Operator: Good afternoon, everyone, and welcome to Amplitude's Second Quarter 2026 Earnings Conference Call.

John Lewis Streppa: I am John Lewis Streppa, head of investor relations, and joining me today are Spenser Skates, CEO and cofounder 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 overall future prospects. 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. You are cautioned not to place undue reliance on these forward-looking statements, and we assume no obligation to update these statements after today's call except as required by law. Certain financial measures used on today's call are expressed on a non GAAP basis. We use these non GAAP financial measures internally to facilitate analysis of our financial and business trends and for internal planning and forecasting purposes. These non GAAP financial measures have limitations and should not be used in isolation from or as a substitute for financial information prepared in accordance with GAAP. Additional information regarding these non GAAP financial measures and a reconciliation between these GAAP and non GAAP financial measures are included in our earnings press release and the supplemental financial information, which can be found on our Investor Relations website. At investors.amplitude.com. And with that, I will hand the call over to Spenser.

Spenser Skates: Thanks, John, and good afternoon, everyone. Welcome to Amplitude's second quarter 2026 earnings call. Today, I will cover 3 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 2 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 $100 thousand 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 are 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 have made that transformation at Amplitude over the last 2 years and now our customers are looking to learn from us. Becoming an AI company starts with the organization. 2 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. Gabe, 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 plus and Java and building databases. Most recently, we added Angela Ferranti as SVP of marketing. Angela founded Lovable, 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 are continually reeducating everyone at Amplitude through initiatives like AI Week, unlimited token spend, and a living token leaderboard. This has all resulted in 3x the number of pull requests in 6 months. We have 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 have leveraged AI to shorten our closing process by a day. We built customer health dashboards that enable our sellers and leaders to track customer usage, bring 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 to deploying at scale. More than 40 AI native companies now pay us over $100 thousand a year. Those customers include Harvey, Midjourney, Character AI, and 1 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 have improved our pricing and packaging. We reduced down to a 1-meter to make it simpler for enterprises to add additional products. We increase the amount of data on our free plan so we are the best for those just getting started. Amplitude has the best pricing whether you are a startup or a large enterprise. 1 of the biggest changes with building an AI native company we are 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 as op in operating income as we have planned. I am continuing to drive Amplitude to a 20%+ operating margin business over the long term. We offer 3 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 is also integrated natively with data warehouses. Wade is the future of product development, self improving products where we automatically recommend what to build based on signals from users. While we are early here, I am actually excited to show you a demo today. Together, these 3 products close the product development loop. Understand what is happening, measure what ships, 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 is 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 to discovery rates are improving by 1 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 a demo, I wanna show you custom agents Statsig, and Wade. 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 will 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 in 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 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. Statsig runs experiments natively on your cloud data warehouse, whether that Snowflake, BigQuery, Databricks, or Redshift. Let me show you what this looks like. Here is the results page for 1 of hundreds of experiments that an ecommerce customer is running. This experiment is testing a larger product image versus the default size. there is 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 the experiment is healthy or not. We expect to see a 50-50 split. So we are doing good. And as you can see over here, we are getting a healthy check. We move to the scorecard that has the results. This has a confidence interval of 95%. Statsig uses advanced techniques like CUPID and sequential testing that allows engineers to speed up time to decision. We have those turned on. In monitoring, we see specific events we are tracking for this experiment. We are 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. Statsig has a variety of advanced experimentation 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 Wade the future of product development. Wade allows for self improving products that automatically recommend what to build next based on signals from your users. Wade is magical. Wade 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 am going to walk you through a real example WAVE suggested and built for Amplitude's documentation site. On our documentation site, Wade 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 results state before the person finished typing their search, leading to a bad experience for users. Wade explains the reach of this issue. Every user who uses search, it has an impact expected impact of decreasing total search failures by 80%. Then Wave has automatically created a visual example of the problem below so it is easy to understand. It also has a full explanation of the evidence. For the plan, WAVE sketches a wireframe of the recommended update. Setting a 3-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, Wade measures the results of the change. There is a massive decrease in total search failures. 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 3 stories about how these customers are leveraging our platform. First is Coca Cola FEMSA. 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 1 knew if it was going to work. They used amplitude to find out. Their AI campaigns actually had an 11% click-through rate, 4x 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 went from a 2.5 thousand-store pilot to 690 thousand 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 have 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 AMP. Amplitude. This is the next generation of product development at work. Third is the economist. The Economist is a print publication that is 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 could not keep up. 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 have transformed Amplitude to be AI native, and we are building the future on what can be done in analytics. Self improving products are closer than ever with Wade. Our pace of innovation continues to accelerate, and we are building in a way that can scale with leverage I am extraordinarily excited about what is ahead. With that, I will hand it over to Andrew to walk you through the financials.

Andrew Casey: 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 Statsig, 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 and customers, managing through our own AI native evolution, and implementing our new pricing and packaging strategy. AI is changing how customers use Amplitude. More our customers bill 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 platform and makes it more likely that they will 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 multiproduct attach, 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 1 point versus Q1, due to the integration of STAT SIG. 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 Statsig business. We are 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 2 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 have 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 we will continue to invest in R&D to drive innovation. We are instrumenting our business to accelerate growth. Capture market share, and show leverage. 1 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. Have increased the durability of our business through our RPO growth and reinvented our internal 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 will 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 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 5 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 pro form a basis includes Statsig and Amplitude customers. Gross margin was 71% for the second quarter, down approximately 4 points from the second quarter of last year and down 4 points sequentially. This was driven by continued growth in inference costs as customer adoption our AI tools accelerated along with the integration of the Statsig business and its hosting environment. Sales and marketing expenses were 39% of revenue. Down from 44% in the second quarter of last year. G and a was 13% of revenue, down 1 point from the second quarter of last year. R and D was 21% of revenue, up approximately 3 points from the second quarter last year, reflecting investment to scale the Statsig 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 -$0.01 based on 129.4 million basic shares compared to $0.01 a year ago. Free 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 $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 road map when appropriate. Now turning to our outlook. As a reminder, the philosophy of how we set guidance is through the lens of execution. Are pleased with our overall progress on consolidating point solutions to our core platform and the adoption of our different AI technologies. We have instrumented our business and selling 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 26, 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 shares outstanding of approximately 133 million as measured on fully diluted basis. For the full-year 2026, we are raising our expectation for full year revenue based on the performances in second quarter to be between $407.2 million 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 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 of approximately 137.1 million as measured on a fully diluted basis. In closing, we are accelerating our pace of innovation, and we are 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, 'll open up for Q&A. Over to you, John.

John Lewis Streppa: Thank you, Andrew. We are going to Q&A. For the sake of time, please limit yourself to 1 question and 1 follow-up.

Operator: Our first question today will come from the line of Mark Cash from Raymond James. Followed by Jackson Ader from KeyBanc. Mark, your line is now open.

Analyst: Thanks, John. Yeah. If I could start with Spenser. I really wanted to ask around Wade. I appreciate it is still limited beta. I think you have been using internally for several months now. Yep. I guess, do you see Wade that it could cause maybe a shift-- a company shifting away from using bespoke agents for specific use cases towards a broader AI native product development platform from what you are seeing And if so, how could that change your buyer, maybe the budgets you see and the address market over time?

Spenser Skates: When you say bespoke, like, say more on that. Like, Instead of using particular agents to do a specific task underlying because you have, like, a lot of agents doing things underneath for what you so I see what you are 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 Wade is doing in particular, to your point, is it is kind of-- it is looking at all your data all the time and then saying, hey. Here are points of friction. Here's something that is 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 are not doing. And so it is operating at a kind of higher level. In terms of the persona, I think we are seeing is a convergence between engineers, product managers, and designers into this AI builder persona. it is not really, like, you have engineers who are thinking about what to build, and you have product managers who are also just shipping code. And so the best you know, if you look at where the AI native teams that everyone's aspiring to be, these roles are melding. So it is still the same problem we are solving, which is how do we help you build a better product, but we are just automating more of it because we are saying, hey. We are gonna look at all the data all the time and then suggest recommendations. That like, I have been-- we have been talking about self improving products here at Amplitude for about 9 years. And so I am actually been blown away by what is possible with the technology today where it is just it is the perfect problem for AI in a lot of ways. The datasets are massive and complex. So you cannot get any human to look at them. And then the synthesis of okay. Here's what I think could be better and best practices is actually like, extraordinarily impressive. And so what that means is that just by the fact that someone is using your software, like, it is getting better because it is just translating recommendations. You no longer need someone to go into amplitude or to any data system and say, oh, here's what my interpretation of these results. So I do think, you know, 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 are 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 did I hit on what you are looking for?

Analyst: Yeah. Absolutely. Thank you for that. If I could follow-up with Andrew real quick. If my math is correct, the guidance for the year was raised by more than 2x the beat for revenue and operating income. So I was wondering if you could just go through the key drivers of lifting growth expectations why you-- it saw some pressure on pro forma expansion there in the quarter? And then what you consider regarding margin leverage-- the levers while you are facing COGS pressure and ramping token spend internally? Thank you.

Andrew Casey: Yeah. Sure. So a couple things. 1, that when we look at our ability to actually generate revenue in the out quarters, 1, we start with the strong balances we are we are booking that are showing up in our RPO. Now when you have got commitments from customers for a longer term duration, you start to have better and better predictability about your future revenue. So that is the first thing. it is 1 of the reasons why we emphasize that so much. The second thing is we look at how much our customers are actually responding to some of the initiatives we are putting out in. And that comes in the form of our new product capabilities, our new pricing, new packaging, areas where our sales team is running new promotions and activities. All those are all 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 that we will add more and more in net 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 is the baseline? What can you see in your pipeline? What you expect it can convert is what I what I refer to as our ability to go execute against the plans that are in front of us. And the sales team's been doing a really good job driving consolidation in the market, and that alone with our products is driving great conversions. So that is the first thing. On some of the margin areas, would tell you, look. We just, in the case of, the Google environment that we got for Statsig, we are going to be focused on driving optimizations in that environment over a period of time. it is definitely lower. it is-- we said in the low 50s. From a gross margin perspective. That comes from us taking on a whole new environment. You know, most of Amplitude, all of it, in fact, is on, AWS. So we took on a whole new cloud and hosting environment and know, you have to go through the paces of really optimizing how you run those environments for customers. Our first objective was integrating, making sure there were no disruption of service Now we are moving quickly into how we can optimize those environments. So that is 1 big lever on the gross margin side. And we are constantly looking at how we can make investments to go drive greater efficiencies across all of our, our operating expense areas.

John Lewis Streppa: Brent. Thank you, Mark.

Operator: Our next question will come from the line of Jackson Ader from KeyBanc. Followed by Scott Berg. Go ahead, Jackson.

Jackson Ader: Hey. Thanks, guys. Good to see you. I was curious on I guess, Andrew, kinda sticking with you and talking about rather than on the cloud side, just on the operating expense side. We have seen really nice acceleration in organic ARR. You know, from the business. But you know, if I take kind of a longer term view, even on a non GAAP basis, we are still around breakeven. Right? And so I am curious as you are thinking about, like, driving more leverage and more incremental margin that you talk about before on the income statement, what kind of impact should we expect that to have on the organic growth number if at all?

Andrew Casey: Well, I would tell you that, 1, we are still we are still expect from an organic perspective, we got a great set of products, Spenser 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 would be the predominance where we will 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 will be able to drive better and better gross cost of start revenue and, say, increase gross margins over time. It just takes time to go do those things, especially when you are seeing such a demand inflection from customers and increasing data lines. As I mentioned, we are at an all time high for the amount of data ingestion in the platform versus entitlements When I first joined, it was in the low 60s. We are in 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 you know, usually where we have had a lot of problems in the past of overselling and how to do right size contracts. The first time we are past those things, and we are 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. Far as the leverage goes, I think gross margins will improve over time. it is just gonna 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 and A has room, and I do think that over time, we will see greater and greater efficiencies with the R&D organization as they adopt more and more capabilities to build products at a faster rate.

Jackson Ader: Okay. And then just a quick follow-up. Can you remind us, should there be any now that we are on a different kind of pricing 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 your revenue ramp or recognition as we as we move forward with the new packaging?

Andrew Casey: So on revenue, I would say, you get a fairly predictable pattern under which revenue is recognized. Because as said, most of our 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 My expectation is a bit more on the enterprise selling basis. Q1 will always be our weakest as far as net new ARR ads because we are adding new territories, adding new reps, implementing new strategic initiatives. This year, in particular, we are educating the sales teams on not only the new pricing and packaging, but a lot of the new products we have. So every year, you are gonna have that, and so it will be a slow start and then pick up. This year in 2, 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 is that is from efficiencies we are driving.

John Lewis Streppa: Brent. Thank you, Jackson.

Operator: Our next question will come from the line of Scott Berg from Needham followed by William Fitzsimmons. Go ahead, Scott.

Scott Berg: Hi, Spenser and Andrew. Nice quarter. 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 1 thing that we came back is I do not think your existing customers are quite aware of all the different module modules and innovation that you have rolled out this year. Yeah. Totally. I see Spenser smiling. Is I know that is a function of time, obviously, and 1 customer did not even know that you had acquired Statsig. So I guess, where are you kind of in that journey? Where do you where do you when is the properly ramped in that? I mean, the quarter sales results were good as is, but obviously, better, better awareness there can be even more helpful.

Spenser Skates: Yeah. To your point, I think a lot of people still bucket us in the analytics company, and it drives me absolutely crazy. I honestly just sharing, hey, we have Statsig now, and this is bleeding-edge feature experimentation. And you can use it too, and this is the same infrastructure OpenAI runs internally. Like, awesome. A lot of customers do not even know that. You know? And then same with Wade. You know? I think just starting to understand Wade and then same with our other products. I think if you remember from the prepared remarks, like, do see ramping. So, know, we are moving customers from 1 to 2 to 3 to 4 to 5 to more products, but it is much slower, and that drives me crazy. I think it is, you know, there is no substitute for the work of, 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 in market. Like, that is just work. that is just the whole thing. Something I am spending a lot of time with Nate, our chief commercial officer, 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 to kick off a few weeks ago where we showed off a lot of what you saw today with Statsig and Wade and custom agents. But, you know, that is not even to say if the other products we have, like session replay and guides and surveys and AI feedback that can displace point solutions. Anyway, that is I think last year, said the year of the plat-- it 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 data behavioral data ecosystem is gonna go through the same shift that coding has in the last 2 years. Like, that is still gonna happen. And so they wanna you know, they we see it in, like, a lot of stuff we have been demoing and, you know, our customers see it too. And so they wanna know, hey, am I working with the company that is bleeding edge on this? And so even if they are not necessarily ready to adopt a WAVE or even a Statsig, I know that, okay. You at least help me take the first step to using some of the basic on these capabilities, and then I can add more, you know, even if it is maybe too overwhelming for me right at the start or I am I am not ready as a as a company. So anyway, that is all to say. We still have a bunch of work to do to make sure our field is equipped. You know, there is definitely areas that do it extremely well, but then there is areas we need to do a better job on this. So appreciate you calling that out.

Scott Berg: Thanks for that, Spenser. And then from my follow-up question is on, the integration traction with Statsig. You all had a pretty aggressive goal obviously, to move that asset into your organizations. Kinda where are you with it? Because the other customers that we spoke with were super excited about that. You know, couple of them already, you know, SaaS customers, etcetera. So just kinda 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?

Spenser Skates: Yeah. So as you imagine, like, Statsig has been around for 5 years, and there is a lot of work with getting it from, you know, a whole group of people who have never seen the codebase or sold it or whatever else. I think we have kind of gotten through you know, there is always stuff, but you have we have gotten through all of the urgent fires. In running and delivering Statsig. So that is great. You know, customers are very excited about how it is landing. We wanna make sure to give you know, the fact that it is our main focus as opposed to at OpenAI AI, it was a little more of a side thing for them. You know, it is it is all been received positively. that is good. Now we are starting to think about, okay. what is coming next for Statsig. So if you look at, like, statsig.com/updates, we are shipping stuff. We have been shipping stuff for the last, few months. We are continuing to build in the road map. We are continuing to integrate it with Amplitude much more tightly so that if you are on both, which a lot of our customers are, you get the benefits of being able to use data from 1 and the other. And I think a lot of the other thing we are seeing with Statsig is that there is a lot of demand from AI natives in particular. So 1 of the reasons we are really excited to join forces with Statsig is that they-- like, a lot of the way future product development is being run like, people are choosing Statsig for that. So it is engineering first teams that tend to be much more technical. They are building out whole software development harnesses. They wanna manage how stuff is deployed in that harness. And Statsig is set up really, really well to scale. You know, as I mentioned, OpenAI runs a version of that infrastructure internally for themselves. And so, you know, they have tested that, you know, in tons of different ways over there, and, you know, we are doing the same thing, with everyone outside of OpenAI. And so there is a lot there is a lot for us to do in terms of how do you set Statsig up to be a core part of the software development harness for all these bleeding edge AI customers, and it is where kind of everyone wants to go over time. So that is what we are focused on.

Scott Berg: Awesome. Thanks for taking my questions.

John Lewis Streppa: Of course, Scott. Brent. Thank you, Scott.

Operator: Our next question will come from William Fitzsimmons from Piper Sandler followed by Clark Wright from D. A. Davidson. Go ahead, Billy.

Billy Fitzsimmons: Hey, guys. Good to see the results and guidance. I think 1 of the exciting things about Statsig is potentially the cross sell opportunity. I know there are some things to do first, but last I looked or last I checked, I think there were 80 of the 400 Statsig customers are on Amplitude already, so there is there is a lot who are not. Can you just help contextualize for us how we should think about the potential cross sell opportunity amplitude into Statsig or potentially vice versa in how we should think about that long through the model long term?

Spenser Skates: 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 is where everyone is trying to get their organizations to over the long term. It is a very it is like a more Amplitude is historically focused on product management, and then Statsig is much more tailored towards engineers. Like, has tons of customization. Out of the box. It has, like, all the statistical testing. Now, like I said, those 2 personas are merging, but, you know, it is it is it is 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, you know, AI to their software development process, try to build out a harness, eventually try to get to self improving products, that all of those are opportunities for us to bring Statsig. Now we definitely do see places where Statsig customers are also very interested in Amplitude. But, you know, it is-- there is a lot more both from a number and ARR basis that are Amplitude.

Billy Fitzsimmons: Perfect. And then if I can ask a second 1, can you just contextualize maybe how either your hiring needs have kind of changed year to date or where you are seeing the best ROI from AI driven efficiencies internally within Amplitude?

Spenser Skates: Oh, there is there is a ton. On the hiring front, so a few different things. 1, like, it is been-- I have been just very focused on transforming the entire workforce, getting leaders, getting engineers, getting people in other functions that are AI native both by like, hiring that talent, acquiring it, know, hiring executives that have that background. And then in addition to that, retraining and re-educating the workforce that we have here. Like, everyone wants to learn. it is like, yeah. You know, people see, like, hey. The more I can learn how to use AI, the more relevant my skills are gonna be both at Amplitude and other places in the future. So everyone's, like, embracing it, which is great. The few specific areas, I think on yeah, so that is, like, an always ongoing thing. Like, I just-- we were just adding Angela, which we announced today, in marketing. You know, we are always looking at companies and other places to pick up talent. is another great source of very highly leveraged talent. 1 of the funny things I will tell you guys during, you know, downturns or whatever, a lot of companies pull back on university hiring. But if because it is, like, the easiest thing to cut. But if you have the confidence to evaluate who is great from that talent pool, you can get some exceptional folks right out of school, which is awesome. So we have been had that as a big focus here at Amplitude. So that is, like, the primary thing. And then the 1 specific area is Statsig. You know, as you imagine, this is a huge, you know, complex product and code base and architecture. And so our we have taken our existing experimentation team, and they are now running Statsig, which is awesome. But they also need a lot more help, so we are adding, you know, lots of different roles and hiring on that data science leads, or deployed engineers. You know, other engineers who are just familiar with that architecture. We have actually hired 1 person who used to work at Statsig, pre the opening acquisition, and we are continuing to go more there. So there is there is a lot we need to do there. We have kind of I kinda caught the ball, which is good, but now we have to, like, go maximize it.

Billy Fitzsimmons: Brent to see. Thanks, guys.

John Lewis Streppa: Alright. Thank you, Billy.

Operator: Our next question will come from Clark Wright from DA Davidson followed by Koji Ikeda from Bank of America. Clark, go ahead.

Clark Wright: Thank you. It was great to see the 30% year over year increase in with over 100 ks in ARR, which looks to be the highest in 2021. Could you potentially break out the adds from Statsig? And what else is helping in terms of the new logo momentum that you are seeing today?

Andrew Casey: Sure. So about 40 customers came from the Statsig business itself that we added. And so if you got to do the quick math on that, you are still well in, almost 23, 24% growth in customers that are in that greater than a $100 thousand cohort. And so it is still growing quite nicely and contributing to ARR to revenue growth. So that was really good. And as Spenser mentioned earlier, what we are seeing back when we are talking with customers especially as we have gotten introduced to them for the first time, if they are brand new customers to Amplitude, were formerly Statsig customers, is we are finding that they are, 1, very appreciative of the fact that Amplitude is shepherding and taking forward the road map 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 a an experimentation mindset, and they are 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. There was a multi-hundred-million-dollar opportunity for us just in the install base. So we are pretty excited about it.

Clark Wright: Got it. And then last quarter, you called out event volume growth being 21%. Year over year. What is that now as you kind of talk about the momentum that you are seeing in all time highs? And how should we think about the ramp of that going forward given agentic workflows and the amount of events that they can process?

Andrew Casey: Yeah. it is it is definitely growing faster than both ARR and revenue, and it is 1 of those areas that for us, feels like we have gone through many, many quarters of trying to bring it up and get the entitlements right sized and everything else. it is definitely a leading indicator for us that, you know, 1, we are not gonna have the same types of churn issues like in the past. 2, sales has adopted that value based orientation sale where they are not trying to get everything up upfront. They are trying to get our customers to value quickly and show them a value of an expansion. And like I said, it is it is a it is an indicator that we are gonna see upsells have a larger, meaningful contribution to growth Whereas before, it was a detractor and the predominance of our growth with cross sell. We are just not gonna have those same instances if we have got customers who are bumping up against their entitlements. And getting value from the investment they have made.

Clark Wright: Got it. Thank you.

John Lewis Streppa: Brent. Thank you, Clark.

Operator: Our next question will come from Koji Ikeda from Bank of America followed by Nicholas Altmann. Go ahead, Koji.

Koji Ikeda: Yep. Thank you. Thanks, guys. Thanks so much. I wanted to ask a question on Wade. You know, love the demo. Long term vision. I mean, it sounds like it is gonna be awesome for finding problems and you know, finding solutions, generating code, measuring outcomes. I mean, it looks like the full deal here. And so the question really becomes, if Wade 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?

Analyst: Seems like you could do it all from.

Spenser Skates: Yeah. Totally. Totally. Okay. So, yeah, this is I brushed over this architecturally. What Wade does is it takes data from lots of different data sources. So it takes analytics data from Amplitude, experiment data, from Statsig. We are eventually we are planning to make it agnostic long term so it can take data from any analytics thing if you are using Google Analytics or Adobe or something else. It does not matter. And then translate that insight. So you still need a place to get that data. Like, it is 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 is a nice build where like, hey. Use Amplitude. Use Statsig. The more data sources you put into this thing, the better the output. That you see. 1 of the big learnings from the AI boom is that the power of massive scale of data is just gets you better and more accurate more insightful results. Like, that is just a straight you know, that like, you can see look. The scaling laws look like you can you can grow that almost infinitely. So Amplitude Analytics actually as well as the experimentation and everything else we have play a really important part in being the collection points for that data. Again, though, you know, goal is to be agnostic so we can just plug into whatever system, you know, data warehouse, your own internal thing, you know, other tools, third party tools, kinda build it 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 are a startup starting out for the first time and you do not have the massive, you know, multiple petabyte dataset that we have, it is like, okay. How do you even know if what you are recommending is best practice or what leads to something good? And so there is a lot of feedback loops that we have because we have this dataset. We know, okay. Hey. Here's what a great ecommerce app looks like. Here's what a great social media app looks like. Here's what, you know, you know, a fintech app should look like. Here's the, you know, typical workflows for sign up that work well. Here's what message customization should be so and so on. And so because, like, we are 1 of the few companies out there, there is no open source equivalent datasets for it. And so having that allows us to develop a much higher quality, better version of Wade than kinda anyone else out there. So, the other good part is it is not like a you know, it is an alpha, so there are customers using it. it is not using it internally. there is a number of startups. there is a few enterprises that are using it. And so it is it is spinning out real things that you know, frankly, you look at this, and you are just like, holy shit. How did AI come up with this? This is crazy. I am convinced that whoever wins this space, that is gonna 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 are well positioned in the opportunity because we are the leader in analytics and a few other areas. Yeah. And, you know, let's let's go let's go build that business as quickly as we can.

Koji Ikeda: Got it. Thanks, Spenser. All from me.

John Lewis Streppa: Thank you so much. Of course, Koji. Thank you, Koji.

Operator: Our next question comes from Nicholas Altmann from BTIG. Followed by YC Wong from Citi. Go ahead, Nick.

Nicholas Altmann: Hey. Awesome. Thanks, guys. Just to build off, Koji's last question, I kind of wanted to ask the inverse on Wade of, like, it seems like there is more incentive to adopt the broader platform with Wade. Exactly. And I know it is 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 Wade and this vision of the self improving product. And then the follow-up there is just how should we think about Wade being monetized more so in the near term? Is it kind of indirectly in the sense of it gives customers more incentive to adopt the broader platform, and that is how you sort of plan to monetize it or is it kind of a standalone SKU?

Spenser Skates: Yeah. So I you are exactly right, which is the more data sources you feed to this thing, the better. And so we have already I have already seen multiple customers who have gotten on session replay. As well as 1 that signed up for AI feedback specifically because, hey. The stuff that the wave makes it a lot better. And you are absolutely right where, like, it drives, like, the whole platform play or it is like, okay. You have all these individual point things, and then you just they are more data sources. Session replay in particular is very, very powerful. Like, as you imagine, viewing the exact state of UI and where a user clicked is has a lot of value for how it can be better. So that is been that is been awesome to see. And, you know, again, early, you know, there is there is you know, handful customers on it. But, as we grow it out, I think that will that will drive more adoption. And I also do not think, like you know, to my point earlier to Koji, it is like, you know, our goal is to be agnostic with it. We wanna build the most bleeding edge thing. And so if we plug in other sources too, all the better. On the monetization front, we are we are you know, we will 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 is 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 business. And now you are having a whole flow that does it for you. Hey. I have 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 are single characters. And we are not waiting till someone types a complete word, so they get this error when in the middle of the typing, that feels bad. And it is like, you know, duh. Okay. Yeah. You should resolve that and make that better. And it is not just that. it is like that times hundreds of things all across all surface areas of your product. 1 of the lessons is like, behavioral data and product surface areas are so large, it is impossible for a 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 this just it is magical. Like, it is crazy what it can do. So I think whatever company goes to win that is gonna be multiple billions in revenue, if not more, and we wanna aggressively go And, yes, customers are willing to pay for that. Now, you know, again, early days, we are in alpha, you know, so we have not figured out exactly how we are gonna monetize it, but we absolutely will charge for that capability. that is, like, that is 1 of the great, you know, people are talking about, hey. there is all this money going to AI. Where does it actually come out? And this is 1 where you can draw the line really directly. it is like, look. The customer experience is getting better. They are spending more. there is more revenue. there is less friction. Less downtime. Like, the whole thing is just better. Like, great use from an application standpoint.

Nicholas Altmann: Brent. You so much.

John Lewis Streppa: For sure. Thank you, Nick.

Operator: Our next question will come from Yitchuin Wong from Citi followed by Arjun Bhatia from William Blair. Go ahead, Yitchuin. Your line's open.

Yitchuin Wong: Hey. Good evening. Thanks for taking a question here. Spenser and team, like, great to see the fast expanding AI platform here you have, like, every quarter. Like, I wanna touch on agent analytics, which now to measure. it. I love it. Like, agents themselves, right? I mean, where the market that we see is already multiple vendors out there trying to measure prompts, measure latency, hallucination to, like, all the stuff that you can see. But what is the customer problems that the agent analytics could solve that the current observability platform cannot? And then how do you view the market opportunity? Of that problem?

Spenser Skates: Yeah. So, I mean, I think first to the extent this replaces most traditional interfaces, then, you know, the market opportunity is now as large, if not larger, Than what is going 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 what is individually happening within a session to the long term impact of your business. So you can say, okay. Hey. You got a successful answer 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 have seen in this space just kind of stand alone. it is like, okay. They will just show the traces, and that is kind of it. And you have no idea if it is actually leading to different results down the line. And so that is why we see both, like, traditional, like, enterprises that are transforming their businesses like The Economist, as well as a lot of AI natives. Know, I mentioned 1 of the largest foundational model companies They also are looking at, like, you know, as you imagine, they have a lot of tooling there, but they wanna know, okay. Is this leading to someone to becoming 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.

Yitchuin Wong: That sounds like a more TAM expansion opportunity there. Oh, absolutely. Absolutely. Yeah. I did not cover as much today. We demoed it more on the Q1 earnings call. But, yeah, it is it is it is actually 1 of the things, my chief commercial office and I are very excited about. Yeah. Definitely look forward to hearing more, including Wade. I have a quick follow-up for Andrew as well on the guidance. Like, amplitude growth had definitely been accelerating for the past year or more, right? Even adjusting for the static business this quarter, I think it is still accelerated. But the implied guide that I am looking for Q4 shows about a 2- to 3-point decel. Could we kind of have us double click on the largest step down on the Q4 guide is it more just seasonality or incremental conservatism?

Andrew Casey: I would tell you that we always take a look at what, when we are building our guidance, what we believe is, you know, very strong likelihood to occur. And I mentioned some of the factors earlier about pipeline, how well that pipeline's developed, you know, where we are seeing good demand from our customers. Usually, Q4 is our strongest quarter from an net new ARR perspective, and it is because that is the way we built our comp plans. that is the way enterprise selling cycles run typically in a calendar based company. 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 is what we are comfortable with. Got it.

Yitchuin Wong: Congrats, guys.

Andrew Casey: Thank you.

John Lewis Streppa: Thank you, YC.

Operator: And our last question will come from the line of Arjun Bhatia of William Blair followed by Willow Miller. Willow, your line is open.

Willow Miller: 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 guide. I am curious to hear how you are thinking about it now considering Statsig and now Wade?

Spenser Skates: Oh, yeah. I mean, I think Statsig is an accelerant to our long term plans, which is part of why we Vijay and I agreed Amplitude would be the best home for Statsig long term. You know, as I think the so we put up $19 million in organic growth last quarter in Q2. And so, you know, it is just we are just touching on that 20% You know, it is like the annual number is 410. So if you divide that out, it is like, you know, we are just we are just shy of that 20% growth target when you annualize the quarterly numbers. To me, as I have always said, 20% is kind of bare minimum. Like, we all wanna be making sure to continually hitting and exceeding that 20%. Long term, we are we are we are aiming a good deal higher. We wanna get to 30 and then and beyond that as we continue to grow the business. Obviously, a lot of work between here and there, but that is that is what we are very focused on doing.

Willow Miller: Good to hear. Thank you.

John Lewis Streppa: Thank you, Willow. 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 KeyBanc, Citi, and Piper Sandler. Thank you. Thank you all. Thank you.