The Biggest Chain in America Isn't the One You Think

Ask anyone which chain has the most US locations and they'll say McDonald's. They'll be wrong by roughly 6,500 stores. That was the first surprise when we assembled this dataset; the second was that the "biggest" chain by count is also the one closing hundreds of locations a year.

The dataset: scraped public store locators for the largest US quick-service chains, roughly 80,000 locations across the top seven alone. The usual caveat applies, and it's worth spelling out because it shapes several findings below. These are snapshot counts from public locator data, not audited store counts. Locators lag closures, occasionally list openings early, and in this category have a specific quirk: co-branded units (a combined Taco Bell/KFC) can appear in two locators at once, while delivery-only ghost kitchens mostly appear in none. Our pipeline flags both, but every figure here is directional. This is the same methodology as the Starbucks vs Dunkin analysis that started this series, pointed at the whole category.

The League Table

Approximate US location counts from our snapshot:

Chain	US Locations (approx.)	Ownership Model
Subway	~20,000	~100% franchised
Starbucks	~16,000	Mix of company-owned and licensed
McDonald's	~13,500	~95% franchised
Dunkin'	~9,500	~100% franchised
Burger King	~7,000	~100% franchised
Taco Bell	~7,000	~95% franchised
Wendy's	~6,000	~95% franchised

Subway's lead is real but hollow. Fifty percent more US locations than McDonald's, a fraction of the systemwide sales. Average unit volumes tell the story: a typical McDonald's is commonly reported around $3.5–4M a year, a typical Subway under $500K. The count is an artifact of Subway's model, tiny footprints and low franchise entry cost in strip-mall and gas-station real estate, and as we'll get to, the count is falling fast.

The deeper point is that store count and market power are different maps. Weight the dots by estimated unit volume and the landscape recenters around McDonald's, Starbucks, and Chick-fil-A, which has fewer than 3,500 US locations but unit volumes commonly reported above $7M per standalone store, among the highest in the industry.

One more thing before the geography. Outside Starbucks and Chipotle, this is overwhelmingly a map of franchisee decisions: each dot is where an operator with local knowledge committed their own capital. That makes aggregate location data an unusually honest signal about where the industry believes demand lives, which is precisely why it's worth scraping.

Per Capita: Where the Fast Food Belt Actually Is

Raw counts favor big states; California and Texas top almost every chain's absolute numbers and tell you nothing. Per capita is where the geography gets interesting.

The South dominates density. Alabama, Mississippi, Tennessee, Kentucky, Oklahoma, and West Virginia consistently rank near the top for combined locations per 100,000 residents. Cheap real estate, car-dependent development, highway-oriented town layouts: everything about the built environment favors the drive-through format.

The urban Northeast under-indexes, with one loud exception. Massachusetts and New York rank low on burger-chain density, but Dunkin's fortress pulls their totals up; Massachusetts has more Dunkins per capita than almost any state has of any chain. And the Mountain West is genuinely sparse. Wyoming, Montana, and the Dakotas have plenty of fast food per town but few towns, and several national chains skip large rural stretches entirely, leaving regional players to fill the gap.

A sample of the per-capita table (combined top-seven locations per 100k residents, approximate):

State	Combined locations per 100k	Notable skew
Alabama	~28	Burger chains + Subway heavy
Oklahoma	~27	Sonic's home state (excluded from top-7 count, adds ~10 more)
Tennessee	~26	High across all burger brands
Ohio	~25	Wendy's home-state over-index
Massachusetts	~24	Dunkin alone contributes ~15
Texas	~22	Whataburger adds ~2.5 on top
California	~18	Taco Bell and Starbucks lead
New York	~16	Dunkin-driven; burger chains sparse in NYC
Montana	~14	Lowest tier; long gaps between markets

Per chain, the per-capita view exposes home turf: Taco Bell over-indexes in California and the Southwest, Wendy's leans Ohio and the Midwest. Burger King is the odd one, unusually flat, present everywhere and dominant nowhere, which mirrors its long-running traffic struggles. Flat density is not a compliment in this business.

The Chains the National Numbers Miss

A top-seven table hides the most distinctive feature of American fast food: regional chains that dominate their territory more completely than any national brand dominates anything.

Whataburger keeps roughly 750 of its 1,000+ locations in Texas, and in large swaths of the state it out-locates McDonald's. Culver's is approaching 1,000 locations across the Upper Midwest on a butter-burger-and-custard formula with some of the strongest unit economics in burgers, and its push south is one of the fastest footprint growth stories in the dataset. In-N-Out sits around 400 locations, deliberately slow, constrained by its own distribution model, so every new state entry is a market event. Then the Southeast has Bojangles, Zaxby's, and Cook Out; Nebraska has Runza; Oklahoma has Braum's. Nearly invisible nationally, unavoidable locally.

For site-selection work these players matter more than their counts suggest. They hold the best real estate in their home markets and set the local price and quality bar. Build a Southeast demand model that ignores Cook Out and Bojangles and you will systematically overrate what's available; we've watched it happen.

Trajectory: Who's Growing, Who's Shrinking

A single snapshot shows position. Repeated snapshots show trajectory, and trajectory is where the money is.

Subway is the great contraction, and it's the finding that makes the quarterly-refresh case better than any sales pitch we could write. It peaked above 27,000 US locations around 2015 and has closed thousands since, several hundred to over a thousand net a year. You will not find a list of closed addresses in any press release; the only way to see it at address-level resolution is to diff locator snapshots over time, which is exactly what we do. The closures concentrate in over-saturated markets where franchisees cannibalized each other, and in the low-traffic host locations (gas stations, dying malls) that padded the count during the boom years. Quarter by quarter, the disappearing addresses trace exactly where the model stopped working, which is a more honest account than anyone's investor deck will give you.

Taco Bell and Chick-fil-A are the confident growers, and their new dots skew toward high-visibility pad sites with multi-lane drive-throughs, expensive real estate that signals strong unit economics. Burger King is pruning: its franchisor has publicly pushed to close low-volume restaurants while remodeling the rest, and in locator data that shows up as closures clustering in weak trade areas while the total drifts down slowly. Starbucks and Dunkin, as we covered in the coffee-chain deep dive, are converging on drive-through and pickup formats, visible in their locators' attribute fields rather than the counts.

The Stores Themselves Are Changing Shape

Counting locations misses a second shift that's sitting right there in the attribute data. New builds across nearly every chain are drive-through-first, and several growth brands (Dutch Bros, Checkers, Taco Bell's newer "Defy" concepts) build double- or quadruple-lane formats with minimal or no dining room. The drive-through attribute's prevalence among recently added locations runs far above the legacy base.

Dining rooms, meanwhile, are quietly shrinking or closing. Post-2020, a meaningful share of locations list drive-through or pickup-only service models, visible in hours fields ("dining room closed") and service attributes. If you're a commercial landlord, this is the finding that matters: the next generation of QSR real estate needs stacking lanes and smaller pads, not 60-seat dining rooms.

One adjacent trend deserves a mention because it keeps surprising clients. The fastest-growing competitor to fast food isn't a restaurant chain; it's convenience stores, led by food-forward operators like Wawa, Buc-ee's, and Casey's, a shift we mapped in our US convenience store landscape analysis. Any serious QSR market model now includes c-store food service in the competitive set.

Who Uses This and For What

This dataset feeds specific, recurring workflows. Franchise development teams map competitor density and their own nearest units before awarding a territory, to estimate cannibalization and remaining demand; scraped locator data is the only current, unified source across all brands. Site scoring leans on co-tenancy, since a corner with a McDonald's and a Chick-fil-A validates traffic while a corner with two shuttered franchises is a warning. Emerging chains hunt whitespace where incumbent density proves category demand but their niche is unserved. Investors diligencing a franchisee group diff historical snapshots to verify claimed unit growth and catch quiet closures before disclosures. And food distributors allocate sales coverage against actual restaurant counts rather than stale directories.

How We Collect It

Every chain above publishes a locator, and every one implements it differently: radius-search APIs with different page sizes, embedded JSON, third-party locator platforms like Yext and Rio SEO, and in several cases commercial bot protection in front of all of it. The engineering problems are consistent even when the stacks aren't. You need geographic sweeps that guarantee national coverage without gaps (the failure mode is silent: a pagination bug quietly drops half of Kansas and your state table looks plausible anyway, which is why we validate counts against known totals). You need address and hours normalization across dozens of formats, and deduplication for the co-branded units that appear in two locators.

ScrapeAny runs this as a managed pipeline. You name the chains and the cadence, one-time, quarterly, or monthly; we handle collection, QA, and normalization; you get an analysis-ready dataset as CSV, JSON, API, or loaded into your warehouse. Chains not already on our list typically take a few days to add.

Map Your Competitive Landscape

The US fast food map rewards whoever reads it most carefully, whether that's a franchisor picking territories, an investor verifying a growth story, or a brand hunting whitespace. If your team needs current location data for QSR, coffee, c-stores, or any physical retail category, tell us which chains and markets matter and we'll get you a working sample dataset within days.