Fundraising Milestones: Crossing the Seed to Series A Chasm in Application Software

At Work-Bench, we are laser focused on investing in the top enterprise software startups at the earliest stage and working with them to grow into the category defining companies of tomorrow.

One of the most common questions we hear from early-stage application-layer startups is — what’s the magic number a startup needs to achieve to successfully raise a Series A?

For much of the past decade, the unofficial number to be ‘Series A eligible’ was $1M ARR. The idea was that if you’re operating in an interesting market, raised a Seed round, and achieved $1M ARR within a reasonable time frame (~2-3 years), Series A investors will take a serious look at your company.

However, the fundraising market ebbs and flows. The past five years are particularly interesting as we experienced 3 distinct market swings:

1. The COVID-19 pandemic fueled the Zero Interest Rate Phenomenon (ZIRP) where startup formations and valuations skyrocketed.

2. The ZIRP hangover where valuations reverted to pre-pandemic norms.

3. The AI hypecycle we’re in today where goalposts have shifted in terms of what’s required to be ‘investible’.

What it Takes to Raise a Series A

To help founders better understand the metrics expected ahead of a Series A fundraise, I surveyed 30 investors from leading Seed, Series A, and multi-stage firms to get their take on the key benchmarks for a successful raise.

*Note: While the survey didn’t ask for specific startup case studies, it measured ‘standard’ vs. ‘exceptional’ fundraises based on factors like time to raise, number of term sheets, round size, and valuation.

*Caveat: The path from Seed to Series A has become more nuanced and competitive than ever before. Fundraising is never black and white, and every company’s fundraise is unique. Different investors have different metric thresholds for what they consider ‘Series A ready’.

While there will always be outliers to the rules, here’s what I found:

• ARR expectations for a STANDARD Series A: 68% of investors indicated that ARR should fall between $1M - $1.5M ARR

• ARR expectations for an EXCEPTIONAL Series A: 85% of investors favored ARR landing between $2M - $3M ARR

• YoY Growth expectations for a STANDARD Series A: 54% of investors selected 3x YoY growth

• Growth expectations for an EXCEPTIONAL Series A: Exceptional growth is harder to define and was quite barbelled in responses — 31% of investors said 4x with 21% of investors selecting 10x growth. It’s important to note that deal velocity and gross revenue growth is an important here — growing from $100K to $400K is very different than growing $1M to $4M over the same time duration.

Shifting Tides In The Age of AI

AI has shifted the benchmarks for evaluating Series As, demanding stronger traction, accelerated growth, and sharper differentiation. However, while AI startup valuations have soared, there’s heightened scrutiny on customer commitments and retention.

Keeping this all in mind, here are what some investors are now prioritizing:

• Margin Profiles. Even though AI companies are growing quickly, some investors question the margin profiles for AI native businesses. Some investors are more flexible on lower short-term gross margins with a widely held expectation that inference costs will continue to decrease and drive margin expansion over time.

• Revenue Durability. Many AI native companies quickly grew revenue building for niche wedge use cases with the goal of finding stickier, more valuable workflows over time. Even though many companies have not yet found durable workflows, many investors have bet ahead of “high value” use cases that could command larger ACVs.

• Customer Concentration. Given how quickly AI companies are shipping new features, investors are open to underwriting AI businesses with more customer concentration risk relative to traditional workflow SaaS. Their hope is that AI businesses will add workflows and become stickier with additional users.

• Retention, Retention, Retention. Many Series A investors are concerned with churn in AI businesses and their first filter in assessing an AI startup is often top-line growth and customer retention. For founders, having 1-2 years of renewal data could be helpful to prove that usage is sticky and not just experimental.

• AI Narrative. A strong "why now" narrative, clear differentiation from incumbents, and signs of demand are becoming more essential as budgets shift toward AI-driven software. This means proving out clear evidence of AI’s tangible impact—whether through enhanced product usage, innovative roadmaps, or the ability to attract top AI talent.

My biggest advice to AI startup founders – find a balance between meeting aggressive growth expectations and building durable workflows that retain customers.

Other KPIs Founders Should Be Tracking

In the past year or two, several KPIs have gained increasing importance for founders to track:

• Retention metrics, including net (NRR) and gross (GRR) as well as durability of revenue (e.g., contract length), and live ARR/CARR.

• Burn multiple with a target of <2.5x, ideally <2x, alongside gross margin due to AI-related costs and human-in-the-loop models.

• Usage and engagement metrics to assess customer quality.

• Growth efficiency, including sales and marketing spend relative to ARR, and sales cycle metrics like POC conversion rate.

The bar for raising a Series A is only getting harder — these expectations exemplify how Series A investors are looking for fast growing, efficient companies that can retain customers at scale. As one survey respondent put it:

“Strong growth and fundamental metrics are the basics alone and are not enough to move the needle. We need to see a strong story around long-term defensibility for why this company and category should exist.”

At Work-Bench, we remain committed to helping the next generation of enterprise software founders navigate the startup journey, equipping them with the insights, resources, and support needed to not only meet, but exceed these benchmarks.

If you’re building in AI/ML/Data, Infrastructure, Security, or Enterprise Applications, feel free to reach out.