Amplitude, Inc.

During today's call, management will make forward-looking statements, including statements regarding our financial outlook for the fourth quarter and full year 2025, 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 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. With that, I'll hand the call over to Spencer.

Good afternoon, everyone, and welcome to Amplitude's third quarter 2025 earnings call. Today, I'm going to cover three things. First, our strong Q3 results and progress in the enterprise. Second, the AI opportunity within analytics. Third, product innovation and our customers. Let's go ahead and get started with our Q3 results. We delivered another strong quarter, continuing the acceleration we saw in Q2. We exceeded expectations on our core financial metrics and made solid progress against our enterprise strategy. Our third quarter revenue was $88.6 million, up 18% year-over-year and exceeding the high end of our guidance. Annual recurring revenue was $347 million, up 16% year-over-year and up $12 million from last quarter. Non-GAAP operating income was $0.6 million. Customers with more than $100K in ARR grew to 653, an increase of 15% year-over-year. Our Q3 performance reflects our continued execution against our strategy. We are winning simple by bringing amplitude to everyone with AI. We are winning the enterprise with broad-based success with both AI natives and traditional enterprises, securing larger multi-year contracts. And we're winning the category with multi-product adoption, now representing 71% of our ARR. Finally, we're winning together by leading the shift to being AI native across the entire Amplitude team. I wanted to take a little bit of time to talk about how AI is changing how software gets built. Every single product team runs the same loop. Build, ship, use, and learn. The left side of that loop, build and ship, has been transformed by AI. AI coding has made it faster than ever to turn ideas into products. We are now at a point where someone can create a product that's used by millions overnight. By contrast, the right side of this loop, use and learn, remains in the stone ages. Companies ask users what they want, but actions are more powerful than words. The best way to understand what people want is to watch what they do. This is what Amplitude solves. Our AI analytics platform helps companies understand how people engage in their product, what they like, where they get stuck, and what keeps them coming back. These behavioral signals are the most powerful indicator for what to build. Automating and scaling that understanding is the next frontier. In this context, analytics is the perfect problem for AI to solve. To use analytics, you have to do a lot of manual work in specifying and setting up your query in between rounds of thinking. AI can handle all of that manual work, freeing up humans to focus on the thinking that leads to great insights. Amplitude has a unique position to build the AI analytics platform of the future. We have the world's largest database of product behavior. We have spent a decade working with world-class analytics teams. Over the past year, we've rebuilt the Amplitude team to be AI native. We've reorganized product development twice, and we've acquired four AI companies. We've trained our engineering, product management and design teams deeply in AI. The company that disrupts the right side of this loop, the use and learn, the fastest will define the future of this space. We are all in here. Let's get into the details on product innovation. In the last few weeks, we have launched several AI native products here at Amplitude. I want to start with MCP. In October, we announced the public availability of our MCP server, the top requested feature from our customers. MCP is made for data analytics. It exposes all of Amplitude's functionality so an AI agent can interact with it directly. It allows you to use Amplitude without knowing anything about the Amplitude UI or your data taxonomy. This is the best MCP use case that I have ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. It shows what is possible in the AI analytics future I talked about earlier. It brings the power of our data to anyone in any workflow. This opens Amplitude up to an entirely new cohort of non-technical users, in turn driving our growth. Let me show you with a quick demo. MCP lets AI tools interact directly with Amplitude data. You can ask a vague question about your product inside any AI model and have it query Amplitude iteratively. Our MCP already has native connections to Cloud, Cursor, and GitHub, with more to come soon. For this demo, I'm going to use Cloud. I'm going to start with a simple prompt, give me high-level web traffic metrics over the last few weeks. Cloud will then get context, search web traffic metrics, And then it's identified that during the week of September 21st, we've had a peak. I can then ask follow-up questions to drill down, investigate the September spike. What's driving this growth? Claude then accesses behavioral insights, checks traffic sources, marketing campaigns, and content performance. It shows that our webinar campaigns and the release of our product benchmark report drove this traffic. To get deeper insights, I'm going to ask, what are the downstream growth metric impacts by these campaigns? Claude then queries the campaign data set, downstream conversion funnels, and Salesforce metrics. It concludes that the September campaigns drove a higher number and quality of visitors to the site. Of course, I'm going to want to share these findings so I can prompt it to create an amplitude notebook for the growth team. So in a few minutes, customers can get deep research, insights, and a detailed shareable notebook that allows them to take action. In addition to the launch of MCP, we expanded the open beta for our AI agents. These agents continually monitor product data, detect anomalies, and surface insights automatically. In June, we launched our closed beta. And then two weeks ago, we opened the beta to all customers. Our focus is now on two agents. The first is the dashboard agent, which analyzes charts and proactively flags significant changes. And the second is the session replay agent, which reviews thousands of user sessions, detects points of friction, and then shows curated clips that highlight issues. Both are powered by the same behavioral data that MCP can access. They are already helping customers uncover opportunities and resolve issues faster. In addition, last week we also introduced AI visibility. As consumers turn to AI tools like ChatGPT, Cloud, and Google's AI Summary when they search, marketers are flying blind. They have no idea how their companies show up or rank within the results produced by these new tools. To solve that problem, we launched AI Visibility for free last week. Think of it as SEO for LLMs. It shows where a brand appears or doesn't across all major AI models, how they rank against competitors, and how they can improve their position. We saw a lot of excitement around the launch with our customers and on social media. The conversation about the future of AI Visibility is still going today on Twitter. Let me show you a quick demo of this too. Understanding how your products appear in AI responses and improving it will be key to increasing awareness. With AI visibility, customers can now see the percentage of mentions of their product in AI responses. They can also see competitor mentions versus their own, and then topics by visibility. They can dig into prompts to see the exact questions customers are asking and how AI is answering. For example, when people ask LLMs for product-led growth tools, they mention amplitude 90% of the time. Customers can also learn how to improve their ranking. I can use the analyze page to see how AI interprets the existing content or run a series of simulated changes to test updates before publishing. AI visibility tracks your brand and helps you turn that visibility into growth. Finally, next week, we will launch AI Feedback. This is our newest AI native product based on the core offering from our Craftful acquisition in July. We're going from acquisition to new Amplitude product launch in four months. AI feedback takes user feedback and information from multiple sources and turns it into insights a company can use. By bringing feedback, behavior, and action into a single platform, it helps teams hear customers and understand them. Let me show you with my last demo. AI feedback is the new way to listen to users at scale and act on their feedback. AI feedback collects input from all of our customers' feedback sources. You can link Zendesk tickets, Gong call transcriptions, App Store reviews, comments on Reddit and other social media, first party surveys, and more with no engineering help. In this example, I have already set up AI feedback for a mobile app and connected it to reviews from the Apple App Store as well as Google Play. Agentic AI processes the massive volume of unstructured data and sorts it into categories like you see here. Our proprietary AI analysis gives product teams the right level of detail. Users can see feature requests to help build a roadmap or can filter by topics like complaints to know which issues to address. Here, there are 26 mentions of reliable read start and alert clearing. I can click in to see specific comments for more detailed information and subtopics. For example, there are four mentions of notifications not syncing across devices. With Amplitude, customers can then turn that feedback into action. We can create a cohort of these 96 users watching session replays of how they interact with notifications or surveying them for more on what they need. Our innovation will accelerate from here. In addition to what I've just shown, over the next few quarters, we'll introduce new AI-first products like automated insights, global chat, assistant, and additional agents that extend our reach. These new capabilities expand who can use Amplitude and strengthen the value of the analytics platform overall. Every new product draws on the same behavioral data set and feeds back into it, creating a single system of improvement. That's what makes our use and learn opportunity so large. Innovation is the biggest driver of long-term growth at Amplitude and our strongest moat in this AI-first world. Let's talk about customers. We had a great quarter for new and expansion deals with enterprise customers, including Bentley Systems, FanDuel, Thomson Reuters, Taco Bell, Global Radio, Empower, Granola, Algolia, and Gusto, among others. I'm going to highlight how a few of them are putting this all to work. Three examples stand out this quarter, each showing the power of the Amplitude platform in a different way. First is FanDuel. They continue to be a great example of platform consolidation at scale. FanDuel is constantly refining its experiences and tailoring them for millions of fans. Connecting real-time feedback to customer experience is critical to their success and Amplitude powers that loop. FanDuel uses Amplitude end-to-end across multiple product lines, from analytics to guides and surveys to session replay. That unified view helps their teams test, learn, and improve faster. Their expansion and renewal patterns remain among the strongest in our base. Second, Granola. This fast-growing AI startup adopted Amplitude before launch after hearing about us through the AI ecosystem. Today, more than half the company uses Amplitude every day to understand how people use their product and where to iterate next. Granola ships new features quickly and relies on real-time feedback to guide product decisions. Their story is a great example of how AI native companies are choosing Amplitude to accelerate growth and scale with confidence. Third is Bentley Systems, a global leader in design, construction, and infrastructure software. Bentley selected Amplitude as its single analytics platform across all products. The company previously relied on siloed legacy tools, but needed one system to understand usage, drive adoption, and guide feature development. By activating historical data from Databricks and combining it with behavioral insights in Amplitude, Bentley can now test, learn, and implement improvements far more quickly across its portfolio. These stories all point to a common theme. From AI startups to global enterprises, customers are betting on Amplitude as the AI analytics platform that will help them thrive in this new era. We are at the beginning of redefining analytics as an AI native system that learns, reasons, and acts. Over the next few quarters, we will bring a new wave of AI native products to market that will reshape how companies use data to build better products. This is just the beginning of the AI era for analytics. I'll now hand it over to Andrew to walk through the financials.

Thank you, Spencer, and good afternoon, everyone. We've delivered another solid quarter of acceleration in our ARR, improved operational efficiency, and created greater durability in our future revenue base. Our customers are increasing their pace of innovation and, in turn, need to understand how the changes are being received, how to adapt, and how to implement those changes. As such, we believe Amplitude's importance to the enterprise is increasing. On our business, we have continued to perform against our strategy that we communicated at our investor day earlier this year. We've increased the value our platform can deliver by adding guides and surveys, AI agents, our MCP server, and others. We've grown the base of our enterprises we are serving, the number of customers that are using multiple products, and the full platform. We've done all this while improving operational efficiency of the business. We continue to improve the durability of our business as measured by improving our contract duration and remaining performance obligations, or RPO. This quarter, our average contract duration grew to nearly 22 months, up from 19 months just one year ago. Our RPO growth has improved from Q2 with current RPO growth year-over-year accelerating to 22% from 20% last quarter. and long-term RPO growth accelerating to 78% year over year, up from 64% last quarter. This results in total RPO growth of 37%, accelerating from 31% last quarter. The growth in our RPO is the direct result from building a more repeatable and scalable go-to-market strategy focused on enterprise customers. Turning to our third 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 on our website. Third quarter revenue was $88.6 million, up 18% year-over-year and 6% quarter-over-quarter. This quarter's growth benefited from better linearity in deal signings earlier in the quarter, growth in our services business, and the strong net new ARR growth we had in Q2. Total ARR increased to 347 million exiting the third quarter, an increase of 16% year over year, and 12 million sequentially. 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. Platform sales were also particularly strong. 39% of our customers now have multiple products, with 71% of our ARR coming from that cohort. The number of customers representing 100,000 or more of ARR in Q3 grew to 653, an increase of 15% year-over-year and up 19 customers since last quarter. In-period NRR progressed to 104, led by cross-sell expansions. Gross margin was 76% for the third quarter, down one point from the third quarter of 2024, but up one point since last quarter. We continue to make progress on optimizing our hosting costs, driving multi-product contracts, and monetizing services engagements. And we will continue to look for opportunities to incrementally improve our gross margin over time. Sales and marketing expenses were 43% of revenue, a decrease of one point from the second quarter. We continue to focus on improving sales efficiencies, driving improvement through our changes in process, coverage, and expansion of enterprise customers. At the same time, we are investing for future growth while balancing this incremental investment with efficiency gains. G&A was 13% of revenue, down three points from the third quarter of 2024. We expect G&A to improve as a percentage of revenue over time. R&D was 19% of revenue, up three points from the third quarter of 2024. We expect to continue to invest in the talent and the capabilities of our team to drive greater innovation in the future. Total operating expenses were $67 million, or 75% of revenue, down one point sequentially. Operating income was $0.6 million, or 0.6% of revenue. Net income per share was 2 cents based on 143.2 million diluted shares compared to net income per share of 3 cents with 131.3 million diluted shares a year ago. Free cash flow in the quarter was 3.4 million or 4% of revenue compared to 4.5 million or 6% of revenue during the same period last year. In the third quarter, we managed our cash collections and made meaningful progress shifting to contracts with annual payments in advance. Now, turning to our outlook. As Spencer laid out, the world of development, test, and ship is changing rapidly. Analytics and the use of data to understand outcomes and drive action will be more important than ever for enterprises. Our strategy remains consistent with our go-to-market. We will continue to focus on gaining new enterprise customers and driving cross-platform sales with our existing customer base. We also believe that with the release of our AI capabilities, the monetization of data ingested into our platform and cross-sell opportunities of new products gives us the right strategy to align the value of our customers received with our growth opportunities and to grow our business in a profitable way. For the fourth quarter of 2025, we expect revenue to be between $89 and $91 million, representing an annual growth rate of 15% at the midpoint. We expect non-GAAP operating income to be between 3.5 million and 5.5 million. And we expect non-GAAP net income per share to be between 4 cents and 5 cents, assuming diluted weighted average shares outstanding of approximately 142.6 million. For the full year of 2025, we are raising our revenue expectation for the full year due to the quarter's positive performance. We expect full-year revenue to be between $340.8 and $342.8 million, an annual growth rate of 14% at the midpoint. We are adjusting our range for the full-year non-GAAP operating income to be between $0.5 million and $2.5 million, reflecting growth investments. We expect non-GAAP net income per share to be between $0.06 and $0.08, assuming weighted average shares outstanding of approximately $142 million, as measured on a fully diluted basis. In closing, we continue to execute our strategy of growing our enterprise customer base, expanding multi-product attach with our customers, and growing with additional leverage in our business model. This has only occurred through the focused execution of our employees and our relentless drive towards creating value for our customers. With that, we'll open it up for Q&A. Over to you, John.