I'm really excited about this next session here at SaaS Metrics Palooza 23. Have you ever met that person and after spending 15, 30 minutes with them, you're like, wow, this person, sky's the limit, going to be so successful. That's how I felt when I met Janelle Teng, Vice President at Bessemer Venture Partners, and also the creator of a great Substack newsletter called The Next Big Teng. With that, Janelle, the stage is yours. Thank you so much, Ray, that's so kind of you to say that. It's been a very busy few months in the cloud world, and I'm so excited to be here today to share the latest and greatest of what's been happening in the public as well as private cloud markets. As most folks know, it is no secret that the past year has been one of the most challenging and uncertain years in the entire history of the cloud economy. In 2022, the BVP Cloud Index was slashed over 50% in a severe market pullback event that we at Bessemer had coined the SaaSsacre. And as you can see from the chart, this decrease was a more marked decline compared to broader market indices. A main driver of the pullback was due to the external shock of hiking interest rates. So within the year, we moved out of a hyper low interest rate environment at record breaking speed. That's that light blue line there, which led to multiple compression and a significant amount of macro uncertainty, as demonstrated by the darker blue line in the chart. By the end of the year, the average forward trading multiple of the BVP Cloud Index had not just reset below the trailing 10-year average, but it also reset below the pre-pandemic long-term averages that we were used to. Beyond multiple, macro instability also impacted business fundamentals, as cloud companies faced many headwinds stemming from recessionary fears, such as lengthening sales cycles and tighter customer budgets. With such a bleak macro backdrop in place, SaaS IPOs came to a screeching halt, which was a stark contrast to 2021's IPO frenzy. Similarly, SaaS M&A activity in 2022 also slowed, but remained slightly more robust for several reasons. One, it was a record year for private equity-sponsored deals, and specifically, take private transactions. And on the strategic side, there were several blockbuster announcements of acquisition, including Adobe's announcement to acquire Figma, which marked the highest acquisition multiple offered for a software company of scale. So while that acquisition has not yet closed, this example reinforces that best-in-class cloud companies can command a valuation premium even in tough economic times. So suffice to say, conditions were very gloomy in the cloud world last year, but things seem to be looking up in 2023. Since the start of the year, the BVP Cloud Index is up over 15%. As highlighted, a lot of the uncertainty in 2022 stemmed from changing macro conditions, most notably the rapid interest rate hikes by the Fed in an attempt to curb inflation. 2023 has seen more macro stabilization, including slowing inflation, and the volatility index has remained healthier this year. So although multiples are still far from the highs of 2021, as visibility continues to improve around future macro equilibrium, we've now started to see trading multiples for cloud companies normalize closer in the range of what we've seen during pre-pandemic times. So consequently, with these stabilizing macro conditions, the SaaS IPO landscape is showing more signs of life. And just a few weeks ago, marketing automation leader Klaviyo made its public debut, which marks the first pure-play SaaS company to file to go public after an almost two-year drought in SaaS IPOs. And Klaviyo is truly a best-in-class cloud company across many SaaS metrics, including efficiency and scale. And as of yesterday's close, Klaviyo is currently trading about 17% over its IPO price. On the M&A side, we saw many of the trends in 2022 continue into this year. Take-private deals were still quite prevalent, and there was also robust activity on the strategic side. We saw companies make M&A investments to bolster their AI capabilities, such as in the case of Databricks' $1.3 billion acquisition of MosaicML, which we'll talk a bit more about later on in this presentation. And another recent blockbuster deal announced just a few weeks ago was Cisco's acquisition of log analysis and cloud observability software provider Splunk for $28 billion in 2020. So if this deal is approved, it will instantly shoot up into the top five largest software acquisitions in history. At Bessemer, we fundamentally believe that the cloud model, with its recurring revenue nature, low marginal costs of distribution, and strong net dollar retention dynamics, is perhaps one of the most attractive and certainly one of the most resilient business models to be invented. So in the face of one of the toughest years in history for the cloud economy, we saw companies remain resilient by adapting very quickly in a shift from the age of access to the age of efficiency. Just within the year, we saw public cloud companies shift their focus away from growth at all costs towards profitability, which is quite powerfully visualized here in this chart. And today, the top 10 highest valued BVP cloud index companies are driving an average of about 14% free cash flow margin, which is more than two times what we saw from the same top 10 cohort just a year ago. And such efforts to drive profitability are certainly not going unnoticed by investors. Because these companies adapted, investors are also responding to the paradigm shift. And cloud leaders that took swift action to boost efficient growth were rewarded for this initiative. So let's contextualize this into a heuristic map to public market valuation sentiment to really bring things to life. At the end of bull market exuberance in 2021, the height of that bull market, a 1% improvement in revenue growth, had the same impact on cloud valuation as a 6% improvement in free cash flow margin. So said another way, from a valuation perspective, growth was six times more important compared to profitability. Today, this tide has turned, and the ratio stands at two to one, still in favor of growth, but certainly more balanced compared to the peak market period. A 1% improvement in revenue growth has the same valuation impact as a 2% increase in free cash flow margin. So we've discussed what happened in the public market, let's turn our focus to the private market. As a guide for what we've been seeing on this front, I'll highlight a few key trends that we've observed in this year's cloud 100, which is a list of the top 10 hundred private cloud companies that we at Bessemer Venture Partners, along with Forbes and Salesforce, compile every year. And as it is a headline, similar to what we saw in the public market, last year was an extremely challenging year for private cloud companies as well. It was in fact the first year that we saw the total cloud 100 list value decrease, because it ended up being down 11% year over year. On the growth front, after years of defying gravity, we saw the average cloud 100 growth rate decreased to 55%. And this is down almost half from the average of 100% in 2022. The top quartile companies are still growing 70% year over year, which is very impressive. But that is down from an average of about 120% just the year before. And alongside this declining growth, we also saw the average cloud 100 ARR multiple decrease for the second year in a row. It is now down to 26x, which is down from 30x in 2022, and 34x from the peak in 2021. And this trend that we see here very much mirrors the pattern of multiple compression that we observed in the public market. Despite the slowing growth rate, we've seen the top 100 private cloud companies manage to scale with strong operational efficiency. So almost a quarter of the cloud 100 cohort is already cash flow positive, and two-thirds are forecasting to become cash flow break-even or profitable by the end of next year. Even when focusing on the cohort of cloud 100 companies that are still unprofitable, about 60% said that they have meaningfully improved burn in 2023, and only 7% said that they have chosen to meaningfully increase burn this year. So again, this supports the overall trend we've seen in the public markets with companies focusing on prioritizing profitability over growth in this new macroeconomic landscape. And we've highlighted a few times now how efficiency is the current zeitgeist of the cloud community. As such, throughout the year, my team and I at Bessemer have consolidated a best practice guide from our portfolio with key strategies for founders to leverage as they drive efficient growth. And we've organized this guide into major P&L bucket tactics, so from COGS to sales and marketing, G&A, R&D, so on and so forth. I'll walk through a few case studies now as we explore how BVP portfolio companies applied several of these tactics to drive efficient growth. And let's start with a gross margin case study. Gross margins are quite key since they set the ceiling for profitability. As an example here, public cloud companies with gross margins under 60% structurally find it very difficult to drive over 20% of free cash flow margin. A major lever that companies are using to improve gross margins is to optimize cloud hosting costs. One of the most effective strategies we've seen on this front is to improve underlying code to unlock performance and cost optimization. In this case study, which is what SendGrid did, they did a systemic review of their entire code base to understand the most expensive components and subsequently determine if there are potential optimizations to be unlocked. So what they did was they essentially identified specific components within their service-oriented architecture that were responsible for the largest consumption costs and then refactored that code by switching languages for microservices. And most impressively, this optimization not only enabled a performance boost, but also led to annual cost savings in the magnitude of seven figures, so it was a very significant cost saving for them. SendGrid did this optimization on their own with an internal team. But as seen on this slide, there's really a whole ecosystem of cloud cost management software that has blossomed to help companies leverage this tactic as a cost savings opportunity. And now let's move on to a sales and marketing case study. Did you know that the average public SaaS company spends about close to 40% or more on revenue, of revenue on sales and marketing? And this often represents the largest bucket of OPEX. So consequently, any improvement and efficiency on this front can lead to significant bottom line gains. And GlossGenius in our portfolio is a role model here on how to hone in on S&M efficiency while scaling. So for context, GlossGenius is a vertical SaaS and payments leader in the beauty and wellness industry. Initially, the company grew completely organically, so just through word of mouth. But as they grew bigger and scaled, they started to incorporate food and inorganic channels, such as referral programs. As they layered on these new channels, they only doubled down and further invested in channels that were able to hit their key North Star metric, and they very quickly cut programs that did not meet this threshold. So a key guiding principle for GlossGenius in this case was a sub-eight month CAG payback and at least a three times LTV to CAC. But it's important to note that every company is unique and should certainly develop their own benchmarks and heuristics here. And our next study involves G&A. G&A is oftentimes on absolute basis the smallest bucket of OPEX, but is ripe to really drive efficiency given it is mostly a cost center. There are a variety of ways to optimize on this front, such as rationalizing real estate footprint, you could increase internal automation, or you could negotiate and reduce software spend. Hatmo, a Munich-based construction software company in our portfolio, did just that by successfully renegotiating 33% off a few of their key large software contracts. Hatmo did this by using a third-party vendor called Vertis to help them, but companies can certainly negotiate independently or can leverage a growing ecosystem of SaaS optimization and services and software to help them fight SaaS fraud. Lastly, an overall recurring theme that we've witnessed over the past year is that more and more cloud companies are leveraging AI and LLMs as a major way to drive efficiency and boost productivity internally and across all P&L categories, from automating content creation and marketing, to code generation and engineering, to document review and legal. And we find this to be a very exciting trend given we're in such early days of seeing AI adoption across enterprises. But adopting AI is not just important to driving internal efficiency, but certainly very important to accelerating customer-facing and revenue-generating product innovation. So just within our portfolio, we've seen companies such as Zapier and Developer Tools, Intercom and Customer Success, to Canva and Design Software, adopt AI to help them drive new product features. And the relevance of AI in the cloud can't be understated. So 55% of the Cloud 100 have announced generative AI features just within the last eight months. So the speed at which the top private cloud companies are incorporating AI is extremely rapid. And on this front, companies are employing different strategies to augment their product offerings with AI. There isn't really a one-size-fits-all approach here. So 70% of companies are leveraging AI ML, generative or otherwise today, in their products already. 65% of them are pursuing an embedded AI strategy, which is where AI is integrated to enhance current features within a core flagship application. And about 5% are AI-native. And what do these strategies mean? Here are a few case studies from the Cloud 100 cohort, really to bring these tactics to life. So Cloudinary is a cloud solution for managing digital assets. They pursued an embedded AI strategy, showing how a company doesn't need to be an AI company from day one in order to incorporate AI into their roadmap. The company released new generative AI features, such as generative replace and generative fill, to really enhance the capabilities of its programmable media image and video API. So for example, if you look at the image on the slide, generative replace can use natural language prompts to detect and replace objects in photos. So a user in Cloudinary can now use AI to identify and transform a subject's black sweatshirt into a white designer suit, all just using a simple text prompt. And the outcomes of this strategy were certainly quite powerful. So for one, Cloudinary's users were able to unlock AI-powered productivity gains. And two, Cloudinary was able to expand its TAM through enabling non-technical users to now leverage its platform to make previously developer-grade modifications. And three, this entire strategy has helped to create an NVR multiplier by allowing Cloudinary to now reach new personas and functions, such as marketers. The second case study is a story that we already highlighted earlier. In July, Databricks acquired MosaicML, which is a platform enabling enterprises to create, train, and deploy generative AI models. This acquisition furthered Databricks' platform functionality in AI, leading to one, rapid expansion of Databricks' AI product portfolio, supporting the recent launch of its Lakehouse AI platform, two, creating a cost-effective unified solution for its current customers to manage the data for their own proprietary AI models, and three, strengthening Databricks' mission to democratize access to data and AI for its customer base. Of course, an acquisition approach is certainly not for everyone. Another strategy that we are seeing is cloud leaders are building natively on AI from their very inception. So an example here is DeepBell. As we think about AI-native companies, we define them as AI being ported to their platform from the very get-go. And DeepBell is a real-time AI part language translation firm that is built natively on neural machine translation and deep learning architecture. So from day one, this has enabled DeepBell to develop high-quality translations that resonate with native speakers and also produce translations with a high degree of accuracy that can be used in critical business settings. Lastly, what this has helped DeepBell to do is that it's able to expand its product suite to adjacent capabilities such as DeepWrite. And these are just truly a handful of examples. As more and more founders look to incorporate AI into their products, I would recommend that you evaluate your product roadmap really from first principles. So meaning, if you were to build your product from the ground up today with the AI capabilities that are now possible, how would you best solve your customer's pain point? I recommend looking for three types of opportunities. First, look for language interfaces within your current product. So wherever you have text interactions today, these could be superpowered by AI. Next, look for any predictable and repeatable sequences of workflows within your product because after all, these technologies are fundamentally designed to predict your next step in a sequence and thus can be used to consolidate manual workflows in your product. And then finally, look for cross-product workflows. So what do users do immediately before or after using your product that involves language? This could be sending an email, it could be generating a report or pulling data from another system. All of that should be considered in scope for your roadmap. And as a founder in today's AI revolution, the incredibly exciting news is that as you think about rewriting your product roadmap, there are so many complementary solutions and providers that you can leverage to extend your AI capabilities. And this entire ecosystem has blossomed just within the past few months, offering many options and flexibility for all who want to participate in this burgeoning paradigm shift. And so just to summarize what we've covered about the current state of the cloud, despite a turbulent last few years, the cloud economy is showing early signs of a rebound with a thawing IPO window. The current zeitgeist has moved from the age of access to the age of efficiency, where there is now a renewed focus placed on profitability and efficient growth. And finally, the rise of AI is spurring innovation and has certainly injected more energy and has helped to reignite the cloud ecosystem. And so thank you all for joining me today. If you enjoyed today's discussion, please feel free to check out all our articles and research papers on ddp.com for all things cloud related. Thank you. Danelle, thank you so much. Man, you covered so much information. You did it so efficiently. The good news is you get to spend a few more minutes with me and with a couple of questions. Sure, there you go. So one of the things that we've talked about once before was in the state of the cloud 23 report, you talked about your top five predictions. And one of those predictions, and this was earlier this year, was that the initial benefits of generative AI adoption would accrue to the individual versus to the company. Has that thinking evolved or your experience evolved in the last six months? You know, I think the primary beneficiary in this whole AI revolution is still the end user. And you can think about that interest in terms of productivity gains or new ways to do things that are benefiting that innovation is flowing directly to the end user and has been democratized in a way that we haven't seen in previous years. So in the past, a lot of these gains went to large companies. So if you think about, you know, the whole autonomous vehicle wave, a lot of that AI research only benefited, at least at the time, a lot of the companies and not the end user today because AI is so democratized, and you know, anyone can literally go online, have a web browser and use AI directly from their homes. I think variable end users everywhere are sort of able to access that value very, very quickly. But at the same time, I give a lot of credit to companies as well, who are incorporating some of these AI features, as we've talked about, really to bolster their current platform. And so I think the value kind of goes, you know, everyone can benefit from the movement, but I certainly think that the end user is still the key beneficiary in all of this. And then a second question, you talked about how so many B2B cloud companies today are trying to optimize their cloud infrastructure costs, get that cost of goods sold down, and you know, half our audience here are VPs of finance or CFO, so they love that. But as I've been trying to do a little bit of research and talking to players around large language models, the foundation, by the way, I'm dangerous because I have a little bit of information, so I may have jumped to a conclusion, but because they're so processor intensive and the GPU shortage has restricted capacity, one of the challenges is if you're leveraging one of these foundational LLM models, your costs can shoot through the roof because there's no capacity. So do you have any insights or ideas of how you can mitigate that risk as a CEO or CFO trying to leverage these foundational models? Yeah, no, I think that's certainly a hot topic in the entire, not just VC world, but tech world as well, where, you know, the demand for compute power has certainly outstripped a lot of the supply. Long term, you would think that, you know, with the innovations that are going on, tech companies with supply chain problems being fixed with more competition, the supply side will sort of normalize to the demand side. I would say in the short term, what's so exciting is that there are quite a few startups or solutions coming in to try and fill that gap and provide some of this GPU capacity or compute power to companies of all sizes in a very efficient manner. And so there are marketplaces now that do that. There are companies that are trying to find capacity from other areas and bringing it, making that capacity more available to smaller customers. And so there's now also an ecosystem that's very, very nascent, but blossoming, trying to fill that gap before hopefully the long term vision of demand and supply converging actually happens. Then my last question. So Carta is here at SaaS Metrics Palooza and they have 36,000 customers on their platform, so they provide a lot of insights on the state of equity investments, VCs. You showed M&A acquisitions, you showed public company comps. But here's a question for you. In your State of the Cloud 23 report earlier this year, you surveyed 60 of the investors at Bessemer. And when will be the best time to raise money? And beyond right now, if you're a great company, the largest percentage said second half 24. Have you evolved your thinking on that optimal time that most CFOs and CEOs can think about getting their next round of VC investment? Yeah, I think that was a really controversial question, even in our investor base, because the answers were kind of all over the place, really. And I would say my take is like, yeah, don't wait to raise if you need to, if you think you'll need to. And by the way, fundraising should be the means to an end and not the end itself. So again, if you can find a way to break even or have strong fundamentals without additional fundraising, don't think you need to just because. But I would say like the best time to fundraise is when you don't actually need the money because you're in a stronger position to run a proper process because you don't feel the urgency when you're kind of facing your cash out date. You have more leverage in negotiations, can be more thoughtful about your different options. So I don't think there's ever going to be like a perfect time to raise. And again, every company kind of has different things to balance as they think about this decision. But I would say always try to be in a position, a strong position when you do decide to go to raise. And again, don't think of raising as the end goal. It should be the means to an end. So what are you raising for? And start from that premise before thinking about how much you want to raise it with. It's a lot of sense. And for our audience, if you happen to miss our first session of the first day, two of Janelle's partners at Bessemer Venture Partners, Byron Deeter and Samir Dholakia, talked about the metrics that they're looking at that they really want to see to ensure that efficient growth is happening. If you're thinking about raising money. So I highly recommend if you didn't see that session to go see that. Go to Bessemer and BVP.com and Atlas is an amazing resource of all kinds of reports, benchmarking, et cetera. And then, of course, follow Janelle at The Next Big Teng on SubStack. Janelle, thank you so much for being a featured speaker here at SaaS Metrics Palooza 23. Thank you for having me, Ray. Bye bye.