StartupFlow AI

Data report

The 2026 State of Startup Credits

We hold 1,084 credit programs and 1,367 accelerators as structured records rather than as prose, which lets us ask questions of the market that a list cannot answer. The clearest finding is also the most annoying: 94.7% of credit programs will not tell you what they are worth.

Most programs do not publish a number

Of 1,084 credit programs, only 57 publish a per-startup figure in US dollars that we can use. The rest either state nothing, quote a figure in another currency, or publish the size of the whole fund rather than what one startup receives.

This is why we do not put a dollar total on your eligibility results. Any headline number assembled from published amounts is drawn from about a twentieth of the market and would be presented as though it covered all of it.

94.7%

Publish no usable amount

1,027 of 1,084 programs

$25,000

Median published credit

Across the 57 that state one

0.8%

Require a partner referral

9 programs, per published rules

What accelerators actually cost

The accelerator side of the catalog is well populated, so these are the figures we hold highest confidence in. Medians rather than means throughout: the distributions are skewed by a handful of very large programmes and an average would flatter them.

6%

Median equity taken

Across 1,367 accelerators

$120,000

Median funding offered

Where a figure is published

21.4%

Take no equity at all

293 programmes

27%

Run remotely

369 programmes, no relocation

Two of those are worth sitting with. More than a fifth of accelerators take no equity, which contradicts the assumption that acceleration always costs ownership. And 27% run remotely, so the standard advice to move to a hub is less binding than it was.

Where the accelerators are

Concentration is real but less extreme than the discourse suggests. The US holds the largest share by a wide margin, and India is second by volume rather than any European market.

Accelerators by country of base, top 12 of 1,367.
United States
346 programmes
India
121 programmes
United Kingdom
64 programmes
Canada
52 programmes
Pakistan
28 programmes
Germany
25 programmes
Australia
24 programmes
France
22 programmes
United Arab Emirates
20 programmes
China
20 programmes
Nigeria
20 programmes
Brazil
19 programmes

Methodology, and what is missing

Every figure above is generated from our live catalog by a script in our API (`credits-report.ts`), against programs that are active and approved. You can regenerate it and check us. Three limits matter, and we would rather state them than have you find them.

Amounts are barely published

935 programs state no amount at all. 83 state one in a currency other than US dollars, and we do not convert, because applying an exchange rate to a figure that may be years old invents a precision we do not have.

Our own amount field conflates two things

9 records hold the size of an entire fund rather than a per-startup award: a national risk-sharing facility of $200,000,000 is not a credit anyone receives. Those are excluded from the statistics above, and it is a defect in our data we are fixing rather than a feature of the market.

Eligibility rules are largely not captured yet

This is the biggest gap, and it is the reason this report says nothing about which countries are shut out of which programmes. In our current data almost every credit program records no country restriction and none records a funding cap. That is not a finding about the market, it is a measurement of what our catalog has extracted so far. Verifying those rules against provider pages is ongoing, and when it is done this report will be able to answer the question we actually wanted to ask.

See your own numbers

Five questions, checked against the rules we do hold.

Check eligibility

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We will send the updated report when the eligibility data is complete, which is when the interesting questions become answerable.

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Generated from 2,451 active, approved programs. Press and researchers: the dataset is free to use with attribution to StartupFlow AI.