SaaS Capital Blog

Average Deal Size for Private SaaS Companies - 2026

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What is the Average Deal Size for Private SaaS Companies?

While there’s no shortage of data on public SaaS companies thanks to their reporting requirements, those benchmarks often don’t reflect the reality of smaller, private businesses. Public company metrics tend to skew higher due to scale, funding, and market position. To provide more relevant benchmarks, SaaS Capital conducts an annual survey focused solely on private, B2B SaaS companies. Our 15th annual report includes data from over 1,000 respondents and highlights key trends in Annual Contract Value (ACV) across company sizes, retention levels, and funding types.

2026 Revenue Per Employee Benchmarks for Private SaaS Companies

A common metric by which SaaS companies track their performance is annual recurring revenue (ARR) per employee. This 2026 update explores the median ARR per employee broken down by company size and funding type, equity-backed or bootstrapped.

Trust is the True Moat: What AI is Actually Changing for SaaS

We don’t agree with the simplistic formulation of “AI is eating SaaS,” and we’ve consistently written about that. SaaS companies get paid to solve business problems, and SaaS companies – or at least the healthiest and most adaptable among them – will incorporate AI into their solutions and continue to get paid to solve business problems.

2026 Spending Benchmarks for Private B2B SaaS Companies

How much do SaaS companies spend on customer support or marketing? In day-to-day SaaS company operations, questions like the above are common. So, we asked survey takers what percentage of revenue is spent on cost of goods sold, customer support/success, selling costs, marketing costs, research & development, and general and administrative. This post summarizes the benchmarking data.

2026 Benchmarking Metrics for Bootstrapped SaaS Companies

SaaS Capital works with both VC-backed and bootstrapped SaaS companies and has seen the advantages of both approaches. However, one situation that is especially interesting is bootstrapped SaaS companies with $3M to $20M in Annualized Run Rate Revenue (ARR). Growing from a pre-revenue startup to $3M (scale-up phase) marks a significant shift and bootstrapping from $3M to $20M creates enormous value for owners. To help support bootstrappers during their scale-up, we wanted to focus on 2026benchmarking metrics for scale-up stage bootstrapped SaaS companies.

Four early 2026 SaaS trends

SaaS valuations hit decade-plus lows in Q1 as markets priced in AI as an existential threat. But the picture is more nuanced than the headline numbers suggest. Here are four metrics that cut through the noise.

An Evolution in SaaS: The Median Company has a Positive Operating Margin

For the first time in over a decade, the median publicly traded SaaS company is generating a positive operating margin. This post examines the trend, its relationship to interest rates, and what it means for evaluating which companies are best positioned to navigate an uncertain environment.

My AI is Lazy: AI Created Fake Backlinks to Our Site. Here's What SaaS Marketers Need to Know

AI content tools are churning out huge amounts of content daily. Much of that content includes citations, references, and backlinks. And some of those links are hallucinations: plausible-looking URLs that lead nowhere. For SaaS companies, this creates several problems.

Introducing the SaaS Capital AI Assessment Framework

The wide availability beginning in 2025 of Artificial Intelligence (AI), and in particular the generative models known as LLMs, has given rise to fevered speculation as to the potential effects on the Business-to-Business Software-as-a-Service (B2B SaaS) market. SaaS is not dying, and a single generalist AI model will not replace all software. However, like other transformational advances in software, there will be a reshuffling of the deck.

Untangling AI: A 7-Category Schema for B2B SaaS

AI is not one thing. It's at least seven different conversations happening at the same time, and treating them as one makes every discussion less productive. Breaking "AI" into distinct categories, each with different stakeholders, timelines, and risk profiles can help focus AI conversations.