Amplitude, Inc.

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

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

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