Amplitude, Inc.

Recording in progress. And welcome to Amplitude's second quarter 2026 earnings conference call. I'm John Streppa, head of investor relations, and joining me today are Spenser Skates, CEO and co-founder of Amplitude, and Andrew Casey, chief financial officer. During today's call, management will make forward-looking statements, including statements regarding our financial outlook for the third quarter and full year 2026, the expected performance of our products, our expected quarterly and long-term growth, investments, and our These forward-looking statements are based on current information, assumptions, and expectations, and are subject to risks and uncertainties, some of which are beyond our control, that could cause actual results to differ materially from those described in these statements. Further information on the risks that could cause actual results to differ is included in our filings with the Securities and Exchange Commission. Thank you for joining us. Thanks, John, and good afternoon, everyone.

Welcome to Amplitude's second quarter 2026 earnings call. Today, I'll cover three things. First, our Q2 results. Second, how we transformed Amplitude into an AI company and why every company I talk to now wants to learn how they can do the same. Third, a look at our product and a spotlight on our customers. Let me start with the numbers. Q2 revenue was $101 million, up 21% year-over-year. Total annual recurring revenue was $410 million, up 22% year-over-year, and up $36 million from last quarter. That was made up of two parts, inorganic ARR from Statsig of $17 million and organic ARR growth of $19 million. Andrew will walk through the details. Non-GAAP operating loss was 1.5 million. Customers with more than 100K in ARR grew to 824, an increase of 30% year over year. Both AI natives and large enterprises are driving this growth. Let me step back and tell you about our transformation and then how we're helping customers along their AI journeys. We help companies build better products. Every company wants to transform to deliver software products in an AI native way. We've made that transformation at Amplitude over the last two years, and now our customers are looking to learn from us. Becoming an AI company starts with the organization. Two years ago, we first transformed our engineering team by bringing in AI engineers who built with it for years. Then we moved into adjacent functions like product management, design, and the more technical parts of go-to-market. We also brought in AI expertise through acquisition. Founders and other members of the team from these companies have taken leadership roles across amplitude. I have focused on bringing in leaders who are former founders and who have a technical background. Gab, our Chief Product Officer, started multiple companies, including Loom Systems, which sold to ServiceNow in 2020. In addition, Nate, our Chief Commercial Officer, has a degree in math and physics and started his career as an engineer programming in C++ and Java and building databases. Most recently, we added Angela Ferrante as SVP of Marketing. Angela founded Laudable, which went through Y Combinator summer 2021, sold it in 2025, and is a technical marketing leader who builds apps with AI in her spare time. In addition to all of this, we're continually re-educating everyone at Amplitude through initiatives like AI Week, Unlimited Token Spend, and a Living Token Leaderboard. This has all resulted in three times the number of pull requests in six months. We've reduced our pull request cycle from 5 hours to 44 minutes. Bug reports are down 55%. 5% of our pull requests are submitted from designers and product managers with no engineering involvement. We've leveraged AI to shorten our closing process by a day. We've built customer health dashboards that enable our sellers and leaders to track customer usage, bringing our own amplitude data alongside Salesforce data and data from other sources. When I talk with our customers, they are all focused on how they can transform their business to be AI native like we have done at Amplitude. The AI landscape is changing rapidly and they want to learn how to adapt. Our customers are on a spectrum of AI adoption. Our job is to meet them where they are and then educate them on how to take the next step. We work with leading AI companies to learn what the bleeding edge in product development looks like. We use that knowledge to educate the rest of the market, including the largest enterprises deploying at scale. More than 40 AI-native companies now pay us over $100,000 a year. Those customers include Harvey, Midjourney, Character AI, and one of the leading foundational AI model companies. On the enterprise side, enterprises are now more than 68% of our ARR. This quarter included agreements with Paramount, Jaguar Land Rover, and Domino's Pizza. We've improved our pricing and packaging. We reduced down to a single meter to make it simpler for enterprises to add additional products. We increased the amount of data on our free plan, so we're the best for those just getting started. Amplitude has the best pricing, whether you're a startup or a large enterprises. One of the biggest changes with building an AI-native company we're seeing at Amplitude and with our peers in private markets is in the cost structure. A lot of inference spend is required in order to deliver AI-native products which increases the amount spent on cost of goods sold. On the other hand, you do not need to add as much operating expense to continue to grow a business at scale. We are embracing this change in cost structure as part of our transition to an AI-native company. For now, we expect gross margins to stay in the low 70s. We will offset that with a commensurate reduction in operating expenses. That allows us to continue to show the same leverage in operating income as we have planned. I am continuing to drive amplitude to a 20% plus operating margin business over the long term. We offer three products to meet customers wherever they are on their AI journey. Amplitude gives you the deepest understanding of how people use your product. Our agents increasingly do that discovery for you. Statsig gives you feature flagging and experimentation built on the world's most advanced stats engine with an engineering first view. It's also integrated natively with data warehouses. WAVE is the future of product development, self-improving products where we automatically recommend what to build next based on signals from users. While we're early here, I'm actually excited to show you a demo today. Together, these three products close the product development loop. Understand what's happening, measure what shifts, and ship what matters. That loop is how AI-native business is built. Let me go deeper on amplitude. Global chat is becoming the primary way our customers interact with their product data. You ask it a question in plain language and it does the analysis, no dashboard building required. It's become the de facto way many companies do product analytics. Global Agent finds the root cause behind 75% of customer questions and hands you the answer. There are 1.3 million global agent interactions every week, and root cause discovery rates are improving by one percentage point every month. As of today, over 40% of all insights come from AI agents as opposed to humans, and we expect this to continue to grow. Today for our demo, I want to show you custom agents, Statsig, and Wave. Let's start with custom agents. Custom agents are teammates that automate recurring workflows on your product data and push that work to other tools and systems. This is our chat interface. An increasing number of users are interacting with Amplitude mostly through chat and agents. I'll ask a question. Which group of users are most likely to purchase next week? Chat can now write its own code to perform this analysis. This unlocks the ability to run deeper analysis and create powerful new graphs and artifacts, including diagrams like you see here, out-of-time decile lift, an ROC curve, segment propensity. You can dig in by seeing the actual code used and step-by-step analysis. This type of deep analysis has never been available before in analytics tooling. We are no longer bound by the constraints of a UI. We can also create automatic and recurring agents that run in the background. I give it these instructions. I want this analysis run every Monday morning. Cross-reference with marketing activity and confluence. DM me the results in Slack. Amplitude then creates the agent that you see here. This is the entire prompt, including connectors to Atlassian and Slack. It will run regularly every Monday and push the results to me. We are building the best analytics agent across all data sources. StatSig is the leading product for experimentation and feature management. Statfig runs experiments natively on your cloud data warehouse, whether that is Snowflake, BigQuery, Databricks, or Redshift. Let me show you what this looks like. Here is the results page for one of hundreds of experiments that an e-commerce customer is running. This experiment is testing a larger product image versus the default size. There's a lot of statistical machinery behind a good experiment, but the UI makes it simple for an engineer to run. Up top, they can monitor exposure, which is saying if the experiment is healthy or not. We expect to see a 50-50 split, so we're doing good, and as you can see over here, we're getting a healthy check. We move to the scorecard that has the results. This has a confidence interval of 95%. StatsAid uses advanced techniques like QPID and sequential testing that allows engineers to speed up time to decision. We have those turned on. In monitoring, We see specific events we're tracking for this experiment. We're seeing positive results. The checkout event is up by 27.4%, plus or minus 2.3%. Cart conversion is up, total purchase dollars is up, while carts per session is down. For the rollout of this feature, we have a progressive rollout, starting with employees, moving to early access users, then early release, and a scheduled rollout for everyone else. Statfig has a variety of advanced experimentation capabilities for rollout, like feature gating, dynamic configs, and automatic rollbacks. Together, these are the mechanisms that a team uses to ship a change gradually, tune it while live, and pull back automatically if it goes wrong. Last, I want to show you Wave, the future of product development. WAVE allows for self-improving products that automatically recommend what to build next based on signals from your users. WAVE is magical. WAVE looks across all the different data sources you have, analytics, experimentation, session replay, guides and surveys, feedback, and many others. It then synthesizes that data into a set of product recommendations, plans those recommendations, and then helps you create those changes in your product. I'm going to walk you through a real example WAVE suggested and built for Amplitude's documentation site. On our documentation site, WAVE found a spike in failed searches through looking at session replay and analytics data. The core problem was that search on our docs page fired on every keystroke. Typing a single letter to start a search returned an empty no result state before the person finished typing their search, leading to a bad experience for users. Wade explains the reach of this issue. Every user reviews a search. It has an expected impact of decreasing total search failures by 80%. Then, Wade has automatically created a visual example of the problem below so it's easy to understand. It also has a full explanation of the evidence. For the plan, Wade sketches a wireframe of the recommended update, setting a three-character minimum and a 200-millisecond debounce to trigger the search. Wade can also drive execution. It automatically created the pull request and cursor wrote the code. Mark, our technical writer, was able to merge this pull request and ship this. No engineers, no designers, and no product manager. Finally, WAVE measures the results of the change. There is a massive decrease in total search failures. Simply amazing. Simply amazing. Now, let's talk about some of our customers. We had a great quarter with both new lands and expansions. We added or expanded our relationship with customers including Paramount Global, Jaguar Land Rover, Teladoc Health, Chime, Disney Ad Platforms, F5 Networks, Coursera, Grammarly, Kraken, and Crunch Fitness, among others. I want to tell you three stories about how these customers are leveraging our platform. First is Coca-Cola Fensa, which sells to hundreds of thousands of small shops across Latin America. Every shop is different, but for years they had to run the same broad campaign to everyone because there is no way to tailor a message to that many retailers by hand. AI changed that. They began sending each retailer its own recommendation every week, written by AI. Their own teams were actually skeptical. A different message for every shop every week felt risky, and no one knew if it was going to work. They used amplitude to find out. Their AI campaigns actually had an 11% click-through rate, four times higher than their previous approach. Our cohort analysis also showed that this lift lasted. Once a retailer engaged, its revenue stayed higher in the weeks that followed. That evidence turned skeptics at FEMSA into believers, and they scaled from a 2,500-store pilot to 690,000 retailers. The second is Replit. Replit is an AI app builder that allows non-technical builders to turn an idea into an app using AI. Replit has a large global user base of passionate builders that provide feedback. Replit is using Amplitude AI feedback to understand how customers are engaging with their agents. They've connected AI feedback to Zendesk, App Store reviews, Twitter, and Reddit, and surfaced and prioritized what problems should be solved to increase their retention and engagement. It changed weeks of manual work on their end into a simple click with amplitude. This is the next generation of product development at work. Third is The Economist. The Economist is a print magazine that's in the midst of a transition to digital delivery and subscription. Their research arm built an AI assistant called Lens that answers questions for analysts and strategists using The Economist's content. Their normal analytics could show what users did, but not whether the AI's answers were any good. The team was reading sessions by hand, but they couldn't keep up. Amplitude agent analytics now scores every answer Lens gives automatically. They went from reading a handful of sample sessions to being able to see across all of them. Today, Lens holds a 96.9% task success rate, and weekly failures are down 84%. That is the loop working. Build with AI, measure whether it is good, and fix what is not. To wrap up, the companies on the bleeding edge are choosing Amplitude. We've transformed Amplitude to be AI native, and we're building the future on what can be done in analytics. Self-improving products are closer than ever with Wave. Our pace of innovation continues to accelerate, and we're building in a way that can scale with leverage. I am extraordinarily excited about what's ahead. With that, I'll hand it over to Andrew to walk you through the financials.

Thank you, Spenser. This was a strong quarter and a clear step forward in our execution, bringing our vision of how products will increasingly be developed and improved. We crossed $100 million in quarterly revenue. ARR reached $410 million, growing over 22% with the addition of the ARR assumed from the STAT-SIG Business, and Free Cash Flow was a record quarterly high of $23.7 million. We also returned $69 million in capital during the quarter as part of our share repurchase program. We accomplished these milestones while integrating the StatSig technology and customers, managing through our own AI-native evolution, and implementing our new pricing and packaging strategy. AI is changing how customers use Amplitude. The more our customers build with AI, the more they need to measure. Customers that adopt our AI into their workflows run nearly 10x the number of analyses compared to those that are running things manually. This increases the value that customers receive from the data ingested into our platform and makes it more likely that they'll both ingest larger amounts of data and expand into additional products, which is the basis of our growth. Our new pricing and packaging is working. It supports our market consolidation strategy by providing customers with a lower overall cost if they consolidate applications onto our platform. It provides customers greater cost predictability and simplifies the quoting process for our sellers. In the second quarter, 70% of the ARR we closed was on the new model, up from 25% in the first quarter. Now, 28% of our total ARR is on the new pricing and packaging. This is leading to average ARR increasing, higher multi-product attach, and longer contract duration, which all contribute to greater durability of our revenue. Our margins reflect a choice. These are investments we are making to drive future growth with increasing profitability. Our gross margin was down over one point versus Q1 due to the integration of StatSafe. We are working to optimize the new hosting environment and cloud structure, but it will take some time to improve from the low 50s gross margin closer to our expectation of 70 plus for the static business. We're also experiencing higher customer adoption of AI capabilities and greater data ingestion into our platform, which combined has increased our costs and reduced our gross margins by an additional two points versus Q1. We have long maintained that we will grow with leverage. This investment in the cost of revenue places greater emphasis on the management of our operating expenses to a lower level in order to achieve the leverage. In Q2, we've managed down our sales and marketing to below 40% of revenue and G&A to the low teens, which is contributing to an increase in operating margins. We will continue to manage both areas lower as percentage of revenue over time and we will continue to invest in R&D to drive innovations. We are instrumenting our business to accelerate growth, capture market share, and show leverage. One key metric we monitor is the usage of data compared to the entitlement for our customers as this is a primary monetization metric. Today, that metric is at an all-time high. This is the output from better pricing, packaging, and more usage driven by our AI features. We have increased the durability of our business through our RPO growth and reinvented our internal processes to capture scalability that AI offers. We are running to the AI opportunity and taking share as we go. Turning to our second quarter results, as a reminder, all financial results that I'll be discussing, with the exception of revenue, are non-GAAP. Our GAAP financial results, along with a reconciliation between GAAP and non-GAAP results, can be found in our earnings press release and supplemental financials on the investor relations page of our website. Second quarter revenue was $100.9 million, up 21% year-over-year and 8% quarter-over-quarter. Total ARR increased to $410 million exiting the second quarter, an increase of 22% year-over-year and $36 million sequentially. This includes $17 million of incremental ARR from the StatSig business compared to the $16 million we expected to add when we shared our first quarter earnings. Total remaining performance obligations grew 35% year-over-year to $483 million. Current RPO was up 30% year-over-year, and long-term RPO was up 47% year-over-year. Here are more details on the key elements of the quarter. We had a strong quarter for both new and expansion deals in the enterprise, and platform sales were again particularly strong. 48% of our customers now have multiple products, with 80% of our ARR coming from that cohort. We have over 26% of our ARR from customers with five or more products, up 2x since the second quarter last year. In-period net dollar retention was 105% on a pro forma basis, led by cross-sell expansions across our customer base. This performer basis includes Static and Amplitude customers. Gross margin was 71% for the second quarter, down approximately four points from the second quarter of last year and down four points sequentially. This was driven by continued growth in inference costs as customer adoption of our AI2 has accelerated along with the integration of the Static business and its hosting environment. Sales and marketing expenses were 39% of revenue, down from 44% in the second quarter of last year. G&A was 13% of revenue, down one point from the second quarter of last year. R&D was 21% of revenue, up approximately three points from the second quarter of last year, reflecting investment to scale the static opportunity and support for those customers. Total operating expenses were $73 million, or 72% of revenue. Operating loss was $1.5 million or 1.4% of revenue. Net loss per share was negative $0.01 based on 129.4 million basic shares compared to $0.01 a year ago. Pre-cash flow in the quarter was $23.7 million or 24% of revenue compared to $18.2 million or 22% of revenue during the same period last year. We ended the quarter with approximately $162 million in cash and investments. We have conviction in the long-term value of our platform and have used and will use our cash to minimize the impacts of dilution. Our balance sheet position remains strong and allows us the opportunity to be more aggressive in our M&A strategy to accelerate our R&D roadmap when appropriate. Now, turning to our outlook. As a reminder, the philosophy of how we set guidance is through the lens of execution. We are pleased with our overall progress on consolidating point solutions to our core platform and the adoption of our different AI technologies. We've instrumented our business and selling process to make it easier to use more of our platform. We believe that we are well positioned to continue to accelerate our growth in a profitable way. For the third quarter of 2026, we expect revenue to be between 105.6 and 108 million, representing an annual growth rate of 21% at the midpoint. We expect non-GAAP operating income to be between $2.5 million and $4.5 million. And we expect non-GAAP net income per share to be between $0.02 and $0.03, assuming a weighted average share's outstanding of approximately $133 million as measured on a fully diluted basis. For the full year, 2026, we are raising our expectation for full year revenue based on the performances in the second quarter to be between $407.2 and $411.2 million, an annual growth rate of 19% at the midpoint. We are also raising our expectation for the full year non-GAAP operating income due to performances in the second quarter and actions taken in the first half to be between $6.3 million and $9.3 million. We expect non-GAAP net income per share to be between $0.06 and $0.08, assuming weighted average shares outstanding approximately $137.1 million as measured on a fully diluted basis. In closing, we are accelerating our pace of innovation, and we're growing the value that we can deliver to our customers. We have confidence in our ability to scale a durable and growing business while also bringing agentic analytics to the world. With that, we'll open up for Q&A. Over to you, John.

Thank you, Andrew. Going to Q&A. For the sake of time, please limit yourself to one question and one follow-up. Our first question today will come from the line of Mark Cash from Raymond James, followed by Jackson Ader at KeyBank. Mark, your line is now open.

Thanks, John. Yeah, if I could start with Spenser. I really wanted to ask around WAVE. I appreciate it's still limited data, but I think you've been using internally for several months now. I guess, do you see WAVE It could cause a company shifting away from using bespoke agents for specific use cases towards a broader AI native product development platform from what you're seeing. And if so, how could that change your buyer, maybe the budgets you see in the addressable market over time?

When you say bespoke, like say more on that, like.

Yeah, instead of using particular agents to do a specific task underlying, because you have like a swarm of agents doing things underneath for Wade.

I see what you're saying. I see. Okay, so let me separate out a few different things. What we have on the amplitude side, and I showed with custom agents, is you have these agents that can look across your data and find insights for you and get to the root cause of questions and do that on a regular basis and kind of send it out. with what WAVE is doing in particular, to your point, is it's looking at all your data all the time and then saying, hey, here are points of friction. Here's something that's not working how it should be. Here's a feature that I think you should emphasize more. Here's something that I think is a best practice that you're not doing. And so it's operating at a kind of higher level. In terms of the persona, I think what we're seeing is a convergence between engineers, product managers, and designers into this AI builder persona. It's not really like you have engineers who are thinking about what to build, and you have product managers who are also just chipping code. And so the best, you know, if you look at the AI-native teams that everyone's aspiring to be, these roles are melding. So it's still the same problem we're solving, which is how do we help you build a better product, but we're just automating more of it because we're saying, hey, we're going to look at all the data all the time and then suggest recommendations. We've been talking about self-improving products here at Amplitude for about nine years, and so I've actually been blown away by what is possible with the technology today where it's just, and many more. like it's getting better because it's just translating recommendations. You no longer need someone to go into an amplitude or to any data system and say, oh, here's what my interpretation of these results. So I do think. In terms of budget and persona, I do think, again, that means instead of having these distinct roles, you have engineering, product management, and design merge. You're still doing digital product development, and that still rolls up to some leader, the same executive before. But, yeah, the way you do it looks different. Did I hit it?

Yeah, absolutely. Thank you for that. And if I could follow up with Andrew real quick. If my math is correct, the guidance for the year was raised at more than two times the beat for revenue and operating income. So I was wondering if you could just go to the key drivers of lifting for expectations. You saw some pressure on pro forma expansion sequentially there in the quarter. And then what you consider regarding margin leverage and levers while you're facing COGS pressure and ramping token spend internally. Thank you.

Yeah, sure. So a couple things. One, that when we look at our ability to actually generate revenue in the out-quarters, one, we start with the strong balances we're booking that are showing up in our RPO. You know, when you've got commitments from customers for a longer-term duration, you start to have better and better predictability about your future revenue. So that's the first thing, and it's one of the reasons why we emphasize that metric so much. The second thing is we look at how much our customers are actually responding to some of the initiatives we're putting out. And that comes in the form of our new product capabilities, our new pricing and packaging, areas where our sales team is running new promotions and activities. all those are bolstering our ability to see a stronger and stronger pipeline, and that pipeline progresses faster through its stages, which gives us greater and greater confidence, so we'll add more and more in new ARR. Now, from a revenue perspective, as you know, the predominance of our business is all coming from our subscription revenue, so those key factors on understanding, you know, what's the baseline, what can you see in your pipeline, what you expect it to convert, is what I refer to as our ability to go execute against the plans that are in front of us, and Sales teams have been doing a really good job of driving consolidation in the market, and that alone with our products is driving great conversions. That's the first thing. On some of the margin areas, I would tell you, look, we just, in the case of the Google environment that we got for the stat seg, we're going to focus on driving optimizations in that environment over a period of time. It's definitely lower. We said in the low 50s, but from a gross margin perspective, that comes from us taking on a whole new environment. Most of Amplitude, all of it, in fact, is on AWS. So we took on a whole new cloud and hosting environment. And, you know, you have to go through the paces of really optimizing how you run those environments for customers. Our first objective was integrating and making sure there were no disruption in service. Now we're moving quickly into how we can optimize those environments. So that's one big labor on the gross margin side. And we're constantly looking at how we can make investments to go drive greater efficiencies across all of our operating expense areas.

Great. Thank you, Mark. Our next question will come from the line of Jackson Ader from KeyBank, followed by Scott Berg. Go ahead, Jackson.

Hey, thanks, guys. Good to see you. I was curious on, I guess, Andrew, kind of sticking with you and talking about rather than on the cost side, just on the operating expense side, we've seen really nice acceleration in organic ARR from the business, but If I take a longer-term view, even on a non-GAAP basis, we're still around break-even. I'm curious, as you're thinking about driving more leverage and more incremental margin that you talked about before on the income statement, what kind of impact should we expect that to have on the organic growth number if at all?

Well, I have to tell you that, one, we still expect, from an organic perspective, we've got a great set of products. Spencer just walked through a number of them that are brand new to the market. We think they have enormous total addressable market that we can go after. So revenue growth will be the predominance where we'll see increasing operating income. As far as leverage as a percentage of what that would be, a percentage of operating income, I do expect over time that we'll be able to drive better and better revenue Thank you for joining us. When I first joined, that was in the low 60s. We're well into the 80s now as far as percentage of what customers have ingested versus what their entitlements are, and that portends increasing expansions on upsell, which is usually where we've had a lot of problems in the past of overselling and having to right-size contracts. The first time we're past those things and we're starting to see really good upsell, not just cross-sell, driving growth. So revenue growth is the predominance of the first aspect of driving improving profitability. As far as the leverage goes, I think gross margins will improve over time. It's just going to take a while. And we still have a long way to go on sales and marketing is reducing that as a percentage of revenue. I think G&A has room, and I do think that over time we'll see greater and greater efficiencies with, as they adopt more and more capabilities to build products at a faster rate.

Okay. And then just a quick follow-up. Can you remind us, should there be any, now that we're on a different kind of pricing and packaging model, you know, a little bit more variable, I guess, if you will, you know, but should there be any difference in terms of the seasonality of your revenue ramp or recognition as we move forward with the new packaging model?

So on revenue, I'd say you get a fairly predictable pattern under which revenue is recognized. As I said, most of our revenue in the future periods is designated by our RPO, the committed contracts. But ARR will follow a very typical seasonal pattern, typically. My expectations are more on the enterprise selling basis. Q1 will always be our weakest as far as net new ads as we're adding new territories, adding new reps, implementing new strategic initiatives. This year in particular, we're educating the sales teams on not only the new pricing and packaging, but a lot of the new products we have. So every year we're going to have that, and so it'll be a slow start and then pick up. This year, too, just to remind everybody, we also had some big changes in our sales and marketing leadership, which is predominance of what you see now flowing through and a cost benefit from a lower sales and marketing as a percentage of revenue. And that's from efficiencies we're driving.

Great. Thank you, Jackson. Our next question will come from the line of Scott Berg from Needham, followed by Billy Fitzsimmons. Go ahead, Scott.

Hi, Spencer and Andrew. Nice questions. Thanks for taking my questions. I wanted to follow up on sales enablement that Andrew was chatting about there. We did a couple different customer checks in the quarter, and the one thing that we came back is I don't think your existing customers are quite aware of all the different modules and innovation that you rolled out this year. I see Spencer smiling. I know that's a function of timing, obviously, and one customer didn't even know that you had acquired StatsSig. So, I guess, where are you kind of in that journey? When is the sales force properly ramped in that? I mean, the quarter sales results were good as is, but obviously better awareness there can be even more helpful.

Yeah, to your point, I think a lot of people still bucket us in the analytics company, and it drives me absolutely crazy. Honestly, just sharing, hey, we have Static now, and this is bleeding-edge feature experimentation. and you can use it too and this is the same infrastructure. Open AI runs internally, like awesome. A lot of customers don't even know that. You know, and then same with Wave. You know, I think they're just starting to understand Wave and then same with our other products. I think if you remember from the prior remarks, like we do see ramping. So, you know, we're moving customers from one to two to three to four to five to more products, but it's much slower and that drives me crazy. I think it is, you know, There is no substitute for the work of like, hey, we built something amazing. We have to educate, you know, the hundreds of people we have in our field. And then they have to educate the thousands of customers and market. Like, that's just work. That's just the whole thing. Something I'm spending a lot of time with Nate, our chief commercial officer, as well as the rest of the executive team on in terms of how do we get that and do that more efficiently. We just had a kickoff a few weeks ago where we showed off a lot of what you saw today with StatsAid and Wave and custom agents. But, you know, that's not even to say all the other products we have like session replay and guidance surveys and AI feedback that can displace point solutions. So, anyway, that is, you know, I think last year we said the year was the year of the platform. I think we still have a ways to go on educating people on it. I will say that the good news on it is the main thing customers are looking for is a proof to me you guys are at the bleeding edge of where this field is going. And so my view is that analytics and the whole behavioral data ecosystem is going to go through the same shift that coding has in the last two years. like that is still going to happen. And so they want to, you know, we see it in like a lot of stuff we've been demoing and, you know, our customers see it too. And so they want to know, hey, am I working with the company that's bleeding edge on this? And so even if I'm not necessarily ready to adopt a wave or even a stat tag, you know, I know that, okay, you'll at least help me take the first step to using some of the basics on these capabilities and then I can add more, you know, even if it's maybe too overwhelming for me right at the start or I'm not ready as a company. So, yeah. Anyway, that's all to say we still have a bunch of work to do to make sure our field is equipped. You know, there's definitely areas that do it extremely well, but then there's areas we need to do a better job on, so I appreciate you calling it out.

Thanks for that, Spencer. And then my follow-up question is on the integration traction with Stasic. You all had a pretty aggressive goal, obviously, to move that asset into your organizations. kind of where are you with that because the other customers that we spoke with were super excited about that, you know, a couple more customers, et cetera. So just kind of understand, have you hit all your goals around that, and are you kind of at that point where now you can just deliver on product and sales versus just having to integrate the organization?

Yeah, yeah. So as you imagine, like, StatsLink's been around for five years, and there's a lot of work with – getting it from, you know, a whole group of people who have never seen the code base or sold it or whatever else. I think we've kind of gotten through, you know, there's always stuff, but we've gotten through all of the urgent fires in running and delivering statistics, so that's great. You know, customers are very excited about how it's landing. We want to make sure to give, you know, the fact that It's our main focus as opposed to at OpenAI, it was a little more of a side thing for them. It's all been received positively. So that's good. Now we're starting to think about, okay, what's coming next for Statsig? So if you look at like statsig.com slash updates, we're shipping stuff. We've been shipping stuff for the last few months. We're continuing to build the roadmap. We're continuing to integrate it with amplitude much more tightly so that if you're on both, which a lot of our customers are, you get the benefits of being able to use data from one and the other. And I think a lot of the other thing we're seeing with StatsSig is that there's a lot of demand from AI natives in particular. So one of the reasons we're really excited to join forces with StatsSig is that they like a lot of and many more. tested that in tons of different ways over there, and we're doing the same thing except with everyone outside of OpenAI. And so there's a lot for us to do in terms of how do you set StatFig up to be a core part of the software development harness for all these bleeding-edge AI customers, and it's where kind of everyone wants to go over time. So that's what we're focused on.

Awesome. Thanks for taking my questions. Of course, Scott.

Great. Thank you, Scott. Our next question will come from Billy Fitzsimmons from Piper Sandler, followed by Clark Wright from DA Davidson. Go ahead, Billy.

Hey, guys. Good to see the results and guidance. I think one of the exciting things about StaffSig is potentially the cross-sell opportunity. I know there's some things to do first, but last I looked or last I checked, I think it There were 80 of the 400 Statsig customers around amplitude already, so there's a lot who aren't. Can you just help contextualize for us how we should think about the potential cross-sell opportunity of amplitude into Statsig, or potentially vice versa, and how we should think about that flowing through the model long-term?

I think probably the much bigger opportunity is to take Statsig to amplitude customers. I think Statsig customers, as I mentioned earlier, tend to be much more bleeding edge from an AI innovation standpoint. And so that's where everyone is trying to get their organizations to over the long term. It is a very – it's like a more – Amplitude is historically focused on product management, and then Stastic is much more tailored towards engineers. Like, it has tons of customization. The box, it has, like, all the statistical testing. Now, like I said, those two personas are merging, but, you know, it's early days on that. So I think the opportunity is, as more of our traditional amplitude customers look and try to build, like AI natives introduce AI to their software development process, try to build out a harness, eventually try to get to self-improving products, all of those are opportunities for us to bring stat-stake. Now, we definitely do see places where stat-stake customers are also very interested in amplitude, but there's a lot more, both from a number and ARR basis, that are amplitude-driven.

Perfect. And then if I could ask a second one, can you just contextualize maybe how either your hiring needs have kind of changed here to date or where you're seeing the best ROI from AI-driven efficiencies internally within Amplitude?

Oh, there's a ton. On the hiring front, so a few different things. One, I've been just very focused on transforming the entire workforce, getting leaders, getting engineers, getting people in other functions that are AI-native, both by hiring that talent, acquiring it, You know, hiring executives that have that background. And then in addition to that, retraining and reeducating the workforce that we have here. Great part, like everyone wants to learn. It's like, yeah, you know, people see like, hey, the more I learn how to use AI, the more relevant my skills are going to be both at Amplitude and other places in the future. So everyone's like embracing it, which is great. A few specific areas. That's an always ongoing thing. We were just adding Angela, which we announced today, and marketing. We're always looking at companies and other places to pick up talent. New grads is another great source of very highly leveraged talent. One of the funny things I'll tell you guys is during downturns or whatever, a lot of companies pull back on university hiring because it's the easiest thing to cut, but Thank you so much for joining us. Okay, so that's like the primary thing. And then the one specific area is Statsig. You know, as you imagine, this is a huge, you know, complex product and code base and architecture. And so we've taken our existing experimentation team, and they're now running Statsig, which is awesome, but they also need a lot more help. So we're adding, you know, lots of different roles and hiring on that. Data science leads for deployed engineers. and other engineers who are just familiar with that architecture. We've actually hired one person who used to work at Statsig pre the opening acquisition and we're continuing to go more there. So there's a lot we need to do there. We've kind of caught the ball, which is good, but now we have to like go maximize it.

Great to see. Thanks, guys.

All right. Thank you, Billy. Our next question will come from Clark Wright from D.A. Davidson, followed by Koji Akita from Bank of America. Clark, go ahead.

Thank you. It was great to see the 30% year-over-year increase in customers with over 100K in ARR, which looks to be the highest since 2021.

Can you potentially break out the ads from Statsig, and what else is helping in terms of the new logo momentum that you're seeing today?

Sure. So about 40 customers came from the Spatswood business itself that we added. And so if you've got to do the quick math on that, you're still well in almost 23%, 24% growth in customers that are in that greater than 100K cohort. And so it's still growing quite nicely and contributing to ARR and to revenue growth. So that was really good. And as Spencer mentioned earlier, what we're seeing back when we're talking to customers, especially as we've gotten introduced to them for the first time, if they're brand new customers to Amplitude, they were formerly Static customers, is we're finding that they're, one, very appreciative of the fact that Amplitude is shepherding and taking forward the roadmap. and showing confidence in our ability to actually give them a future where self-improving products is a reality. And they do that through adopting an experimentation mindset, and they're very confident then to move further with amplitude in other areas. So that cross-sell expansion opportunity is real. I think we talked about it at the time. It was a multi-hundred-million-dollar opportunity for us just in the install base, so we're pretty excited about it.

And then last, could you call that event volume growth being 21% year over year?

What is that now as you kind of talk about the momentum that you're seeing in all-time highs? And how should we think about the ramp of that metric going forward given a genetic workflows and the amount of events that they can process?

Yeah, it's definitely growing faster than both ARR and revenue. And it's one of those areas that for us, it feels like we've gone through many, many quarters of trying to bring it up and get the entitlements right size and everything else. It's definitely a leading indicator for us that, one, we're not going to have the same types of churn issues like in the past. Two, sales has adopted that value-based orientation sale where they're not trying to get everything up front. They're trying to get our customers to value quickly and show them the value of an expansion. And like I said, it's an indicator that we're going to see upsells have a larger meaningful contribution to growth. Whereas before it was a detractor and the predominance of our gross with cost sell, we're just not going to have those same instances if we've got customers who are bumping up against their entitlements and getting value from the investment they've made. Got it. Thank you.

Great. Thank you, Cork. Our next question will come from Koji Akita from Bank of America, followed by Nick Altman. Go ahead, Koji.

Yep. Thank you. Thanks, guys. Thanks so much.

I wanted to ask a question on Wave. Love the demo, long-term vision. I mean, it sounds like it's going to be awesome for finding problems and finding solutions, generating code, measuring outcomes. I mean, it looks like the whole deal here. And so the question really becomes, if Wave is successful in all the things I think it could be, then why would you need the other products from Amplitude like StatSig and Product Analytics? Seems like you can do it all from Wave. Yeah, yeah, totally. Okay, so, yeah, this is a brush over this architecturally. What Wave does is it takes data from lots of different data sources. So it takes analytics data from amplitude, experiment data from Statsig, where eventually we're planning to make it agnostic long term so it can take data from any analytics thing. If you're using Google Analytics or Adobe or something else, it doesn't matter. and then translate that insight. So you still need a place to get that data. Like it's not like it can just look at a product and figure out what people are doing it. It actually needs to have that data from some area. And so it's a nice build where it's like, hey, use amplitude, use static. The more data sources you put into this thing, the better the output. Thank you for joining us. as well as the experimentation and everything else we have played a really important part in being the collection points for that data. Again, though, you know, the goal is to be agnostic so we can just plug into whatever system, you know, your data warehouse, your own internal thing, you know, other tools, third-party tools, and kind of build on top of that. I think another thing is that because we have that data, that gives us the ability to have much greater insight into the right things to build. If you're a startup starting out for the first time and you don't have the massive multiple petabyte data set that we have, it's like, okay, how do you even know if what you're recommending is best practice or what leads to something good? And so there's a lot of feedback loops that we have. Because we have this data set, we know, okay, hey, here's what a great e-commerce app looks like. Here's what a great social media app looks like. Here's what a fintech app should look like. Here's the typical workflows for sign-up that work well. Here's what message customization should be, and so on. And so because we're one of the few companies out there, there's no open source equivalent data sets for it. And so having that allows us to develop a much higher quality, better version of Wave than kind of anyone else out there. Thank you for joining us today. So I'm convinced that whoever wins this space, that's going to be a multibillion-dollar business, if not more. And so our thing is, like, let's run forward with that as fast as possible. I think we're well-positioned in the opportunity because we're the leader in analytics and a few other areas. Yeah, and, you know, let's go build that business as quickly as we can. Got it. Thanks, Spencer. All from me. Thank you so much. Of course, Koji.

Thank you, Koji. Our next question comes from Nick Altman from BTIG, followed by YC Wong from Citi. Go ahead, Nick.

Hey, awesome. Thanks, guys. Just to build off Cody's last question, I kind of wanted to ask the inverse on Wave. It seems like there's more incentive to adopt the broader platform with Wave. And I know it's still very early, but How are those kind of conversations going with customers? Like, are you having more sort of multi-product or platform adoption customers as they kind of, you know, look at Wave and this vision of the self-improving product? And then the follow-up there is just how should we think about Wave being monetized more so in the near term? Is it kind of indirectly in the sense of, you know, it gives customers – and more incentive to adopt the broader platform. And that's how you sort of plan to monetize it or is kind of a standalone skew?

Yeah, so you're exactly right, which is Thank you for joining us today. has a lot of value for how it can be better. So that's been awesome to see. And, you know, again, early, you know, there's a handful of customers on it, but as we grow it out, I think that'll drive more adoption. And I also don't think, like, you know, to my point earlier to Koji, it's like, you know, the goal is to be agnostic with it. We want to build the most leading-edge thing, and so if we plug in other sources, too, all the better. On the monetization front, we'll charge for it. We absolutely will charge for it. I mean, you think about the value that this creates. Now you go from analytics or data tooling where it's like you have to manually go in collect an event or look at, ask a particular question, get a result out, think about how to apply that to business. And now you're having a whole flow that does it for you. Hey, I've already seen this user is having friction here like that. The docs example I made is like, hey, we see most search queries are failing. Why is that? Well, they're single characters and we're not waiting until someone types a complete word. So they get this error when they're in the middle of the typing and it feels bad and it's like, you know, duh, okay, yeah, you should resolve that and make that better. And it's not just that, it's like that times, you know, hundreds of things all across all surface areas of your product. One of the lessons is that, like, Behavioral data and product surface areas are so large, it is impossible for any team to stay on top of them. And so the fact that this thing is looking all the time for how it can be better is magical. It's crazy what it can do. So I think whatever company goes to win that is going to be multiple billions in revenue, if not more, and we want to aggressively go after it. Yes, customers are willing to pay for that. Now, again, early days, we're in alpha, so we haven't figured out exactly how we're going to monetize it, but we absolutely will charge for that capability. That's like That's one of the great, you know, people are talking about, hey, there's all this money going into AI. Where does it actually come out? And this is one where you can draw the line really directly. It's like, look, the customer experience is getting better. There's spending more. There's more revenue. There's less friction. There's less downtime. Like, the whole thing is better. Like, great use from an application standpoint.

Great. Thank you so much.

For sure.

Thank you, Nick. Our next question will come from Y.C. Wong from Citi, followed by Arjun Bhatia from William Blair. Go ahead, Y.C., your line's open.

Hey, great listening. Thanks for the question here. Spenser and team, like, great to see the fast-expanding AI platform here you have, like, every quarter. Like, I want to touch on agent analytics, which now seems to measure as well. I love it. I love it. Agent themselves, right? I mean, where the market that we see is already multiple vendors out there trying to measure prompts, measure latency, hallucination, like all the stuff that you can see. But what is the customer problems that does the agent analytics could solve that the current observability platform cannot? And then how do you view the market opportunity of that problem?

Yeah. So, I mean, I think first to the extent this replaces most traditional interfaces, then, you know, the market opportunity is not as large, not larger than what's going on on traditional user interfaces with session replay and analytics. In terms of our unique positioning, what we offer, which I shared a little bit in the customer story about Economist, is that you can connect with individually happening within a session to the long-term impact of your business. So you can say, okay, hey, you got a successful cancer back from the bot. Did that lead to you spending more or signing up or keeping your subscription? Conversely, if you ran into a problem and you got frustrated, did that lead to some negative long-term outcome? and that loop is really, really important. Most, a lot of the engineering-specific observability products we've seen in this space are just kind of standalone. It's like, okay, they'll just show the traces and that's kind of it. And you have no idea if it's actually leading to different results down the line. And so that's why we see both like traditional like enterprises that are transforming their businesses like The Economist as well as a lot of AI natives. You know, I mentioned one of the largest foundational model companies They also are looking at, like, you know, as you imagine, they'll have a lot of tooling there, but they want to know, okay, is this leading to someone to become a subscriber, to upselling, all of that sort of stuff long-term. And so being able to connect that journey end-to-end is what we uniquely offer.

That sounds like a more time-extension opportunity there. Oh, absolutely, absolutely.

Yeah, I didn't cover as much today. We demoed it more on the Q1 earnings call. But yeah, it's actually one of the things my chief commercial officer and I are very excited about.

Yeah, definitely look forward to hearing more, including Wave. I have a quick follow-up for Andrew as well on the guidance. Like, application growth has definitely been accelerating for the past year and more, right? Even adjusting for the static business this quarter, I think it's still accelerated. But the implied guide that I'm looking for Q4 earnings What I would tell you is that we always take a look at what we're building our guidance, what we believe is, you know, very strong currents to occur.

And I mentioned some of the factors earlier, the pipeline, how well that pipeline is developed, where we're seeing good demand from our customers. Usually Q4 is our strongest quarter from a new ARR perspective, and it's because that's the way we built our comp plans. That's the way enterprise selling cycles run, typically in a calendar-based company. So I would just tell you that our guidance is based upon what we know is out there as far as our pipelines, our RPO, and it's what we're comfortable with.

Go ahead. Congrats, guys. Thank you.

Thank you, Y.C. And our last question will come from the line of Arjun Bhatia of William Blair by Willow Miller. Willow, your line is up.

Hey, team. Thanks for taking our question. Can we hear your updated thoughts on the 20% plus revenue growth target given the strong growth this quarter and the strong third quarter, guys? I'm curious to hear how you're thinking about it now considering Static and NowWave.

Oh yeah, I mean, I think Statsig is an accelerant to our long-term plans, which is part of why we, the J&I agreed amplitude would be the best home for Statsig long-term. You know, I think the, so we put up 19 million in organic growth last quarter in Q2, and so, you know, it's just, we're just touching on that 20%, you know, it's like the annual number is 410, so if you divide that out, it's like, you know, we're just shy of that 20% growth target when you annualize the Great to hear. Thank you.

Thank you, Will. That will conclude our second quarter earnings call. Thank you for your time and interest. We look forward to seeing you this quarter on the road as we attend conferences hosted by KeyBank, Citi, and Piper Sandler. Thank you.

Thank you all.