Startup Failure Rate Data 2026: By Stage, Industry, and MVP Approach

The 90% Statistic Is Misleading

You have heard that 90% of startups fail. The number is defensible at a macro level across a 10 year horizon. But it tells founders almost nothing actionable, because it collapses wildly different risk profiles into a single figure.

A bootstrapped B2B SaaS company with 15 paying customers at launch faces fundamentally different odds than a consumer app burning $50,000 per month on user acquisition before finding product market fit. Treating those as the same population produces meaningless averages.

The more useful question is: what does the failure rate look like for a company with your profile, at your stage, in your category, using your development approach? That is what this post tries to answer.

Data sources used throughout: CB Insights post mortem database (437 startups, 2022 to 2025), Crunchbase funding outcome tracking (cohort of 14,200 seed stage companies, 2018 to 2024), BLS Business Employment Dynamics data (US employer firms), First Round Capital State of Startups surveys (2023 to 2025), and Startup Genome Global Startup Ecosystem Report 2025.

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Table 1: Startup Failure Rates by Funding Stage

Stage	Sample Size	Failed Within 2 Years	Failed Within 5 Years	Reached Next Stage
Pre seed (bootstrapped)	8,400	48%	72%	19% raised seed
Pre seed (angel funded)	3,200	41%	68%	28% raised seed
Seed stage	5,600	35%	63%	40% reached Series A
Series A	2,100	22%	48%	58% reached Series B
Series B	890	14%	38%	64% reached Series C
Series C+	410	9%	29%	71% exited or sustained

Data source: Crunchbase cohort analysis (companies founded 2018 to 2021, tracked through 2025), CB Insights venture outcomes database.

The seed to Series A conversion rate of 40% is the most practically important number here. It means that for every 10 startups that raise a seed round, only 4 will raise a Series A. The other 6 will either shut down, go into zombie mode (running but not growing), or be acqui hired for minimal returns.

What separates the 4 from the 6? Post mortem analysis points consistently to two factors: validated product market fit before scaling spend, and unit economics that work at small scale.

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Table 2: Startup Failure Rates by Industry Vertical (5 Year)

Industry	5 Year Failure Rate	Median Runway at Failure	Top Failure Reason
Restaurant / food service	82%	11 months	No demand / wrong location
Consumer hardware	78%	18 months	Manufacturing costs / supply chain
Consumer mobile apps	74%	14 months	No retention / monetization
Healthcare tech (B2C)	71%	22 months	Regulatory complexity
EdTech	68%	20 months	Low willingness to pay
Marketplace (two sided)	67%	19 months	Chicken and egg problem
Fintech (B2C)	65%	24 months	Regulatory and trust barriers
B2B SaaS	63%	26 months	Wrong ICP / no enterprise sales motion
Developer tools	61%	28 months	Monetization too late
AI / ML tools (B2B)	58%	24 months	Competition from foundation model providers
Cybersecurity (B2B)	54%	30 months	Long sales cycles / budget constraints
Healthcare tech (B2B)	52%	32 months	Long procurement cycles

Data source: CB Insights sector analysis (2025), Startup Genome Industry Failure Rate Report (2025), Crunchbase vertical outcome tracking.

B2B models consistently outsurvive B2C models, with the exception of consumer hardware which compresses the failure timeline further due to inventory risk. The AI and ML tools category is the notable new entrant: its 58% failure rate is below average but its failure reason is new. The threat of foundation model providers expanding upmarket is a structural risk unique to this wave of startups.

The developer tools category has a healthy survival rate but a problematic failure reason: monetization too late. Many developer tool founders delay charging because they are afraid of killing growth. The ones who survive figure out how to charge without destroying developer goodwill early.

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Table 3: Failure Rates Correlated with MVP Approach

MVP Development Approach	1 Year Failure Rate	2 Year Failure Rate	Median Monthly Revenue at Month 12	% Raising Follow On Funding
Validated before building (10+ user interviews)	22%	32%	$5,100	34%
Core value focused (8 or fewer launch features)	26%	39%	$4,200	28%
Co founder team with technical depth	29%	42%	$3,800	31%
No code MVP with fast iteration	31%	46%	$2,900	19%
Feature complete launch (15 to 25 features)	39%	56%	$2,800	22%
Agency built without founder involvement	44%	62%	$2,200	17%
Over built launch (25+ features)	52%	69%	$1,900	14%
Built without any user validation	58%	74%	$1,600	11%

Data source: HouseofMVPs cohort data (n=147 companies, 2023 to 2025), First Round Capital "What We Got Right and Wrong" (2024), Indie Hackers product outcome tracking.

The distance between the best and worst approach is staggering. Founders who validated before building had a 32% two year failure rate. Founders who built without validation had a 74% two year failure rate. That is not a small optimization. It is a 2.3x difference in survival, driven almost entirely by the decision to talk to users before coding. For a structured framework on when to build a POC versus a full MVP, see the POC vs MVP decision guide.

The "agency built without founder involvement" row is worth discussing. These projects fail not because agencies do bad work, but because the founder has no deep product intuition when it is time to iterate. They do not know why things were built the way they were. They cannot make fast decisions without going back to the agency. By the time they get answers, momentum is gone.

For a practical guide to avoiding the over building trap, see how to scope an MVP.

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Survival Curves: What the 5 Year Picture Looks Like

The aggregate survival data for US employer startups (BLS Business Employment Dynamics, tracked through 2025) gives us a clean baseline:

Year	Survival Rate (All Industries)	Survival Rate (Tech/SaaS)	Survival Rate (Validated MVP Cohort)
Year 1	79%	82%	88%
Year 2	67%	71%	78%
Year 3	55%	61%	67%
Year 4	47%	54%	59%
Year 5	40%	48%	52%

Data source: BLS Business Employment Dynamics (2025), HouseofMVPs cohort tracking.

The tech and SaaS survival curve runs approximately 8 percentage points better than the all industry average at each checkpoint. The validated MVP cohort runs another 8 to 10 points better than the tech average.

Year 3 is the most dangerous year for SaaS companies. This is the point where early adopter revenue plateaus, the founding team has been executing for long enough to be exhausted, and scaling from a small base requires genuine go to market investment. Companies that reach year 3 with strong unit economics and a repeatable sales motion usually survive. Companies that reach year 3 still looking for product market fit rarely do.

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The Real Failure Timeline

Most startup post mortems describe a failure moment: the day the team decided to shut down. But the data shows the actual failure usually happened much earlier and much more quietly.

CB Insights analyzed 437 startup failures and found that in 71% of cases, the fundamental failure condition existed within the first 6 months. The company continued operating for an average of 14 more months after the failure condition was established. During that time, founders raised more money, hired more people, and built more features, all on top of a broken foundation.

The most common pattern: a founder builds a product without validation, launches to minimal uptake, interprets the lack of traction as a marketing problem, spends money on ads and growth, fails to improve retention, and eventually runs out of runway. The product market fit problem was there at month 2. The shutdown happened at month 20.

This is why the validation data is so striking. It does not just improve the odds. It compresses the feedback loop. Founders who validate early learn faster whether they have a real problem to solve. The ones who do not validate keep building on a hypothesis that was wrong from day one.

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Failure Reasons: Detailed Breakdown

The CB Insights post mortem database has been expanded to 437 startups as of the 2025 update. Here is the updated breakdown:

Failure Reason	% Citing (2025)	% Citing (2022)	Trend
No market demand	42%	42%	Flat
Ran out of cash	29%	29%	Flat
Wrong team	23%	23%	Flat
Got outcompeted	19%	19%	Flat
Pricing / monetization issues	18%	18%	Flat
Product not user friendly	17%	13%	Up
No business model	14%	17%	Down
Poor marketing / no distribution	14%	14%	Flat
Ignored customer feedback	13%	8%	Up significantly
Product timing	10%	10%	Flat
Legal / regulatory issues	8%	7%	Slight up
Team burnout / pivot fatigue	7%	4%	Up

Data source: CB Insights "The Top Reasons Startups Fail" post mortem analysis (2025 edition, n=437).

The rise of "ignored customer feedback" from 8% to 13% is the most significant shift. It is consistent with a broader pattern: AI coding tools have made it faster to build, and some founders are shipping more features in less time without doing more listening. Speed without feedback loops is just faster failure.

"No business model" declining from 17% to 14% suggests that founders are now more intentional about monetization from the start, likely influenced by the recurring revenue SaaS model spreading beyond traditional software companies.

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What Separates Survivors from Failures

Across all the datasets reviewed, three behavioral patterns consistently separate startups that survive from those that do not:

Pattern 1: Early paying customers. Startups that had 5 or more paying customers within 60 days of launch had a 67% two year survival rate. Startups that had zero paying customers at 60 days had a 41% two year survival rate. Revenue is the only real validation. Signups, waitlist entries, and letters of intent are noise.

Pattern 2: Narrow initial ICP. Founders who could name their first 10 target customers by company name or individual name before building had substantially higher PMF rates. Broad targeting ("anyone who does X") is a failure signal. Narrow targeting ("the solo founder running a bootstrapped SaaS under $10k MRR who is manually tracking churn in a spreadsheet") produces products that actually get used.

Pattern 3: Iteration speed after launch. Survivors shipped product updates at a median cadence of every 8 days in the first 6 months post launch. Failed companies shipped updates every 23 days. The survivors were not just building faster. They were in a tighter feedback loop with users.

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What This Means If You Are About to Build

The data is pointing at the same things it has pointed at for a decade, just more precisely:

1. Validate the problem before writing code. 10 interviews minimum. This alone shifts your survival odds by more than any technical decision you will make.
2. Launch with fewer features than you think you need. Every feature beyond the core value prop adds scope, delays launch, and reduces the feedback signal.
3. Charge immediately. Free users give you engagement data. Paying users give you survival data.
4. Treat month 3 as the most important milestone, not launch day. Launch day tells you if people will try it. Month 3 tells you if the problem was real.

See how to validate a startup idea for a practical process to work through before you scope your build.

If you are ready to build with these principles applied, HouseofMVPs works from a fixed scope process designed around these benchmarks. You can also use the MVP Cost Calculator to ground your timeline and budget in real data before you start.