors. Extra Points Library organizes hard-to-find college-sports documents. Exploding Topics built a substantial business around detecting and organizing emerging trends before Semrush acquired it.

Consumer databases are rarer, although Nomad List proves they can work.

A newer category has appeared around generative AI. Products such as Mentions monitor whether brands appear in ChatGPT, Perplexity and other AI answers. Similarweb has also been pushing its data directly into AI workflows and agents.

Those categories look different on the surface. The commercial pattern is pretty similar: the underlying information changes often enough, or is painful enough to collect, that customers keep paying for access.

Data-product model	Examples	What customers are really paying to avoid
Search and scraping APIs	DataForSEO, ScrapingBee	Running collection infrastructure themselves
Web intelligence	BuiltWith, Similarweb	Crawling and interpreting the web at scale
Vertical databases	NicheScraper, Extra Points Library	Rebuilding a specialized research process
Consumer databases	Nomad List	Comparing fragmented information manually
AI visibility data	Mentions, Similarweb	Rechecking AI answers across models and prompts

Are APIs the easiest data products to push past $10K/month?

APIs have one of the clearest paths to $10,000 a month because customers can build the data directly into products and internal workflows.

ScrapingBee is a good example. Its founders documented reaching $10,000 MRR after roughly 18 months. The business then reached $20,000 only three months later and eventually grew to roughly $5 million ARR before its acquisition.

DataForSEO operates at a much larger scale. The company sells search-engine, keyword, product and other datasets programmatically, and recent Estonian corporate figures show quarterly turnover measured in millions of euros.

The appeal is easy to understand from the customer's side. An SEO platform using fresh Google results every day cannot realistically cancel its supplier every time somebody forgets to open a dashboard. The API has become part of the product.

That gives API businesses excellent retention when the integration is important. The trade-off is cost. Scraping, proxies, browsers, storage and API calls all consume infrastructure, so a high-revenue API can be less economically attractive than a small database with almost zero delivery cost.

Still, the evidence is strong enough for us to put machine-readable data near the top of the list of proven models.

Why do scraping and search-data products make so much money?

Scraping and search-data products sell relief from a tedious engineering problem that never really goes away.

Anybody can theoretically request a webpage. Doing it millions of times while handling blocks, CAPTCHAs, proxies, browser rendering, retries, changing HTML and inconsistent output is a very different job.

Search data has the same issue. Collecting one Google result is trivial. Supplying millions of reliable keyword, ranking and shopping observations every day requires infrastructure, monitoring and constant maintenance.

That explains the commercial success of companies such as ScrapingBee and DataForSEO better than the raw data itself. Customers are paying to make a messy recurring problem disappear from their own engineering roadmap.

Freshness makes the model even stronger. Yesterday's SERP can already be wrong today. Product listings move. Prices change. Websites change technology. A customer who needs the answer again tomorrow has a reason to keep the subscription running.

Web data has an unusually useful combination: collection is technically annoying, the information decays quickly, and customers frequently need it in machine-readable form. Not glamorous, but very sellable.

Can a simple searchable database really make $10K/month?

Yes, a searchable database can still become a five-figure monthly business when it answers a question people repeatedly care about.

Nomad List may be the cleanest small-company example. The product began with a spreadsheet comparing cities for remote workers and gradually became a searchable database covering costs, connectivity, weather, safety and other location variables. More than a decade later, recent public revenue estimates still put Nomad List above the $10,000-a-month mark.

BuiltWith shows how much further the idea can go in B2B. Its basic question is almost childishly simple: what technology does this website use? That observation becomes useful for sales teams once it can be searched across millions of companies.

A salesperson selling an alternative to Shopify might want every company currently using Shopify. A hosting company could look for websites running a particular infrastructure stack. Investors can measure which technologies are gaining or losing adoption.

The interface can remain simple because the value lives in the answer.

Searchable databases work especially well when users repeatedly return with slightly different questions. Each additional filter, historical record and newly indexed entity then makes the database more useful without changing the basic product.

Does valuable data have to be secret?

No, many successful data products make money from information that anyone could theoretically find.

BuiltWith observes public websites. Scraping products fetch public web pages. Extra Points Library organizes many documents originating from universities and public institutions. Nomad List combines openly available facts with community and proprietary observations.

The commercial advantage comes from doing work that users do not want to repeat themselves.

History is one source of that advantage. A new competitor can crawl today's web, but it cannot instantly reconstruct years of historical technology changes.

Normalization is another. Ten million messy observations become much more useful once addresses, company names, categories, dates and identifiers have been cleaned into predictable fields.

Coverage also compounds. The difference between checking 100 sites and continuously checking hundreds of millions of domains is enormous even though every individual observation may be public.

Similarweb pushes further into proprietary data by combining massive amounts of web and app activity with its own models. Its latest filings describe digital data that can now be consumed directly by analytics systems and AI applications.

Secrecy helps when it exists, but systematic collection, history and reliability are already enough to build very large businesses.

How fresh does a data product need to be?

The best subscription data products usually contain information that becomes less useful with age.

Search rankings can change in hours. E-commerce products can trend for a few weeks and disappear. Websites add and remove technologies. Prices move. Companies hire and fire. AI models change the brands they mention.

That natural decay creates recurring demand.

NicheScraper currently makes about $18,000 MRR from e-commerce intelligence. Merchants care about products selling now because a supposedly “winning” product from two years ago has little commercial value today.

BuiltWith has the same dynamic in B2B. Knowing that a company installed a technology recently can create a sales opportunity. A five-year-old record is mostly historical research.

Historical data still adds value because it shows direction. The strongest product can tell a customer what exists today, what changed recently and how unusual that change is compared with the past.

That combination is difficult to reproduce quickly. A competitor can start collecting current observations tomorrow, but tomorrow is also day one of its historical dataset.

Can a tiny niche database really beat a huge general database?

Yes, a small vertical database can produce serious revenue when each record helps somebody make an expensive decision.

Extra Points Library is a useful example even though its standalone revenue has not been disclosed clearly enough for us to call it a confirmed $10,000-a-month product. The database contains roughly 15,000 college-sports budgets, contracts, conference documents and related records. Professional access costs $300 a month.

Fifteen thousand documents is tiny compared with the billions of observations processed by web-intelligence companies. The audience is also tiny. Yet only 34 customers paying $300 would produce more than $10,000 in monthly revenue.

NicheScraper works with another narrow commercial problem: merchants trying to identify products and competitors in e-commerce. Its recently checked $18,000 MRR shows how far a focused dataset can go without becoming a general-purpose market-intelligence platform.

The useful metric is economic relevance per record. A database with 3,000 carefully chosen companies can be worth more than one containing three million generic businesses if those 3,000 companies represent valuable prospects, acquisition targets or investment opportunities.

For a small founder, that is encouraging. Recreating Similarweb would require enormous data infrastructure. Becoming the best source for one neglected professional question can start with a few thousand records.

Can one person build a data product above $10K/month?

Yes, data products can support unusually small teams because collection, updates, payments and delivery are all things software can automate.

BuiltWith became an early example of this. Co-founder Andrew Rogers later wrote that Gary Brewer's side project was already doing roughly $40,000 a month around the time they met, while Brewer was still reluctant to leave his job.

Nomad List gives us a more consumer-facing version. Pieter Levels has kept the business inside a portfolio run with essentially no conventional staff, relying heavily on automation. Whatever recent revenue figure we choose within the range of public estimates, the product remains above our $10,000 threshold.

The economics can deteriorate quickly when a database requires large amounts of manual research. Every new customer then creates more human work, and the business begins to look like an agency hidden behind a search box.

Automation changes that equation. A crawler can discover new records overnight. Scripts can refresh prices. Users can contribute corrections. APIs can deliver the same dataset to thousands of customers.

Small-team data products work best when the database becomes larger without the research team having to grow at the same speed.

How long does it take a data product to reach $10K/month?

Reaching $10,000 MRR usually takes longer than a viral launch, but successful data products can accelerate sharply after customers start depending on them.

ScrapingBee gives us unusually clean founder-reported history. It took roughly 18 months to get to $10,000 MRR. Reaching $20,000 then took only another three months.

Data Fetcher, although closer to data infrastructure than a proprietary dataset, followed a similar curve. Founder Andy Cloke documented reaching $10,000 MRR around two years after launch, with the business later moving above $20,000.

That pattern makes sense for data businesses. Early on, the founder has to build both the product and the underlying dataset or collection system. Search traffic, historical records, integrations and customer habits then start accumulating.

A mature data product can become much harder to displace than it looked during its first year because a competitor launching later starts without the historical database, distribution and customer integrations.

These businesses are not effortless passive income. Several of the strongest examples took years to become easy-looking businesses.

Is AI killing data products or creating more of them?

AI is currently creating more opportunities for differentiated data products while making generic information much easier to copy.

Similarweb offers unusually concrete evidence. The company has now passed $300 million ARR, and its latest quarterly update said it signed three large multi-year contracts whose combined value was about $60 million. Those customers included AI-driven companies using Similarweb's data in AI-related workflows.

The company has also been embedding its datasets directly inside tools such as Manus and Perplexity. A user can ask an AI agent for competitive research while Similarweb supplies fresh keyword, referral and traffic data behind the answer.

A second market is appearing around measuring AI itself. Mentions reached a publicly disclosed $20,000 MRR by tracking how brands appear in answers from ChatGPT, Perplexity and similar products.

This changes what makes a dataset defensible. A generic list of companies with descriptions scraped from their homepages has become easier for an AI agent to rebuild. Continuously collected prices, search results, transactions, model outputs, historical changes and niche records are much harder to regenerate on demand.

AI raises the bar. Data products with weak information advantages will feel more disposable. Products connected to fresh or proprietary streams have gained another distribution channel and another class of customer.

Which supposed $10K/month data products should we leave off the list?

We should leave out products when the public evidence cannot clearly separate current data-product revenue from old milestones, company-wide revenue or adjacent services.

AI Directories is one example. Some startup databases describe the project as reaching $10,000 MRR, while other accounts mix a $5,000 recurring-revenue figure with $10,000 in total revenue. That is too messy for a list whose whole point is knowing what clears $10,000 per month now.

Extra Points has recently generated roughly $25,000 in total monthly recurring revenue, but the business includes a newsletter and other products alongside its database. The Library is described as its fastest-growing revenue stream, yet we do not have enough product-level disclosure to conclude that the database alone has crossed $10,000.

ScrapingAnt publicly disclosed roughly $19,800 MRR in an earlier founder interview. That proves the scraping API crossed the threshold at one point, although the number is too old for us to present $19,800 as its current MRR.

BuiltWith has almost the opposite evidence problem. We know directly that it crossed $40,000 per month many years ago, and today's product footprint and pricing make a fall below $10,000 extraordinarily implausible. Current revenue estimates vary widely, however, so quoting a precise modern number would create fake accuracy.

This stricter filter produces a shorter list, which is exactly what we want. “It probably still makes $10K” and “we have recent evidence that it makes $10K” are different claims.

So which data products make over $10K/month now?

The clearest $10K+/month data products today are search and scraping APIs, web-intelligence databases, e-commerce research tools, specialized professional datasets, location databases and the emerging category of AI-visibility data.

The strongest current evidence spans almost absurdly different scales. NicheScraper sits around $18,000 MRR. Nomad List remains above the threshold despite being more than a decade old. ScrapingBee reached roughly $5 million ARR before joining Oxylabs. Similarweb has now passed $300 million ARR.

After comparing those businesses, a clear pattern emerges. The best data products solve questions people need answered repeatedly. Their information changes, takes real work to collect, becomes more useful as history accumulates, or helps customers make decisions worth far more than the subscription.

APIs are particularly strong because they turn data into infrastructure. Vertical databases need fewer customers because specialized information can support much higher pricing. Continuously refreshed datasets have a built-in reason for people to renew.

The weakest idea is simply packaging facts once and hoping people keep buying them. The businesses that endure keep collecting.

So if we wanted to build a data product with a realistic chance of reaching $10,000 a month today, we would look for a narrow group of customers repeatedly asking the same expensive question, then build the dataset that gives them a faster answer than they can get anywhere else.