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Good afternoon, everyone, and welcome to Amplitude's Second Quarter 2026 Earnings Conference Call.
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.
Thanks, John, and good afternoon, everyone. Welcome to Amplitude's second quarter 2026 earnings call. Today, I will 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 $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 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 had built with AI 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 teams 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++ 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 three times the number of pull requests in six months. We have reduced our pull request cycle from five hours to 44 minutes. Bug reports are down 55%. Five percent 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 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 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 have improved our pricing and packaging. We reduced down to a one-meter to make it simpler for enterprises to add additional products. We increased 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. One 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 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 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 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 three 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 has 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 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 a demo, I want to 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, and 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 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 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 well. 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 CUPED 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 capabilities 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 Wade suggested and built for Amplitude's documentation site. On our documentation site, Wade found a spike in failed searches by 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 is impacted. Expected impact: decreasing total search failures by 80%. Then Wade 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, 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, 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 three 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 was 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 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 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. They achieved 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 have transformed Amplitude to be AI-native, and we are building the future of 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.
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 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 the 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, 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 the Statsig business. We are working to optimize the new hosting environment and cloud structure but it will take some time to improve 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 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 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 a 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. 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. That has increased the durability of our business through our RPO growth and we have 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 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. Forty-eight percent 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 two times 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 forma basis includes Statsig 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 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&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 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 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 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 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 weighted average shares 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 performance 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 performance 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 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, I'll open up for Q&A. Over to you, John.
Thank you, Andrew. We are going to Q&A. For the sake of time, please limit yourself to one question and one follow-up.
分析師問答
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.
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 it internally for several months now. 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 addressable market over time?
When you say bespoke, like, say more on that—like, instead of using particular agents to do a specific task underlying because you have a lot of agents doing things underneath? I see what you are saying. 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. What Wade is doing in particular, to your point, is 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. So it is operating at a higher level. In terms of the persona we are seeing, there 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 shipping code. If you look at where the AI-native teams everyone aspires 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 automating more of it because we are saying, hey, we are going to look at all the data all the time and then suggest recommendations. I've been talking about self-improving products at Amplitude for about nine years, and I am actually blown away by what is possible with the technology today. 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 "here's what could be better" and best practices is extraordinarily impressive. What that means is that just by the fact that someone is using your software, it is getting better because it is translating recommendations. You no longer need someone to go into Amplitude or any data system and say, here's my interpretation of these results. So I do think in terms of budget and persona that 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, but the way you do it looks different. Did I hit on what you are looking for?
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 two times 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 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.
Yeah. Sure. So a couple things. One is that when we look at our ability to actually generate revenue in the out quarters, we start with the strong balances we are booking that are showing up in our RPO. When you have commitments from customers for a longer term duration, you start to have better and better predictability about your future revenue. That is the first thing. The second thing is we look at how much our customers are actually responding to some of the initiatives we are putting out in the field. That comes in the form of our new product capabilities, our new pricing, new packaging, and areas where our sales team is running new promotions and activities. All those are bolstering our ability to see a stronger pipeline, and that pipeline progresses faster through its stages, which gives us greater confidence that we will add more net new ARR. From a revenue perspective, the predominance of our business is coming from subscription revenue. So those key factors on understanding baseline, pipeline, and conversion are what I refer to as our ability to execute against the plans that are in front of us. The sales team has 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, I'd tell you: look, in the case of the Google environment that we took on for Statsig, we are focused on driving optimizations in that environment over time. It is definitely lower; we said in the low 50s from a gross margin perspective. That comes from us taking on a whole new cloud and hosting environment. Most of Amplitude is on AWS, so we took on a whole new cloud and hosting environment and you have to go through the paces of optimizing how you run those environments for customers. Our first objective was integrating and making sure there was no disruption of service. Now we are moving quickly into how we can optimize those environments. That is one big lever on the gross margin side. And we are constantly looking at how we can make investments to drive greater efficiencies across all of our operating expense areas.
Brent. Thank you, Mark.
Our next question will come from the line of Jackson Ader from KeyBanc. Followed by Scott Berg. Go ahead, Jackson.
Hey. Thanks, guys. Good to see you. I was curious—Andrew, sticking with you and talking about the operating expense side. We have 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 are still around breakeven. So I am curious as you are thinking about 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?
Well, I would tell you that one, we still expect from an organic perspective we have 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 is the predominant way we will see increasing operating income. As far as leverage as a percentage, over time I expect we will be able to drive better and better gross cost of revenue and increase gross margins, but it just takes time—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 well into the 80s now as far as percentage of what customers have ingested versus their entitlements, and that portends increasing expansions on upsell, which is usually where we have had issues in the past of overselling and how to right-size contracts. For 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 predominant aspect of driving improving profitability. As far as leverage goes, I think gross margins will improve over time; it is just going to take a while. We still have a long way to go on sales and marketing reducing as a percentage of revenue. I think G&A has room, and over time we will see greater efficiencies with the R&D organization as they adopt more capabilities to build products at a faster rate.
Okay. And then just a quick follow-up. Can you remind us, now that we are on a different pricing and packaging model, should there be any difference in terms of the seasonality of your revenue ramp or recognition as we move forward with the new packaging?
On revenue, you get a fairly predictable pattern under which revenue is recognized because most of our revenue in future periods is designated by our RPO—the committed contracts. ARR will follow a very typical seasonal pattern. My expectation is enterprise selling basis: Q1 will always be our weakest as far as net new ARR adds 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 going to have that slow start and then pickup. This year, in Q1, we also had some big changes in our sales and marketing leadership, which is the predominance of what you see now flowing through and a cost benefit from a lower sales and marketing as a percentage of revenue. That is from efficiencies we are driving.
Brent. Thank you, Jackson.
Our next question will come from the line of Scott Berg from Needham followed by William Fitzsimmons. Go ahead, Scott.
Hi, Spenser and Andrew. Nice quarter. Thanks for taking my questions. I wanted to follow-up on sales enablement that Andrew was chatting about. We did a couple different customer checks in the quarter, and one thing that we came back with is I don't think your existing customers are quite aware of all the different modules and innovations that you have rolled out this year. I know that is a function of time, obviously, and one customer did not even know that you had acquired Statsig. So I guess where are you in that journey? When is the field properly ramped? The quarter sales results were good, but obviously better awareness can help further.
Yeah. To your point, a lot of people still bucket us as an analytics company, and that drives me absolutely crazy. I honestly just want to go out and say, hey, we have Statsig now; this is bleeding-edge feature experimentation and you can use it too. A lot of customers do not even know that. Same with Wade. I think understanding Wade and our other products like session replay, guides, surveys, and AI feedback that can displace point solutions—there is still a lot of education to do. If you remember from the prepared remarks, we are moving customers from one product to multiple products but it is slower than I'd like. There is no substitute for the work of educating the hundreds of people we have in the field and then they have to educate the thousands of customers in the market—that is real work. I am spending a lot of time with Nate, our chief commercial officer, and the executive team on how we get that done more efficiently. We just kicked off a few weeks ago where we showed off a lot of what you saw today with Statsig, Wade, and custom agents. That said, customers are mainly looking for proof that we are at the bleeding edge of where this field is going. Even if they are not ready to adopt Wade or Statsig immediately, they want to know we can help them take the first steps. So we still have work to do to make sure our field is equipped. Some areas do this extremely well, but there are others where we need to improve. Appreciate you calling that out.
Thanks for that, Spenser. And then a follow-up on the integration traction with Statsig. You all had a pretty aggressive goal to move that asset into your organization. Where are you with it? Because the customers we spoke with were super excited. Have you hit your goals around that? Are you at the point where now you can just deliver on product and sales versus having to integrate the organization?
As you might imagine, Statsig has been around for five years and there is a lot of work getting it from a standalone company into our organization. We have gotten through the urgent fires in running and delivering Statsig; we are operational and delivering. Customers are very excited about how it is landing. We want to make sure it is our main focus; for OpenAI it was more of an internal tool, but for us it is core and it has been received positively. Now we are starting to think about what's next for Statsig. We are shipping updates and continuing to integrate it more tightly with Amplitude so that if you are on both you get benefits of using data from one with the other. There is a lot of demand from AI natives in particular. One reason we are excited to join forces with Statsig is that future product development is being run that way: engineering-first teams that are very technical and want to manage deployment harnesses, and Statsig is set up well to scale. OpenAI runs a version of that infrastructure internally and has tested it in many ways. We are doing the same for customers outside OpenAI. There is a lot to do in terms of making Statsig a core part of the software development harness for bleeding-edge AI customers and that is where everyone wants to go over time. That is our focus.
Of course, Scott. Brent. Thank you, Scott.
Our next question will come from William Fitzsimmons from Piper Sandler followed by Clark Wright from D.A. Davidson. Go ahead, Billy.
Hey, guys. Good to see the results and guidance. I think one of the exciting things about Statsig is potentially the cross-sell opportunity. Last I checked, I think there were 80 of the 400 Statsig customers already on Amplitude, so there are a lot who are not. Can you help contextualize how we should think about the potential cross-sell opportunity—Amplitude into Statsig or vice versa—and how to think about that through the model long term?
I think the much bigger opportunity is to take Statsig to Amplitude customers. Statsig customers tend to be more bleeding edge from an AI innovation standpoint, and that is where many organizations want to go long term. Historically, Amplitude has focused on product management and Statsig is tailored toward engineers with lots of customization and statistical testing out-of-the-box. Those personas are merging over time, but it is early days. As more of our traditional Amplitude customers try to build AI-native capabilities and introduce AI into their software development process, build out a harness, and get to self-improving products, all of those are opportunities to bring Statsig in. We definitely see places where Statsig customers are interested in Amplitude, but there is a lot more opportunity both in number and ARR from Amplitude customers adopting Statsig.
Perfect. And then a second question—can you contextualize how your hiring needs have changed year to date and where you are seeing the best ROI from AI-driven efficiencies internally within Amplitude?
There is a lot. On the hiring front, I have been focused on transforming the workforce—bringing in leaders, engineers, and people in other functions who are AI-native both by hiring and acquisition, and retraining the workforce we have. Everyone wants to learn because they see AI skills as increasingly relevant. We added Angela in marketing; we are always looking for companies and talent to bring in. University hiring has been a source of highly leveraged talent for us. Specifically, for Statsig, it is a complex product and codebase and we have taken our experimentation team to run Statsig, but we need more help—data science leads, deployed engineers, other engineers familiar with the architecture. We have hired someone who used to work at Statsig and we are continuing to add roles there. We have caught the ball, but now we have to go maximize it.
Alright. Thank you, Billy.
Our next question will come from Clark Wright from D.A. Davidson followed by Koji Ikeda 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. Could you break out the adds from Statsig? And what else is helping in terms of the new logo momentum that you are seeing today?
About 40 customers came from the Statsig business itself that we added. Doing the math, you are still in almost 23–24% growth in customers in that greater-than-$100k cohort. It is still growing quite nicely and contributing to ARR and revenue growth. When we talk with customers who were formerly Statsig customers and are brand new to Amplitude, they appreciate that Amplitude is stewarding and taking forward the roadmap and showing confidence in our ability to give them a future where self-improving products are a reality. They adopt an experimentation mindset and are confident to move further with Amplitude. That cross-sell expansion opportunity is real. There was a multi-hundred-million-dollar opportunity for us just in that install base, so we are excited about it.
Got it. And last quarter you called out event volume growth being 21% year-over-year. What is that now as you talk about momentum and all-time highs? How should we think about the ramp going forward given agentic workflows and the amount of events they can process?
It is definitely growing faster than both ARR and revenue, and it is one of those areas that feels like we have gone through many quarters of bringing it up and getting entitlements right-sized. It is a leading indicator that we are not going to have the same churn issues we had in the past. Sales has adopted a value-based orientation where they are not trying to get everything upfront but trying to get customers to value quickly and show expansion. It is an indicator that we will see upsells contribute more meaningfully to growth, whereas before it was a detractor. If customers are bumping up against their entitlements and getting value from the investment, that leads to better expansion outcomes.
Brent. Thank you, Clark.
Our next question will come from Koji Ikeda from Bank of America followed by Nicholas Altmann. Go ahead, Koji.
Thanks, guys. I wanted to ask a question on Wade. Love the demo and the long-term vision. 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? It seems like you could do it all from Wade.
Totally. Architecturally, Wade takes data from lots of different data sources: analytics data from Amplitude, experiment data from Statsig. We plan to make it agnostic long term so it can take data from any analytics system—Google Analytics, Adobe, or others. It needs data; it cannot just look at a product and figure out what people are doing without that data. So Amplitude Analytics and experimentation play an important role as collection points. The more data sources you put into Wade, the better the output. One of the big learnings from the AI boom is that the power of massive scale of data gives you better and more accurate results. Amplitude has datasets that allow us to develop a much higher-quality version of Wade than others. If you are a startup without massive datasets, it's harder to know if recommendations are best practices. We have feedback loops from many customers and product types that help train and validate Wade. We are one of the few companies with this kind of dataset; there is no open-source equivalent. That allows us to build a better Wade. We are running an alpha with real customers—startups and some enterprises—and it is already producing real results. I believe whoever wins this space could build a multibillion-dollar business, and our positioning in analytics and other areas gives us a strong foundation. Let's build it as quickly as we can.
Got it. Thanks, Spenser. All from me.
Our next question comes from Nicholas Altmann from BTIG followed by YC Wong from Citi. Go ahead, Nick.
Hey. Awesome. Thanks, guys. Just to build off Koji's last question, I wanted to ask the inverse on Wade: it seems like there is more incentive to adopt the broader platform with Wade. How are those conversations going with customers? Are you seeing more multi-product or platform adoption as they look at Wade and the vision of the self-improving product? And how should we think about Wade being monetized in the near term? Is it indirectly monetized by increasing platform adoption or will it be a standalone SKU?
You are exactly right: the more data sources you feed Wade, the better it becomes. We've already seen multiple customers get on session replay and AI feedback specifically because Wade makes those data sources more valuable. Session replay is especially powerful because viewing the exact UI state and where a user clicked has a lot of value. This is driving the whole platform play: customers add data sources like session replay and AI feedback because it improves Wade's output. We also want Wade to be agnostic and plug into other systems. On monetization: we will charge for it. Think about the value Wade creates: going from manual analysis to a flow that finds issues, recommends fixes, generates code or PRs, and measures the outcomes. That directly improves customer experience, revenue, and reduces friction. The capability is something customers will pay for. We are in alpha and have not finalized exact pricing, but we absolutely will charge for the capability. This is a direct place where AI can translate into clear customer value and monetization.
Thanks so much.
For sure. Thank you, Nick.
Our next question will come from Yitchuin Wong from Citi followed by Arjun Bhatia from William Blair. Go ahead, Yitchuin. Your line's open.
Hey. Good evening. Thanks for taking a question. Great to see the fast-expanding AI platform you have. I want to touch on agent analytics. Agents themselves are becoming a major surface area. There are multiple vendors trying to measure prompts, latency, hallucination, and so on. What customer problems can agent analytics solve that current observability platforms cannot? And how do you view the market opportunity for that problem?
If agent interfaces replace traditional interfaces, the market opportunity becomes as large or larger than traditional user interfaces with session replay and analytics. Our unique positioning is that we can connect what happens within a session to long-term business impact. You can ask: if a bot provided a successful answer, did that lead to higher spending, sign-ups, or retention? Conversely, did a frustrating bot interaction lead to a negative outcome? Many engineering-focused observability products just show traces and logs, but you have no idea if it leads to different business outcomes. Being able to connect that journey end-to-end is what we uniquely offer. That is why both enterprises transforming their businesses and AI natives are customers—because they want to know if their agents are producing real business value, not just technical metrics.
That sounds like a meaningful TAM expansion opportunity. I did not cover this as much today but I remember you demoed it on the Q1 call. I have a quick follow-up for Andrew on the guidance. Amplitude's growth has been accelerating for the past year or more. Even adjusting for Statsig, it seems to have accelerated. The implied guide for Q4 shows about a 2- to 3-point deceleration. Could you double-click on that step down—more seasonality or incremental conservatism?
When we build guidance, we focus on what we believe has a very strong likelihood to occur. We factor in pipeline, RPO, and what is converting. Usually, Q4 is our strongest quarter from a net new ARR perspective because of how comp plans and enterprise selling cycles run in a calendar-based year. Our guidance reflects what we know is out there—the pipelines and RPO—and what we are comfortable with. Some of the apparent step-downs can be seasonality and the way enterprise cycles play out quarter-to-quarter.
Thank you, YC.
And our last question will come from the line of Arjun Bhatia from William Blair followed by Willow Miller. Willow, your line is open.
Hey team. Thanks for taking our question. Can we hear your updated thoughts on the 20%+ revenue growth target given the strong growth this quarter and the strong third quarter guide? I am curious how you are thinking about this now considering Statsig and Wade?
Statsig is an accelerant to our long-term plans, which is part of why we felt Amplitude was the best home for Statsig. We put up $19 million in organic growth in Q2. If you annualize the quarterly numbers, we are just touching that 20% growth target. To me, 20% is kind of a bare minimum; we want to continually hit and exceed that. Long term we are aiming higher—30% and beyond as we continue to grow the business. There is a lot of work between here and there, but that's the direction we are very focused on.
Good to hear. Thank you.
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 all.
Thank you. This concludes today's conference. You may disconnect your lines at this time. Thank you for your participation.