How Startups Actually Get Valued

Startup valuation is one of those topics where everyone has a take and almost nobody has the full picture. People see a headline number on TechCrunch and form an opinion about whether the market is hot or cold, rational or insane. Having spent years analyzing thousands of venture deals at Aumni, I can tell you that the headline number is the least interesting part. What actually drives startup valuations is a combination of investor incentives, company fundamentals, market dynamics, and compositional effects that make the data trickier to interpret than it looks at first glance.

Five Factors That Drive Startup Valuations

These five things consistently moved the needle on how investors priced companies. They apply whether you’re looking at a $5M seed round or a $200M Series D.

1. Dilution and Ownership Targets

This one surprises people outside of venture, but investors often work backward from an ownership target rather than forward from a valuation. A Series A investor might target 20% ownership. If they’re writing a $10M check, that implies a $50M post-money valuation. The valuation is a consequence of the ownership math, not the other way around.

At the early stage, this is particularly pronounced. Pre-revenue companies don’t have financial metrics that lend themselves to traditional valuation methods, so the negotiation is really about how much of the company the investor gets for their capital. Founders want to minimize dilution. Investors want meaningful ownership. The valuation is where those two interests meet.

Typical early-stage dilution hovers in a fairly consistent range over time, even as headline valuations move around. When valuations go up, round sizes go up proportionally. The ownership percentages stay remarkably stable. That tells you that ownership targets, not absolute valuation levels, are the primary driver of early-stage pricing.

This has practical implications for founders. If you understand that your Series A investor is anchored on owning roughly 20% of your company, then the negotiation is really about the size of the round. Raise more money, and the implied valuation goes up. Raise less, and it goes down. The ownership percentage is the relatively fixed variable. Everything else flexes around it.

2. Downside Protection and Deal Terms

Valuation is just one number in a term sheet, and sophisticated investors know that the terms surrounding the valuation can matter as much as the number itself. A $100M valuation with a 1x non-participating liquidation preference is a very different deal than a $100M valuation with a 2x participating liquidation preference.

Investors think about downside protection extensively, especially in uncertain markets. When valuations fell in 2022-2023, structural terms got more investor-friendly. Liquidation preference multiples crept up. Participation rights, while still uncommon overall, appeared more often than they had during the boom years. Anti-dilution provisions tightened. These were all ways for investors to manage risk at a given valuation level.

From a founder’s perspective, a higher valuation with worse terms can actually be a worse deal than a lower valuation with clean terms. Companies that raise at peak valuations with aggressive term structures can find themselves in difficult positions when the next round comes at a lower price. The anti-dilution provisions kick in, the preference stack grows, and common shareholders end up with a much smaller slice than the headline valuation suggested.

I’ll put it bluntly: some deals have eye-popping valuations where the term structure means that in any exit scenario short of a massive outcome, common shareholders get almost nothing. The founders probably celebrated on announcement day. The math told a different story. Walk through the waterfall analysis on every term sheet.

3. Traction and Fundamentals

As companies mature past the seed stage, actual business performance becomes the primary valuation driver. Revenue, revenue growth rate, gross margins, customer retention, and unit economics all factor in. Investors at the Series B stage and beyond are typically benchmarking companies against comparable businesses and applying multiples based on financial metrics.

The shift from “potential” to “performance” as the basis for valuation creates interesting dynamics. A seed-stage company with a great team and a big market might get a similar valuation to a Series A company with modest but proven revenue. By Series B, the company with strong metrics will outperform the one that’s still mostly a story. In the deal data, this showed clearly in valuation distributions by stage: early-stage distributions were relatively tight, but by Series C and D, the distributions widened dramatically. Performance becomes the great separator.

There’s a related point worth making about revenue quality. A company doing $10M ARR with 90% gross margins, 130% net dollar retention, and low churn is going to get a very different multiple than a company doing $10M ARR with 50% gross margins and high customer turnover. Two companies with identical top-line numbers can have valuations that differ by 3x or more based on the underlying unit economics. If you’re a founder thinking about how to maximize your next valuation, improving your retention and margin profile is probably more effective than just growing the top line faster.

Growth also matters a ton here, two companies with the same ARR and different growth rates may look very similar LTM but wildly different NTM.

4. Market Size and Competitive Position

Investors pay for market size, and they pay more when a company has a defensible position within a large market. A company in a $50 billion addressable market with a clear path to capturing meaningful share will command a premium over a company in a $500 million market, even if the smaller-market company has better near-term metrics. Venture returns are driven by outlier outcomes, and outlier outcomes require large markets.

Competitive dynamics matter too. A company with clear differentiation, whether through technology, network effects, or regulatory advantages, gets a premium over one competing primarily on execution in a crowded space. Sectors with fewer funded competitors tend to show higher median valuations relative to company maturity. That makes intuitive sense: less competition for a market opportunity means investors are willing to pay more per dollar of revenue.

There’s an interesting feedback loop here. When investors start funding a lot of companies in the same space, they collectively increase competition, which makes each individual company’s position less defensible, which should theoretically decrease valuations. But in practice, the opposite often happens during hype cycles: more companies get funded, generating more headlines, attracting more investor interest, pushing valuations up. The correction comes later, when the competitive dynamics play out and several of those companies fail to gain traction.

5. Stage-Specific Valuation Approaches

The methods investors use to arrive at a valuation change significantly across stages.

Pre-seed and seed: Valuation is mostly driven by ownership targets, comparable recent deals in the same geography and sector, and the qualitative strength of the team and idea. Financial models at this stage are aspirational at best.

Series A: A mix of ownership targets and early performance data. Investors start looking at metrics like ARR, growth rate, and customer acquisition cost, but the weight given to these varies. The team and market narrative still matter a lot.

Series B and C: Financial metrics dominate. Revenue multiples (typically EV/Revenue or EV/ARR) benchmarked against comparable public companies or recent comparable private transactions are the primary framework. Growth rate is the biggest modifier, with faster-growing companies commanding higher multiples.

Series D and later: Valuation approaches converge with traditional finance methods. DCF models, comparable company analysis, and precedent transactions all get used. At this stage, the company’s financials are mature enough to support rigorous quantitative analysis, and the valuation conversation resembles what you’d see in growth equity or late-stage private equity.

Later-stage valuations have a much tighter relationship with financial metrics than early-stage valuations. The factors that drive a seed valuation and the factors that drive a Series D valuation are different enough that treating “startup valuation” as a single thing misses the point. It’s more like a spectrum, with the early stages furthest from traditional methods and the later stages converging toward them.

What Series A Data Actually Tells You

Series A valuation data is some of the most commonly cited and most commonly misinterpreted data in venture capital. People see the median go up and conclude the market is getting hotter. They see it go down and declare a correction. Both reactions miss what’s actually going on, because the pool of companies raising a Series A is constantly changing, and that compositional shift matters at least as much as the price movement itself.

The Denominator Problem

The most important analytical framework for interpreting valuation data is what I call the denominator problem. You can’t look at valuation trends in isolation. You have to understand who is actually raising.

When deal volume drops, the companies that do raise tend to be the strongest. Companies with mediocre traction or unclear product-market fit wait on the sidelines or don’t survive to try. The result is that the pool of companies in the data shifts toward higher quality. That compositional change alone would push valuations up even if investor sentiment hadn’t changed at all.

This is true in any market cycle. During corrections, deal volume falls and the survivors are disproportionately strong. The median valuation may stay flat or even rise, not because the market is healthy, but because the weaker companies simply aren’t in the dataset anymore. During booms, the opposite happens: deal volume expands, more marginal companies raise, and median valuations can appear to stagnate even as the best companies command record prices.

If you’ve spent time in statistics or epidemiology, you’ll recognize this as a selection effect. The sample isn’t random. It’s heavily filtered by market conditions, and that filter changes over time. Ignoring this and treating the resulting averages as if they represent a stable population is a classic analytical mistake.

The lesson is simple but easy to forget: when you see a valuation number, always ask what’s the denominator. Who is actually in this dataset? How has that pool changed over time?

Looking at the Full Distribution

Medians are useful summaries, but they compress a lot of information into a single number. To really understand what’s happening, you need the full distribution.

When deal volume is declining, available capital concentrates on the highest quality opportunities. Investors who might have been writing five checks are now writing two or three, and those checks go to the companies everyone wants in on. That dynamic pushes the top end of the distribution up faster than the middle or bottom.

The spread between quartiles is often more informative than any single summary statistic. A widening spread tells you the market is differentiating more sharply between companies, which usually happens during tighter funding environments when investors are being more selective. A narrowing spread suggests more uniform pricing, which tends to happen during booms when money is flowing freely and even mediocre companies can raise at decent terms. The median might look similar across two periods while the entire shape of the distribution has shifted underneath it.

Three Forces Pushing Series A Valuations Higher

Three substantive factors drive Series A valuations higher in tighter markets, and the first two tend to persist across cycles.

General price level increases. Inflation plays a role in nominal valuation increases across the board. This is easy to overlook but it matters. A dollar of revenue today is not the same as a dollar of revenue five years ago in real terms, and valuations partially reflect that.

Stage migration. This is the factor I think is most underappreciated. More capital flowing into pre-seed and seed means companies arrive at the Series A stage with more revenue, more users, and more validation than they would have had in earlier years. If a company is further along when it raises its Series A, it should command a higher valuation. That’s not inflation or froth. That’s the rational response to more mature businesses raising at that label.

The concept of “Series A” is not fixed. It’s a label that describes a stage of funding, and the typical company at that stage has evolved significantly over time. Any comparison of Series A valuations across periods has to account for the fact that the companies carrying that label are not the same.

Capital concentration in a shrinking pool. When fewer companies raise, the available capital per company goes up for the ones that do. Venture funds still deploying committed capital will concentrate it into fewer, stronger companies, pushing those valuations higher mechanically, especially at the top of the distribution.

Understanding these three forces matters because they have different implications. If valuations are up because of inflation and stage migration, that’s largely structural and persistent. If they’re up because of capital concentration, that’s more cyclical and could reverse when deal volume picks back up. Figuring out which factor is doing the most work at any given moment is what separates useful analysis from narrative-driven commentary.

Putting It All Together

Startup valuation is context-dependent, and the context has more dimensions than most people account for.

When you’re looking at any individual deal, think about the five factors: ownership targets and dilution math, the full term structure (not just the price), the company’s actual traction and fundamentals, the market opportunity and competitive position, and the stage-appropriate valuation methodology being applied. These interact with each other. A company in a huge market with mediocre metrics might get a higher valuation than one in a small market with great numbers. An investor might accept a higher price if the terms give them better downside protection. The valuation number is just one output of a multi-variable negotiation.

When you’re looking at market-level data, add three questions to the mix:

1. What’s the denominator? How has the pool of companies raising changed? Selection effects can explain a lot of what looks like price movement.

2. What does the distribution look like? Is the median being pulled up by the top end, or is the whole distribution shifting? These tell very different stories about market health.

3. What does “Series A” mean right now? If the typical company at that stage has more traction and more revenue than it did five years ago, then higher valuations aren’t a sign of excess. They’re a sign that the label describes a different thing.

These questions aren’t specific to any one time period. They’re the right questions to ask whenever someone puts a valuation chart in front of you and claims to know what it means. The framework is what lets you interpret the numbers correctly no matter what the market is doing.

Further Reading

• Carta State of Private Markets (Q3 2025) - Transaction-level data on median pre-money valuations by stage from Carta’s cap table platform.
• Aswath Damodaran, “Valuing Young, Start-Up and Growth Companies” - A widely cited academic framework for valuing companies with no revenue, short histories, and high uncertainty, adapting traditional valuation methods for startup contexts.
• Kroll Cost of Capital Resources - Industry-standard equity risk premium and discount rate data used by valuation practitioners.
• Cooley Quarterly Venture Financing Reports (Q4 2025) - Deal-level valuation data from the most active venture law firm, covering median pre-money valuations by stage.

Some of the data and analysis in this post originally appeared on the Aumni blog and here.