High Alpha SaaS Benchmarks 2025 Report

Live from Schenectady, New York, it's the Metrics Brothers, Growth and CAC.

And I'm Growth, better known as Ray Wright, founder and CEO of Benchmarket.

And I'm CAC, better known as Dave Kellogg, EIR at Balderton Capital, independent consultant, and the author of Kellogg.

And together we are the Metrics Brothers.

And we go together like alpha and beta.

Like wolf pack kind of stuff? Like I'm the alpha dog here?

No, no, Ray. I know you'd like that. Another kind of alpha, the one that comes from the capital asset pricing model.

Ah, like where beta means how much an asset's return moves relative to the overall market.

Yes. And where alpha is excess, positive or negative returns beyond that.

Ah, so this explains the name of that website, Seeking Alpha, that's always trying to get me to sign up for a premium subscription.

That's right. Well, they weren't seeking the alpha dog. They were seeking basically incremental or excess return. And there's all kinds of references to the word alpha. Today's reference is from a VC firm called High Alpha, and they have recently published a new report full of SaaS benchmarks.

Benchmarks. I love benchmarks, especially when Kyle Poyer's name is associated with them. But before we dive into it, let's hear a word from our sponsor.

The Metrics Brothers is proudly sponsored by Benchmarkit, the B2B AI and software industry's largest, broadest, and most contextualized benchmarking index. Dave Kellogg and Ray Groth Wright have put the Metrics Brothers podcast on hiatus for the remainder of 2026. All episodes we have ever recorded are still available on your favorite podcasting app. You can reach Dave at Kellogg and Ray on LinkedIn at Ray Reich. Thank you so much for listening to the Metrics Brothers podcast.

Okay, so today we're looking at the SaaS Benchmark Report 2025 by High Alpha. It is a— well, first, High Alpha is a B2B SaaS venture firm founded in 2015 in Indianapolis, Indiana by Scott Dorsey and friends after the sale of ExactTarget to Salesforce for $2.5 billion in June 2013. So kind of a classic tale. An entrepreneur creates a company, sells it, has a great exit, and kind of wants to pay it forward by starting his own VC firm, taking presumably some of his best people with them. and then helping kind of back and advise other startups. Now, I would say the story gets unusual. That's a pretty normal story. It gets unusual in the way that they do that, which is they quote unquote create and found SaaS companies, which is a little bit weird. The classical view on Sand Hill Road is that VCs were more like talent scouts, right? Let the entrepreneurs walk by and we'd pick which ones we wanted to invest in. Not that we would, and in general, this had a bad reputation back in the day, that we would sit there and say, ah, Ray and I think we should make a company. Let's go create a company to do this and call central casting, call Heidrick or Diversa and say, get me a CEO, get me a nerd, a blonde, and 2 whatevers, 2 jocks, and call central casting to create the team. That model was generally not liked, but nevertheless, these guys have tried it. So they both do classical investing, I believe, in SaaS companies, but they also kind of create And found them. Most recent funds, 2024, $425 million. So these are not huge funds. And they got about, about 20 people on the About Us page. Ray, thoughts on High Alpha?

No, I think you did a pretty good job covering them. And I'd love to drill down into their benchmark product.

Okay. Do you want to do the report overview or you want me to?

No, why don't you go ahead and take it and I'll just apply it.

Okay. Ray's gonna ride shotgun today. This is their 9th annual report. I don't know how I missed that, Ray, because this is, I think, the first one that I've seen. It's 64 pages. It's a survey with 800 respondents. 70% are in the US, 17% in Europe. So, let's just call it 80/20 US/other. 50% founder CEO. That's pretty amazing, actually, for a survey response that half the respondents are founder CEOs. I think some of that speaks to the size of company involved. And 22% were the finance head. Here we are. But it was 68% of those in the sample were between $1 and $50 million in ARR, right? So, we're not going to get a lot of good analytics in the $50 to $100 or the $100 to $200 or the $200 to $500 ranges, right? This is an earlier-stage company report. 72% of these firms target enterprise or mid-market customers. Look, overall, I think this is a professional report. They have nice definitions, which we like. They're using kind of box and whiskers style presentation of numbers, which we like. It's a worthy read. It's a big enough sample to have meaningful segment coverage. And there's one or two pet peeves I have, but why don't we hear from you first?

Well, so first of all, on the 9 years, I think there may have been some creative liberties taken here because this is really an extension of what OpenView did for many years, which is while Kyle was involved. I've only found 2 years of High Alpha specific, Dave, and that was when Kyle first partnered with them last year after OpenView shuttered their doors. So I think it kind of gave this one a little bit of a halo effect, and I think that also was key to getting the 800 respondents, as well as there was about 12 other— I would call them mid-tier VCs— who also syndicated their survey.

Okay. Okay. Yeah, I knew there was some continuity with Kyle. I knew he'd worked on it. He did some LinkedIn posts on it, but I didn't Ctrl+F the document, but his name's not really on it, or if it is, it's on a footnote. I think it might be on a footnote towards the end. It's certainly not highlighting that he worked on this. So in any case, thanks for the observations. Slide 11. So for those in your handbook, we're gonna be on slide 11 of the PDF. I won't summarize it here, but it starts with a nice, what I would call information-dense metrics overview table. Which talks about kind of key metrics in the rows and then cut by segment size in the columns. Again, the smallest one is less than $1 million and the biggest is greater than $50 million. So, if I'm greater than $50 million, say I'm an $80 million company, I don't love the fact that I'm going to be grouped with everybody greater than $50 million, right? Because there could be some very large companies there, etc. So, I think this is really a $0 to $50 million report.

Yeah.

if I were consuming it. When I looked at these numbers, a couple of things jumped out. First, to me, first, what didn't change? Software gross margin, virtually the same across segments. It varies from 74 to 80, right? 6-point range across the entire size range. I thought that was kind of interesting. The P&L structure in general didn't change much with size range, right? In the earlier days, you'd see higher sales and marketing spend, Right. And higher R&D as a percent of revenue. And here, kind of across the board, sales and marketing is running around 30%, 25% to 30%. R&D is running around 30% to 40%. And it was just kind of interesting to see that. And the gross margin is running between 75% and 80%. So that's what didn't change. Some of the things I thought were suspect were the 5- and 8-month median CAC payback period. Below 5, you're like, whoa. Like, so this That's an incredibly fast CAC payback period. Like, I'd say the norm is between 12 and 18. Someone might even say between, you know, 14 and 20. But 5 months CAC payback. I think there's one other one that jumped out at me. Yeah, the Rule of 40 scores were also super high for very small companies, right? Getting a Rule of 40 score of 33. So, I don't know, maybe this is reflecting of the times. But Ray, what did you see on the metrics summary?

So 3 things jumped out at me. Number one, the sales and marketing spend. As you mentioned, it ranged from 28% for less than $1 million, and then it was 25% greater than $50 million. Seems really low compared to everything I see, Dave. Even in the latest quarter of public SaaS companies, the median sales and marketing spend is 32%. So Things must have changed dramatically in the last 6 months since I saw other benchmarks like this. So that really did jump out at me as incredibly low. The cap payback period, much more highly correlated to ACV than ARR. Like when you see these 5 to 8 month cap payback periods, I automatically think, oh, this must be like a, a $1K to $10K product. Versus a $25K to $100K product. So I would love to see this segmented by ACV also, Dave. And then the last thing that really jumped out at me was the employee count. I think you see the $20 million to $50 million, they said it was down 70 year over year. That's a 35% discount on reduction. And in the $50 million and above, it was down 42% I.e., the number of employees. So that just kind of concerned me regarding the selection of the population here, because that just seems down more than I've seen anywhere, Dave.

Yeah. Okay, good. I didn't even look at that row, to be honest, because it doesn't matter why. But okay, so thanks for those observations. Let's go cut into— look, I have a pet— we're gonna have a lot of pet peeves with this report.

I like the report. I think it's worth reading.

If I was there $150 million SaaS company, I'd read it. They do some really nice things, like they have good definitions, nice formatting, but they do some stuff and I just— there's 2 things in particular they do where I just want to like kick myself. One is a slide like this where there's only one new row on this slide. On slide 12, there's basically a sample of the prior slide's data with one extra row added, which is ARR per employee. So why not just put that in the prior slide? And stuff like this is just like—

Yeah.

Inexplicable. The other more serious pet peeve I have is they do a lot of kind of implied correlation equals causation. Like, the commentary is sloppy in that regard, um, and, and that's something that, that I keep a keen eye out for in these reports. So if I had to summarize my view on this report, read the data if you're in the sample range, but watch out for the commentary because I think they're out over their skis in terms of what they're concluding from this. And sometimes they're stating it like a super super confident way, like, this is proof. I remember that was the one that got me. I was like, God grant me the confidence of a young venture capitalist. You know, if you can conclude that this one table of numbers is proof of anything, uh, I would be surprised. So anyway, um, any higher-level kind of nitpicky comments, Ray? Because look, they caught us in a bad mood. We're both crusty today. So, uh, what, what makes you grumpy, Ray?

Well, I always found that when somebody tells me I'm a little cantankerous to find something positive to say, and I really like the ARR per employee, which is the last line here. It really shows the increased productivity that even early-stage companies are getting to. I mean, $200,000 of ARR per employee at $1 to $5 million range. I mean, that's up from what I saw 2 years ago almost 100%. So that's incredible. And up to $278,000 almost for those $50 million. And you know, you're seeing great public companies today in the $350,000 to $375,000 range. So that was very positive.

Now the thing— okay, we'll get to Grumpy Ray later in the episode. Are we gonna get Grumpy Ray now?

You're gonna get confused right now because, okay, most revenue retention— I still— it's one of my favorite metrics. Like when it shows me that a $1 to $5 million ARR company has 92% gross revenue retention, and even a less than $1 million company has 92%, my thought process automatically goes to how many renewal cycles have they seen here, Dave? Are they in their first 12 to 18 months of existence? And they just haven't had more than one renewal and not 2 or 3. And that often will artificially inflate gross revenue retention and/or they're not using the available-to-renew kind of concept.

Got it. Yeah, I think, well, first, the GRR should not be done on an available-to-renew basis, right? Just to be clear, I don't view GRR as an ATR metric. Do you?

Well, if— I guess the answer is yes, I do, but that's a whole nother episode.

Oh my. Okay, well, we've discovered a deep schism here on GRR calculations. So we'll do another episode on should GRR be done on an available-to-renew basis. But back to this, because we got 64 slides worth of material to hit. Shocking, Ray. Shocking. Um, slide 11, the IPO window is reopening per their data. This was kind of a narrative violation. I was quite interested to see it. Um, I guess I've been more focused on recent IPO financial performance, which hasn't been great. Um, but, but I guess without me noticing, like, we're doing a lot more IPOs than we used to. Now here's a pet peeve. This thing only goes back to 2022. So I think you can say that, and the text is consistent with that, but we're going to see in a couple slides they're going back much further. So they do a little bit of time period cherry picking, which I noticed and don't love. But just taking the slide as it is, since the end of 2021, we're up now to about $15 billion in IPO value and 65 IPO deals, which is a bigger number than I expected. Thoughts on that one, Ray?

No, I really did like this slide. It made me want to go back and count them all up, right? And I didn't go to that level of detail, but I actually forgot about some of these coming public, right? Like CoreWeave and StubHub in the last few months.

Yeah. And look, I'm not going to beat this to death, but here's— there are numerous examples throughout this report of where they get— they overreach given the data. And here's the commentary on this slide. While companies are staying private longer than ever due to massive amount of private capital available, right? Do you know that? Does the data prove that? Like, wait a minute. That's an assertion, folks. And it's not a bad one, right? But there's other reasons why people are staying private, right? Like they can't go public because the public investors don't want them, or that public markets have been fairly punishing to several of the recent IPOs, or they don't want the regulatory and compliance burden, or they don't want the cost, right? There's a bunch of different reasons why people are staying private longer. And just to kind of drop that in bugged me. Because, and they do this a lot in this report, so I'll probably beat it to death, but I'll try not to.

Yeah. One of the things, you know, when you do a private benchmark report, right, I'm always interested why they're interlacing public. And I know people say that private investment trends trail public trends a little bit, but if they're going to put public, what I'd love to see here is also what was the enterprise to revenue multiple trends? for these stocks when they went public, 3 months or 6 months later? Because to me, that's a really interesting story I'm seeing right now. Like Figma. Yeah.

And to be fair, to be fair to them, they're just talking about activity, right? So this is why the report's a little frustrating because it's just talking about activity. The data is about activity. I would view that, Ray, as a kind of invalid criticism, but for the commentary where they kind of overreach and start talking about why companies are staying private. Well, there's a lot of reasons why, and one of them is what you're talking about, the financial performance of recent IPOs. Next slide. Public SaaS companies on ARR per employee. This interesting chart, I'd not seen one like before. I was totally unaware that AppLovin has $3.3 million in ARR per employee, which blew my socks off. Number 20 on the list is RingCentral. And they're still at $583,000. So this chart, look, they have the median on there as well, which is nearly $400,000, which is high for a median for me, but good. I think that is going up because there's a big focus on efficiency. But I don't know, Ray, your thoughts on this one?

I actually really like this chart because it shows me some numbers behind kind of the public markets. I did know it was getting close to $400,000. But then I think about it, Dave, and this is once again another commentary. I look at RingCentral and off the top of my mind, I think its enterprise value to revenue multiple is like 2.3. It's really low and they have $583,000 of ARR per FTE. I was blown away by that. And then in my little mind went to, I wonder what the correlation of ARR per FTE is to enterprise value to revenue multiples. Which I'm going to go do.

Okay, you do that, Ray. Now I'm going to go back to my pet peeve theme. So in the commentary over here, they go, the top 20 companies generate over $580K per employee. Cool. Proof that efficiency isn't a post-reset constraint, but a competitive advantage. I have 2 problems with that sentence. One, I don't know what the hell it means, but even if I did, it's not proof of anything.

Right.

So this is where, to me, keep your eyes on the charts. They've got good data here. Some of the commentary, man, they're just getting out over their skis. So, and I may mention that one or two more times. Slide 21 talks about venture capital deal value returns to 2021 levels, i.e., returning to, in this case, means gets back up to 2021 levels in deal value. This chart, It's good to see. It shows venture activity over the past 7 years. And my only beef with this chart, Ray, is if we're talking about how venture is evolving here, I'm going back to 2018. And just a couple slides ago, I was going back to the end of 2021. And I think that within a section, within 5 slides of each other in a report, if we're talking about a trend, all the data should be from the same time period. Because otherwise it's misleading. And look, as someone who's naturally cynical, I think you've cherry-picked to support your narrative. I have no idea if that's true because they didn't show me the other data. But when, as a board member, whenever I see people playing with the time axes, I'm like, uh-oh, they're cherry-picking.

Yeah, I am. Hyalf actually did a webinar just a couple of days ago highlighting this report, Dave. And one of the questions I asked was on your VC deal activity and the total value. Did you analyze the breakout between SaaS companies that have AI, but it's just not core to their product? SaaS companies that AI is core to their product and AI-native software companies. And they said, yeah, we didn't put any charts in here, but it is true that the AI-native software companies, the amount raised is much higher. And that's what really drove this Deal count to money raised ratio.

Got it. Interesting. Um, now slide 19, surprising level of consistency in year-over-year growth. This is an interesting title for a slide that's like basically a clustered bar or segmented bar chart that shows 6, 5 years of history because they're only looking at the last 2 years in that commentary, right? The headline is about year-over-year growth And when I first looked at this chart, I got, as often with this report, cognitive dissonance, because I'm like, well, all the charts are going down, nothing looks consistent. But if you look within each cluster, the last 2 rows are actually quite consistent. So it took me a little bit to figure out what they meant. Not maybe the best presentation to text narrative matching. But I do think there's a really interesting point, as with all their data, that I kind of hope that 2025 would be a growth rebound year. And it wasn't. It was a growth stabilization. I kind of think we stopped the bleeding on growth. If you look at this chart, that's what you conclude. We stopped the bleeding. We're not slowing down anymore. We did not rebound at all, but we are improving efficiency. And maybe it's unreasonable to expect both, to say, hey, we can improve efficiency and rebound growth all at once. It looks like the data here is saying you could do one or the other. I don't want to— here I am now, I'm out over my skis. The data over here is saying we didn't do both. It's saying we did improve efficiency and we stopped the bleeding on growth.

Yeah. The only commentary I have on this, and it's the same one on the previous page, because they did focus on how they broke out the respondents of AI being core or not core, I would love to see the growth with AI as a core feature versus a complementary or it doesn't exist. Because I have this bias. That right now AI-native companies are taking advantage of experimentation budgets and they're growing so much faster, like the episode we did, which is the new metric, which is months to $100 million ARR.

Okay.

So, Ray, we're coming up on 20 minutes here. So, we're not going to be able to hit all the slides. I'm going to do one more of my favorite slides. You get to pick what one you want to do. So, I'll give you a minute to think about that while I cover slide 21. And then we'll do a wrap on this report. So, I'm now on slide 21, complete shift in the last 10 years to AI centricity. I thought this was a really good chart. I like this. They had 10 years' worth of data and they were doing AI as core to the products versus AI as a supporting feature. And you see basically a complete inversion or inverse correlation between when the company was founded and how true that statement is, how true the statement AI is core to the product is, i.e., 100% of the companies founded in 2025 said that, and 0% of the companies founded in 2016 said that. And it's a fairly linear relationship in between. So how core AI is to your product is very much a function of when you were founded. And then kind of cutting that transversely, they said today across all companies, 36% say AI is core to the product, 64% say AI is a supporting feature. Now, it's very hard to wave a wand and make AI core to your product when it isn't. Like, I see companies founded in 2015 that say— or Workday, it calls itself a native AI something. It's like, no. Or like built with AI at its core from the original day. And it's like, no, that's just not possible. So I think there's a lot of marketing overreach on this question. And I think this data is actually quite good. It reflects the current reality. And my general advice is build AI into your systems. and then probably acquire some core things because you want at least part of your product line to have been built where AI really was core. And the best way to do that is to buy a company that was founded roughly after 2022, if you look at this data. So now I will finish with a pet peeve before giving you the last slide before the wrap-up, Ray. But once again, God grant me the confidence of a young venture capitalist. The subtitle of this chart is ChatGPT's 2022 launch significantly changed how deeply companies incorporate AI in their products. And it's just, it's stated as a fact, right? And it's, that's overstated. It's probably true, but the data doesn't show that. And this is a benchmark report and they just kind of keep doing that. So put your skepticism meter on high when you read this thing, when you're reading the text, but I love the data. Back to you, Ray.

Okay. Now, ending on a high note, I'm just going to go one more slide because It addressed a couple of the things I said earlier in this episode. It shows the growth rate differentiation between companies with AI as core to the product and AI as a supporting feature. And I mean, I look at that kind of $1 to $5 million ARR, you know, I believe this is median. It doesn't really say, but I think the median is around 110% growth, AI core product companies. And it's only 40% for AI just being a support feature. And that held fairly true in the $5 to $20 million range also, was about 90% growth rate for AI as core and only about a 30% growth rate for AI as just an add-on. So I really like this slide and I like to see more of the charts actually using this as a segmentation criteria.

So I have to weigh in on this one and do my pet peeving again. Because again, love the data just like you do, but I wanna pick a bone with some of the words. AI differentiation drives 70% faster growth. You don't know if that's what's driving it, right? The only thing this data says is that companies who report that AI is core to their product are growing 70% faster in that segment than companies who didn't. You don't know if that is causal or not. It could be an underlying confound. Like maybe companies who report AI as core to their product also strongly believe in great user interface design and they have better user interfaces and that's why they're growing more quickly, right? It's not a bad speculation. I don't think they're wrong. Like, I don't actually mind their observations. I just think they, they're not carefully worded enough. And then they compounded on this one with the founder takeaway at the bottom. Embedding AI deeply in your product isn't just a technical choice. It's a growth strategy, right? And you cannot conclude that from this data. All you can say is that companies where they report that AI is core to their product grow a lot faster than companies that don't. And that is a valid observation. Back to you, Ray.

Yeah, and I know we don't have any more time, but I would say just look at slide 37. It shows how NRR and GRR is, I'll use the word correlated to growth rates. It's really a good chart and it shows you how important it appears that both GRR and NRR is to your overall growth rate.

Yeah, look, as long as we're in this particular rat hole, Here they get it right because they say NRR and GRR are strong growth predictors. And there's a difference between a predictor and a causer, right? Or a driver, right? It just means if I know one thing, it predicts the other. It doesn't say it causes it, right? And that's where, right, that's where they get messy here because I'm completely fine with this language. If you know a lot about NRR, you know a lot about overall growth.

Right?

That's what it says. Because one predicts the other. You cannot say that one causes the other. Now, logically, hey, high NRR, you're getting a lot of growth through your existing base. That should help you grow overall more quickly. So again, in this case, they're not overstating it, although I haven't highlighted the text, so maybe they are. But at least in the headline, they're actually calling this one correctly.

Yeah. So overall, I like the report. It has a lot of great— and we didn't talk about the people-based slides. Closer to the end. Good report, great data, great population. Just be aware that there might be some commentary that you should focus on less and focus more on the data and the charts.

Yeah, I would say, Ray, too, you're correct. And they do have some great kind of, for lack of a better term, soft stuff at the end, starting on page 53 called Founder Insights. And they do questions about like how many co-founders you have. They talk about growth correlating with stress, right? They have mental health indicators. They have some pretty interesting stuff. They do stuff on culture at the end as well. So this is a report well worth reading. Uh, sorry they caught us in a crusty mood, but, um, you know, keep an eye out for the commentary and enjoy the data.

Okay, thank you, Dave. Talk later.

The Metrics Brothers AI, SaaS, and More is a production of The Metrics Brothers Growth and CAC and a member of the Benchmarket Podcast Network. By accessing this podcast, you acknowledge that The Metrics Brothers make no warranty, guarantee, or representation as to the accuracy or sufficiency of the information provided Or the humor content of the jokes told. Ray, information, opinions, and recommendations presented are, according to our spouses, probably wrong and provided for information purposes only. This podcast should not be considered professional or, for that matter, unprofessional advice. We disclaim any and all liability for any direct, indirect, undirect, misdirect, incidental, special, ordinary, consequential, inconsequential, or other damages arising out of any use of or God help you reliance upon. Upon the information presented here. Ray Grothreich is based in New York City and available on X at Ray Reich. Dave Kellogg is based in Silicon Valley and available on X at Kellogg. Schenectady, which is French for unspellable, is not our actual production location. Thanks for listening.

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