Top 100 Cities by Start-up Density per 100,000 Residents, 2025
StatRanker • Cities & Innovation
Start-up Density by City: 2025 Comparative Ranking
Updated: April 27, 2026 · Unit: active start-ups per 100,000 residents · Edition: 2025
Start-up density measures how concentrated entrepreneurial activity is relative to population size. Instead of ranking cities only by raw company totals, this measure asks how many active start-ups exist per 100,000 residents. That makes compact founder hubs easier to compare with very large metropolitan economies.
The values are rounded cross-city estimates for comparing urban ecosystems. They combine city-level start-up counts from ecosystem databases with population baselines for the relevant urban or metropolitan area. Because start-up databases, city boundaries and activity definitions differ, the table should be read as a comparison guide rather than an administrative count.
Definition used: start-up density = active start-ups ÷ resident population × 100,000. The preferred geography is a functional urban area or equivalent metropolitan boundary. Where city-level data are used, the population denominator follows the same boundary as closely as the available sources allow.
100cities in the table
≈620top rounded value
2025ranking edition
How to read the table
- High density usually points to compact founder networks, university pipelines, early-stage capital and repeat entrepreneurship.
- Low density does not mean a weak ecosystem; large cities can host many start-ups but still score lower per resident.
- Density is not quality. It should be read alongside funding, exits, survival rates, sector depth and scale-up outcomes.
start-up density
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ecosystem intensity
start-ups per resident
urban innovation
Why compact hubs rank high
The leading entries are compact, network-dense ecosystems where start-up activity is unusually concentrated relative to population size. Palo Alto and Cambridge stand out because university research, venture capital and repeat-founder networks sit inside relatively small local populations. Tel Aviv, San Francisco, Tallinn and Zurich show similar density patterns in different institutional settings.
Table: 100 cities by start-up density, 2025
Cities are ranked by estimated active start-ups per 100,000 residents. Figures are rounded, so neighbouring ranks should be treated as approximate rather than exact statistical differences.
| Rank | City, country | Ecosystem note | Start-ups per 100,000 |
|---|---|---|---|
| 1 | Palo Alto, United States | Compact venture-university corridor | ≈620 |
| 2 | Cambridge (MA), United States | Biotech and deep-tech spillovers | ≈540 |
| 3 | Tel Aviv, Israel | High founder density and export focus | ≈510 |
| 4 | San Francisco, United States | Platform innovation and venture scale | ≈460 |
| 5 | Tallinn, Estonia | Digital state and fast incorporation | ≈430 |
| 6 | Zurich, Switzerland | R&D intensity and global talent | ≈410 |
| 7 | Stockholm, Sweden | Strong scale-up pipeline | ≈395 |
| 8 | Amsterdam, Netherlands | International market access | ≈372 |
| 9 | Singapore, Singapore | Regional HQ and fintech hub | ≈360 |
| 10 | Dublin, Ireland | Multinational spillovers and gateway role | ≈345 |
| 11 | Somerville (MA), United States | Boston-area research spillovers | ≈330 |
| 12 | Mountain View, United States | Silicon Valley platform cluster | ≈325 |
| 13 | Menlo Park, United States | Venture capital proximity | ≈318 |
| 14 | Austin, United States | Talent inflows and software growth | ≈305 |
| 15 | Seattle, United States | Cloud and engineering depth | ≈295 |
| 16 | Boston, United States | Research universities and healthtech | ≈288 |
| 17 | New York City, United States | Finance, media and enterprise scale | ≈275 |
| 18 | Los Angeles, United States | Media, ecommerce and hardware links | ≈260 |
| 19 | Toronto, Canada | AI talent and diversified tech | ≈255 |
| 20 | Vancouver, Canada | Pacific gateway and talent inflows | ≈248 |
| 21 | London, United Kingdom | Global finance and software cluster | ≈240 |
| 22 | Paris, France | AI, fintech and public support | ≈232 |
| 23 | Berlin, Germany | Creative tech and venture depth | ≈228 |
| 24 | Munich, Germany | Deep tech and industrial links | ≈220 |
| 25 | Copenhagen, Denmark | Nordic design and health innovation | ≈218 |
| 26 | Helsinki, Finland | Gaming, hardware and research talent | ≈214 |
| 27 | Oslo, Norway | Energy, maritime and software links | ≈210 |
| 28 | Vienna, Austria | Central European gateway | ≈205 |
| 29 | Barcelona, Spain | Mobile technology, design and international talent | ≈202 |
| 30 | Madrid, Spain | Enterprise, fintech and national scale | ≈198 |
| 31 | Lisbon, Portugal | Remote-work talent and start-up events | ≈196 |
| 32 | Geneva, Switzerland | International organizations and fintech | ≈190 |
| 33 | Lausanne, Switzerland | University spinouts and medtech | ≈188 |
| 34 | Brussels, Belgium | EU policy and B2B services | ≈186 |
| 35 | Luxembourg City, Luxembourg | Fintech and cross-border services | ≈184 |
| 36 | Milan, Italy | Finance, design and industrial services | ≈180 |
| 37 | Rome, Italy | Public services and digital firms | ≈176 |
| 38 | Warsaw, Poland | Regional scale and software talent | ≈174 |
| 39 | Prague, Czechia | Central European software hub | ≈172 |
| 40 | Budapest, Hungary | Engineering talent and regional demand | ≈168 |
| 41 | Riga, Latvia | Baltic fintech and software firms | ≈162 |
| 42 | Vilnius, Lithuania | Fintech and shared-services base | ≈160 |
| 43 | Reykjavik, Iceland | Small population base and digital firms | ≈158 |
| 44 | Bangalore, India | Large engineering and SaaS base | ≈146 |
| 45 | Hyderabad, India | Enterprise tech and cloud services | ≈142 |
| 46 | Delhi, India | Consumer tech and policy proximity | ≈138 |
| 47 | Mumbai, India | Finance, media and consumer markets | ≈135 |
| 48 | Tokyo, Japan | Corporate partnerships and deep market | ≈132 |
| 49 | Seoul, South Korea | Gaming, electronics and platform firms | ≈130 |
| 50 | Beijing, China | AI, hardware and policy capital | ≈128 |
| 51 | Shanghai, China | Hardware, transport and finance | ≈126 |
| 52 | Shenzhen, China | Hardware supply chains | ≈124 |
| 53 | Hong Kong, China | Finance and regional gateway | ≈122 |
| 54 | Sydney, Australia | Fintech and enterprise software | ≈120 |
| 55 | Melbourne, Australia | Healthtech and university talent | ≈118 |
| 56 | Auckland, New Zealand | Pacific software and services hub | ≈116 |
| 57 | Wellington, New Zealand | Creative tech and small-city density | ≈114 |
| 58 | Santiago, Chile | Latin American scale-up base | ≈112 |
| 59 | São Paulo, Brazil | Large regional market and fintech | ≈110 |
| 60 | Mexico City, Mexico | Fintech and consumer markets | ≈108 |
| 61 | Bogotá, Colombia | Regional fintech and B2B services | ≈106 |
| 62 | Buenos Aires, Argentina | Software talent and regional founders | ≈104 |
| 63 | Lima, Peru | Growing regional start-up base | ≈102 |
| 64 | Dubai, United Arab Emirates | Regional HQ and fintech gateway | ≈100 |
| 65 | Abu Dhabi, United Arab Emirates | Public investment and deep-tech firms | ≈98 |
| 66 | Riyadh, Saudi Arabia | Fast-growing national ecosystem | ≈96 |
| 67 | Doha, Qatar | Fintech and public-sector demand | ≈94 |
| 68 | Istanbul, Turkey | Large market and gaming strength | ≈92 |
| 69 | Athens, Greece | Tourism tech and software firms | ≈90 |
| 70 | Bucharest, Romania | Engineering and regional services | ≈88 |
| 71 | Sofia, Bulgaria | Software exports and engineering talent | ≈86 |
| 72 | Belgrade, Serbia | Gaming and software outsourcing base | ≈84 |
| 73 | Zagreb, Croatia | Regional software and mobility firms | ≈82 |
| 74 | Cape Town, South Africa | African scale-up and fintech base | ≈80 |
| 75 | Johannesburg, South Africa | Corporate and fintech demand | ≈78 |
| 76 | Nairobi, Kenya | Mobile money and regional services | ≈76 |
| 77 | Lagos, Nigeria | Large fintech and consumer market | ≈74 |
| 78 | Cairo, Egypt | Large youth market and fintech | ≈72 |
| 79 | Casablanca, Morocco | North African business hub | ≈70 |
| 80 | Accra, Ghana | Fintech and regional services | ≈68 |
| 81 | Tunis, Tunisia | Engineering and regional export talent | ≈66 |
| 82 | Kigali, Rwanda | Policy-enabled digital services | ≈64 |
| 83 | Addis Ababa, Ethiopia | Emerging local-services ecosystem | ≈62 |
| 84 | Hanoi, Vietnam | Software and education technology | ≈60 |
| 85 | Ho Chi Minh City, Vietnam | Consumer internet and fintech | ≈58 |
| 86 | Bangkok, Thailand | Tourism tech and ecommerce | ≈56 |
| 87 | Jakarta, Indonesia | Large digital consumer market | ≈54 |
| 88 | Manila, Philippines | Services, fintech and outsourcing links | ≈52 |
| 89 | Kuala Lumpur, Malaysia | Regional digital-services hub | ≈50 |
| 90 | Taipei, Taiwan | Hardware and semiconductor links | ≈48 |
| 91 | Hsinchu, Taiwan | Semiconductor and deep-tech cluster | ≈46 |
| 92 | Osaka, Japan | Manufacturing and urban services | ≈42 |
| 93 | Hamburg, Germany | Logistics, media and B2B software | ≈40 |
| 94 | Frankfurt, Germany | Finance, B2B software and enterprise services | ≈38 |
| 95 | Manchester, United Kingdom | Regional digital and university-linked firms | ≈36 |
| 96 | Montreal, Canada | AI research, gaming and software talent | ≈34 |
| 97 | Düsseldorf, Germany | B2B services, telecom and enterprise tech | ≈32 |
| 98 | Brisbane, Australia | Software, climate tech and regional scale | ≈30 |
| 99 | Ottawa, Canada | Public-sector technology and software firms | ≈28 |
| 100 | Edinburgh, United Kingdom | Fintech, university talent and public-sector demand | ≈26 |
Boundary note: each city appears once to avoid city/metro duplication. Values are rounded estimates, so small differences between neighbouring ranks should not be overinterpreted.
Interpretation and limits
Start-up density is useful for comparing ecosystem intensity, but it is not an overall city score. A dense ecosystem may have many small companies and limited scale-up capacity, while a large city with lower density may still host major venture funding, corporate R&D and large founder networks.
Best companion indicators
- Absolute start-up count: shows ecosystem scale.
- Venture funding per start-up: shows capital depth.
- Start-up survival rate: shows whether new firms remain active.
- Scale-up count: shows whether early ventures grow into larger firms.
- University and research output: helps explain deep-tech and biotech clusters.
- Business formation rate: gives a wider entrepreneurship context outside venture-backed firms.
Data note: public start-up databases differ in coverage and inclusion rules. Use this ranking for orientation, then check city-level source pages before making investment, relocation or policy decisions.
Primary sources
