Amplitude, Inc. (NASDAQ:AMPL) Q4 2025 Earnings Call Transcript

Amplitude, Inc. (NASDAQ:AMPL) Q4 2025 Earnings Call Transcript February 19, 2026

John Streppa: Good afternoon, everyone, and welcome to Amplitude’s Fourth Quarter and Full Year 2025 Earnings 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 first 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.

With that, I’ll hand the call over to Spenser.

Spenser Skates: Good afternoon, everyone, and welcome to Amplitude’s Fourth Quarter and Full Year 2025 Earnings Call. Today, I’m going to cover 3 things: First, our strong Q4 results and progress in the enterprise. Second, how AI is driving demand for analytics, and our strategy to deliver. Third, a look at our new AI agents in action and a spotlight on customer stories. Q4 represents one of these strongest quarters in Amplitude history. Our fourth quarter revenue was $91.4 million, up 17% year-over-year and exceeding the high end of our revenue guidance. Our annual recurring revenue was $366 million, up 17% year-over-year and up $18 million from last quarter. This was our highest net new ARR quarter since 2021. Non-GAAP operating income was $4.2 million or 4.6% of revenue.

Customers with more than $100,000 in ARR grew to 698, an increase of 18% year-over-year. Over 25 AI companies are now included in that $100,000 cohort as well. This quarter was marked by balanced execution. No single deal was over $1 million, yet we had our highest ever number of multiproduct and $100,000 ARR lands. I want to talk more about AI and our strategy. Over the past year, AI coding assistance from Anthropic, OpenAI, Cursor and others have compressed development cycles dramatically. The velocity at which companies are shipping new products has accelerated. When software is this easy to build, it creates a gap between how fast teams can ship features and how fast they can learn if they are working. This shifts the pressure to the right side of the product development loop that you see here, the use and learn side.

Q&A Session

Understanding how users behave, what works and what doesn’t and what actions to take next becomes the bottleneck. The constraint is no longer knowing how to build, it is knowing what to build instead. This is the hardest problem in software today. I say that because builders and their AI assistants need a system of context that combines multiple data streams. They need structured behavioral data. They need the correct retention and funnel logic, and they need the right analytical tools exposed in a way that enables AI to reason effectively. The AI then needs to be able to iterate with that system, test hypotheses, refine queries, identify root causes and recommend actions accurately and repeatedly. This is not something that can be bidecoded over a weekend or replicated accurately with an LLM on a data warehouse.

However, it is exactly what Amplitude is purpose-built to do. We have worked with thousands of companies over the past 13 years and amass the world’s largest database of user behavior. Our AI can explore patterns, explain changes and guide teams on what to do next more accurately and reliably than any other system. Over the past 6 months, our Agentic analytics platform has reached a 76% success rate on complex production-grade queries, that is 7x better than a straight text-to-SQL approach. With the new agents we launched yesterday, teams can now move from insight to action in minutes, not weeks using analytics, cohorts, experiments and messaging in one continuous agenetic workflow. Through our MCP integrations with Anthropic, Figma, OpenAI, GitHub, Lovable and Slack, we are bringing behavioral intelligence to teams where they already work.

Understanding user behavior now becomes as simple as asking a question in a chat window. This puts Amplitude in a unique position. The frontier labs are pushing the boundaries of AI models and they recognize the complexity of analytics experimentation and behavioral understanding, so they turn to Amplitude. As I mentioned earlier, more than 25 of the leading AI native companies, including some of the names you see here, our customers with over $100,000 in ARR with Amplitude. In addition, one of the world’s largest frontier AI labs is a 7-figure customer as well. They came to us to replace a manual system built from fragmented internal tools and raw warehouse data. Using Amplitude Enterprise Analytics and Session Replay they can now understand activation, engagement, retention and monetization end to end.

With Amplitude MCP, they can offer those insights directly within the AI environments, their teams already use, dramatically improving the ability for them to automate development. And it’s not just AI companies, companies of all sizes need a system that gives them trusted data, insights and action to successfully deploy AI in the real world. So they turn to Amplitude as well. This momentum, combined to one of our strongest quarters across gross bookings and new ARR alongside meaningful improvement in churn. Our go-to-market motion has matured. There is a tighter focus on value-based cases in the enterprise and on expanding with multiproduct deployments. We continue to consolidate the fragmented market. Platform win rates are increasing against point solutions and our newer products are gaining traction.

Guides and surveys launched less than a year ago, is our fastest-growing product to date. We are also seeing a large increase in AI native usage as agents connect directly to Amplitude. Over the past few months, the total number of queries triggered by AI agents has increased dramatically. In October last year, there were almost none, and today, it is 25%. Agents also drove the vast majority of overall incremental query growth. This tells us that customers are trusting agents with analytics work. It also indicates that our platform offers the accuracy and the context needed in production environments. Taken together, this creates a powerful tailwind for Amplitude as we continue building a durable, scalable company that can unlock the next frontier and software.

Over the years, we have intentionally expanded beyond core product analytics and into adjacent workflows. We have continued that work and acquired InfiniGrow, an AI-native marketing analytics start-ups that connect spends, behavior and revenue impact. InfiniGrow brings strong AI native engineering talent to Amplitude. This strengthens our platform as a system of context and expands our ability to bring acquisition, activation and retention into one continuous feedback loop. Yesterday, we launched our global AI agents, specialized agents and MCP. This represents the start of a fundamental shift in how teams work with their analytics data. Historically, analytics has required humans to do most of the heavy lifting, writing queries, building dashboards, monitoring changes, interpreting results and then figuring out what to do next.

That process does not scale in the world where teams are shipping faster and faster. AI agents change that model. Instead of asking questions one at a time, teams can now delegate work to agents that continuously analyze behavior, surface insights and guide action. Our agents understand events, funnels, cohorts, experiments, session replay and outcomes because they operate inside a context system specifically designed for them. Agents make life easier by doing the work that slows teams down today. That is very, very different from bolt-on AI tools from SaaS companies that sit outside the data and try to infer meaning after the fact. The best way to see this and understand this is to look at it in action. I want to show you a quick teaser video, and then I’m going to show you a demo of what we’ve released.

Let’s go ahead and roll the video. [Presentation]

Spenser Skates: It’s a great question all product builders should ask themselves now, what will you build? I want to now walk you through what we’ve launched in AI analytics yesterday. I’m really excited about the future, and I want to show you Global Agent. Global Agent radically changes how our customers interact with their data. Starting your day with a dashboard is dead. Take a look at this interface, no dashboard, no graphs, no charts, just a chat box and a few simple prompts if customers need help getting started. I can talk to Global Agent like I talked to a colleague. I’m going to go ahead and ask it how’s our loyalty program doing? In seconds, it comes back with a summary. Notice, I didn’t use any jargon about event totals or taxonomy, just a regular question.

It’s calling out some pretty concerning numbers. Only 5% of users who view our welcome page actually go on to join the loyalty program. That is low, so I’m going to click in and investigate more. The Global Agent has followed me to a deep dive on this chart. I can keep investigating with another simple question. Break this down by traffic source. Here’s the breakdown. Facebook and Instagram are driving loyalty sign-ups at 5.6% and 5.2%, while Google and direct traffic lagged behind. The Global Agent summarizes it perfectly. Social media converts 10% to 15% better. Since social media outperforms Google, I might shift ad spend, but looking overall, all the rates are low. So before reallocating budget, I’m going to go deeper. Is this a channel problem or an audience problem?

Let me ask, do new users convert differently than existing customers? Without AI, this kind of analysis takes a lot of time, segmenting users, comparing funnels, pulling insights together, the Global Agent does it in seconds. And here it is, 14% conversion for repeat purchases, 5.4% overall. That’s 2.6x higher. That answers my question. It’s an audience issue, not a channel issue. I should reallocate my budget towards repeat purchases. Again, simple language, fast answers, deep learning that anyone can use. Analytics is the perfect use case for agents. So I want to show you specialized agents. Our specialized agents work continuously on specific jobs that would usually take dozens, if not hundreds of hours, monitoring dashboards, analyzing session replays, processing feedback, running conversion experiments, legwork now done automatically.

We’re going to be eating our own dog food on this one. I already have a session replay agent set up to monitor our own session replay tool and have it set in addition to sending a slack when it has a strong finding. This specialized agent has been watching hundreds of replays and sent me some summarized findings. Users with multiple saved filters type search terms, but cannot find filters without scrolling through the full list. Power users cannot preview filter criteria before applying, forcing trial and error selection. These are all things we should improve. We could have had someone watch all those replays. We could have talked to customers from hours on end or we could have let these continue to be issues. Instead, I get these findings served to me on a daily basis with a full report and a detailed breakdown with key findings, suggestions on what to explore next and even a highlighted [ brief ] set of replays of these issues.

Okay. We’re going to save the best for last. Finally, I want to show you what I’m most excited about, which is Amplitude MCP. We’re releasing a fast-growing library of expert level workflows that customers can trigger in AI clients like Claude with a simple slash command. I’m going to go ahead and use Amplitude and Claude by typing use slash create dashboard and create a dashboard that tracks our growth conversion performance, hit, I hit enter and it goes to work. Instead of me manually creating 15 charts, running the segmentations myself, and piecing together an explanation in a doc, this skill handles it in 1 click. With MCP apps, Claude is opening and building Amplitude charts right inside itself. It’s done it. So I’ve now gone to the link it gave me in a perfectly built dashboard with top-level metrics, conversion funnels and segment breakdowns.

Amazing. Moving on to customers. We had a great quarter for new and expansion deals with enterprise companies, including one of the largest music streaming apps, the Cheesecake Factory Asana, PGA of America, CrossFit, Stewart Title Guaranty Company, Crunch Fitness, WHOOP, Once Upon Publishing and NTT DOCOMO. I’m going to highlight 3 examples that demonstrate the power of the platform in different ways. Japanese telecom NTT DOCOMO is using Amplitude across more than 1,000 active users to drive efficiencies at scale. As an early design partner for our AI agents, their data platform team uses agents to streamline analysis across existing dashboards. In 1 project, an agent reduced campaign analysis time by over 90%. Our AI-powered session replay summaries automatically localized into Japanese help UX teams identify issues faster and improve the digital journey for millions of customers.

We are now working closely with NTT DOCOMO to shape our agents road map with feedback on collaboration features and AI-powered insights. Siemens, the $70 billion global technology leader partnered with Amplitude over 3 years ago to power analytics across its website presence and broader digital ecosystem. By consolidating onto our AI analytics platform from a series of point solutions, Siemens gained a unified real-time view of user behavior. Recently, the team organizing their annual user conference use Amplitude to identify their overreliance on direct e-mail and organic channels. They experimented by reallocating spend into targeted web promos plus paid and organic social. This delivered a 90% year-over-year increase in web traffic and a projected 50% increase in registrations in attendance to their conference.

Lastly, we landed one of the largest music streaming apps in the world. We are working with the teams that lead checkout optimization, upgrades, churn prevention and recovery as they seek to understand the revenue drivers for hundreds of millions of monthly active users. They will use Amplitude analytics combined with session replay to get a holistic view on these monetization drivers. These stories all point to a common theme from AI start-ups to global enterprises, customers are betting on Amplitude as the AI analytics platform that will help them thrive in this new era. Before I hand it over to Andrew, I want to be clear on how AI is shaping our opportunity. There is a common misconception in public markets that AI makes analytics either irrelevant or easy to replicate.

The exact opposite is true. AI has made software easier to create, but creation is no longer the moat. The real advantage is how quickly a team can learn, iterate, improve and automate. Agentic analytics is the key. It unlocks the bottleneck on the right side of the product development loop and enables teams to learn as fast as they ship. AI is a structural tailwind for Amplitude. It is why I believe the opportunity ahead is massive and why I’m excited about what’s to come. Now over to Andrew to walk you through the financials.

Andrew Casey: Thank you, Spenser, and good afternoon, everyone. 2025 was a year of innovation, execution, and we delivered a solid base for our future long-term growth strategy. When we met at our Investor Day last March, we laid out a deliberate road map to capture the enterprise and accelerate multiproduct adoption, while leading the industry in innovation. Today’s results demonstrate that we haven’t just met those goals, we’ve established a new baseline for durable growth. The enterprise is now our core growth engine. ARR from our enterprise customer cohort is up 20% year-over-year, with higher retention and expansion rates than the rest of our business. This was not by accident or luck, our AI analytics platform has been designed to be enterprise-grade with trust and safety of our customers at the center.

Our go-to-market team has worked for the past 3 years to orient our go-to-market motion to focus on the enterprise, increasing customer value through selling our platform and engaging in longer-term contracts. 2025 was the coalescence of this work to focus on our customers’ value and creating durable base for future growth. We sustained growth of current RPO greater than 20% throughout the year. And in Q4, total RPO grew 35% year-over-year. Our average contract duration is now above 22 months. In addition to our success in the enterprise, we have also formulated our product and our go-to-market team to embrace our AI platform strategy. By combining niche point product solutions surrounding analytics into a comprehensive platform, we are able to deliver greater value than stitching together point solutions.

We also believe that having a platform is essential to the harnessing capabilities of AI to reduce friction in our customers’ workflows. In 2025, we did a great job expanding our multiproduct attach rate for our customers. 74% of our ARR is from customers with more than 1 product, up 15 percentage points from last year. We still have a great opportunity to expand our multiproduct customers as well. Only 51% of our ARR comes from customers with greater than 3 products. Looking at a full platform deployment of 5-plus products, that percentage is 20%, doubling year-over-year. We have a massive opportunity to expand with our customer base. We believe our market opportunity expands dramatically with the inclusion of our new AI products that promise to expand adoption and use cases.

The progress in selling our platform is best exemplified through improvement of our retention and expansion motion with dollar-based net retention now above 105%, after exiting 2024 at 100%. However, our work is not done. At the beginning of this year, we introduced a new pricing and packaging strategy to our sellers. Let’s start with what’s not changing. We are not changing our core billing metric of events. We believe this is a great representation of the value our customers receive from our platform, and it is also an appropriate monetization strategy as we center AI engagement on our platform. What has changed is we are centralizing the monetization of our other products, such as experimentation, session replay, guides and surveys to be a percentage uplift on the core platform charge, which is events based.

This reduces the friction of adoption of those products by making it easier to understand for our customers and reduces the need to estimate how many sessions or experiments they want to run in the near term. Longer term, this will also encourage greater consumption on our platform as comes no longer fear over using certain parts of the contract or underutilizing others. It’s a radical simplification of our pricing that acknowledges our customers’ needs for greater cost transparency and certainty on their costs as the volume of data ingested into our platform expands. It also supports our focus of integrating AI into all of our product offerings and expanding customer usage, which can be a tailwind longer term on easier lands and faster platform expansions.

In summary, as we’ve transitioned to an AI analytics company, we have created a more durable base of our business focused on the enterprise. We’ve driven expansion of our platform through innovation, and we’re making it easier for customers to get value quickly and encourage expansion. We’ve done all this while being disciplined in our spending and driving to non-GAAP profitability with record free cash flow. Looking at the rule of 40, which we measure based on free cash flow yield and ARR growth, we’ve now improved from a rule of 15 in 2024 to over 24% in 2025. We’ll continue to focus on driving top line growth through a disciplined manner in 2026. Now turning to our fourth quarter and full year results. And 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. Fourth quarter revenue was $91.4 million, up 17% year-over-year versus 9% in fiscal 2024. Fiscal year 2025 revenue was $343.2 million, up 15% year-over-year versus 8% in the fiscal year 2024. Total ARR increased to $366 million exiting the fourth quarter, an increase of 17% year-over-year and $18 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. 44% of our customers now have multiple products with 74% ARR coming from that cohort.

The number of customers representing $100,000 or more of ARR in Q4 grew to 698, an increase of 18% year-over-year and up 45 customers since the last quarter, representing the largest sequential increase in this cohort in company history. Additionally, the number of customers representing $1 million or more in ARR grew in Q4 to 56, up 33% year-over-year, demonstrating our ability to land significant accounts and grow them over time. In period net dollar retention progressed to 105%, and led by cross-sell expansions across our customer base. 58% of Q4 gross ARR was driven by expansions across a broad range of customers with no individual expansion exceeding $1 million. It’s still driving meaningful progress in that dollar retention. We will continue to focus on driving net dollar retention higher through our platform strategy.

Gross margin was 77% for the fourth quarter, flat to fourth quarter of 2024 and up 1 point since last quarter. We continue to make progress on optimizing our hosting, driving multiproduct contracts and monetizing our services engagements. We will continue to look for opportunities to incrementally improve gross margin over time. Sales and marketing expenses were 42% of revenue, a decrease of 1 point from the third quarter. We continue to focus on improving sales efficiencies, driving improvements through our changes in processes, coverage and expansion of enterprise customers. At the same time, we are investing in future growth while balancing those incremental investments with efficiency gains. In Q1 FY ’26, we will have higher sales and marketing expenses as a percentage of revenue, reflecting timing of events and our annual company kickoff.

R&D was 18% of revenue, flat to the fourth quarter of 2024. We expect to continue to invest in the talent and capabilities of our team to drive greater innovation in the future. G&A was 12% of revenue, down 4 points for the fourth quarter of 2024. We expect G&A to improve as a percentage of revenue over time. Total operating expenses were $66 million, 72% of revenue, down 3 points sequentially. Operating income was $4.2 million or 4.6% of revenue. Net income per share was $0.04 based on 141.5 million diluted shares compared to net income per share of $0.02 with 135.7 million diluted shares a year ago. Free cash flow in the quarter was $11.2 million or 12% of revenue compared to $1.5 million or 2% of revenue during the same period last year.

In the fourth quarter, we managed our cash collections and made meaningful progress on shifting contracts with annual payments in advance. For the full year, we had a record free cash flow of nearly $24 million or free cash flow margin of 7%. 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. We have already purchased in the open market under our current buyback. And given the strength in our balance sheet and the underlying business, our Board has approved an additional reserve of $100 million to be used for buybacks. Our balance sheet position remains strong and allows us the opportunity to be more aggressive in our M&A strategy to accelerate our R&D road map when appropriate.

Now turning to our outlook. As a reminder, the philosophy of how we set guidance is through the lens of execution. We are confident we have the right strategy and the right platform to continue to consolidate the fragmented market. We continue to improve our go-to-market motion and are accelerating our pace of innovation. We have the right monetization strategy to encourage the adoption of our AI tools, and we believe those tools will reduce the barrier to adoption of our full platform, leading to greater monetization opportunities. Our strategy remains consistent with our go-to-market is being aided by our simplification of our pricing and packaging. 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, our monetization of data ingested in our platform and the cross-sell opportunities of new products gives us the right strategy to align the value of our customers receive with our growth opportunities and grow our business in a profitable way. For the first quarter of 2026, we expect revenue to be between $91.7 million and $93.7 million, representing an annual growth rate of 16% at the midpoint. We expect non-GAAP operating income to be between negative $4.5 million and negative $2.5 million. And we expect non-GAAP net income per share to be between a negative $0.02 and a negative $0.01 assuming basic weighted average shares outstanding of approximately 135.1 million. For the full year of 2026, we expect full year revenue to be between $390 million and $398 million, an annual growth rate of 15% at the midpoint.

We expect our full year non-GAAP operating income to be between $7 million and $13 million. We expect non-GAAP net income per share to be between $0.08 and $0.13, assuming weighted average shares outstanding of approximately 145.9 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 a Agentic analytics to the world. With that, we’ll open up for Q&A. Over to you, John.

John Streppa: Thank you, Andrew. [Operator Instructions] Our first question is going to come from the line of Taylor McGinnis from UBS, followed by Billy Fitzsimmons from Piper Sandler.

Taylor McGinnis: Maybe just first on — you announced a number of exciting agent offerings this week. And at the same time, you’ve also seen good traction with third-party agents connecting into Amplitude’s platform. So — and then Spenser, you showed a really good example of being able to extract insight using Anthropic Claude. So I guess how do you see Amplitude’s agents and these third-party agents evolving? Maybe you just talk about the differentiation that you anticipate with Amplitude’s agents versus what’s being done with the third-party agents today.

Spenser Skates: Yes. So to be clear, they both use the same underlying infrastructure. What will happen with either MCP model context protocol is a way for external products like Claude or OpenAIs, Chat GPT or Cursor to connect into Amplitude and request a set of calls. But that is the same infrastructure that both our global agents and our specialized agents use. And so the way to think about it is there’s a whole set of tool calls that are available to these agents. You can say, get me a list of events, get me a retention, get me the list of tools you have like retention and funnels, get me the possible properties for this event. And what we’ll do is we’ll expose that to an orchestrator that we have that basically interprets a query, whether it’s in the chat with Global Agent or whether it’s external from MCP.

And then it’ll kind of pull in all the different contexts I talked about and then spit out the answers that you see. So it’s the same underlying infrastructure because the nature and type of questions are the same whether you’re asking it from Claude or Slack or whether you’re in Amplitudes UI.

Taylor McGinnis: Perfect. Awesome. And then Andrew, maybe just one follow-up for you. If we look at the 4Q numbers, it looked like the upside in the quarter was a little bit lighter than what we’ve seen in the past. Now ARR continued to accelerate. So was that just a function of the quarter being back-end loaded? Or anything to flag in terms of the quarter may be in any areas coming a little bit below as expected?

Andrew Casey: So first, I’d say, Q4 was a great quarter for new logo ARR. We had a lot of new customers that are getting value from Amplitude and they’re starting their journey with us. Those tend to be ones in which you’re working throughout the quarter, and there was a large proportion of ARR that was booked later than we’ve seen in prior quarters. And as we mentioned before, we didn’t see a lot of really big expansion during the quarter. So it was one of those areas where you’re building a lot of opportunity for future growth with these new customers. And it’s always one you’re competing for — when you’re going into a new logo, you have to really compete and show value and sometimes those take a little longer as well. But we’re really pleased with all the new customers that have become Amplitude customers, and we think that, that sets us up very well for expansions in the future.

John Streppa: Our next question will come from Billy Fitzsimmons from Piper Sandler, followed by Rob Oliver from RW Baird.

William Fitzsimmons: I guess maybe to start, can you help us think through the NRR improvement? And how much would you contribute or attribute, I should say, to greater upsells and cross-sells versus maybe better — more success in kind of mitigating some of the churn in the business?

Andrew Casey: Sure. So throughout the year, we’ve seen our customers increasingly adopting more and more of our applications in the platform. When we started off 2025, we specifically were training our sales team how to sell our platform when we’re introducing new capabilities. We acquired new capabilities, and we put those into our platform as well. So predominantly throughout 2025, the improvement in net dollar retention was related to our cross-sell capabilities. And as you were alluding to, in the past, we had situations where we were overselling capacity against analytics. And even with some customers increasing data, it wasn’t enough really to offset and contribute materially towards net dollar retention. Now that we’re past most of those capacity-related issues that we created for ourselves.

We’re starting to see customers and their data ingested into our platform contribute towards net dollar retention improvements as well. And so now as we think forward, and I’ve said in the past that we have full intention to continue to set up our customers and expand with our customers, introducing new innovation. We think that both vectors, both data ingested into the platform, meaning upsells as well as cross-sells will contribute to further improvements.

William Fitzsimmons: Makes sense. And I guess on that note, if I could sneak in one more. Can you give us a sense of how the role volume upsells will play in the FY ’26 growth algorithm, especially as you start lapping some of the contract rightsizing from the first half of last year.

Andrew Casey: So one of the things we talked about in the call was introducing our new pricing and packaging that is aligning not only to our enterprise motion but also towards the implementation of our new AI products. And in the past, I would say there were certain times where customers felt very leery about the amount they’d have to pay based on increasing data rates that we’re ingesting in the platform, meaning that those rates were so high that they wouldn’t be able to see the benefits associated with marginal incremental reductions in the cost of that data. Well, our new pricing and packaging structure rewards our customers now for adding more and more data into the platform so that they’re paying marginally incrementally less.

Now that doesn’t mean that it’s not going to contribute growth to Amplitude as our customers are getting greater value by ingesting more data in the platform. We believe it’s fair for us to have some of that fair exchange of value. But if you were going to ask where we are really focused on driving NRR and where that — the biggest benefit, it will be continued to show from those cross-sell opportunities, that expansion of our products because we want our customers to not fear adding more data. We want them to take advantage of implementing more data into our platform, and we want that to scale, especially as they look at longer-term contracts with us.

Operator: Our next question will come from Rob Oliver from RW Baird followed by Clark Wright from D.A. Davidson.

Robert Oliver: A follow-up there, Andrew, on the pricing and packaging question. So obviously, enterprises really like predictability. You guys have never been a seat-based model. So if you can just help us understand in the context of the new pricing model, clearly, it sounds like it’s driving more engagement, a cross-sell opportunity, less of a friction experience. But how does the buyer manage that predictability? And I guess the inverse of that would be, how do you get comfortable on the cost side with AI embedded in?

Andrew Casey: Yes. It’s a great question. We spend a lot of time working with our sales team and our customers and showing how, one, the instrumentation with the platform can have — give the great visibility into the data they’re ingesting within it. And we work with our sellers to help them better understand as the marginal incremental data into the platform, grows, how that then translates into the cost that we’re going to be charging to our customer. We’re encouraging to have that conversation as part of the sales process. It’s a kind of a gentler way of showing and working with the customer on how they are going to adopt Amplitude over a period of time rather than guessing what their data implementation of the platform is going to be.

We’re working with them very closely on it and showing how the instrumentation works. Now the piece that I think is really important, and you touched on it, but I think it’s — we did a lot of work with customers to understand whether we had the right billing metric, whether it’s something that they aligned to the value proposition. And we’ve been testing for quite a while. In fact, nearly 20% of new ARR that we booked in the quarter was actually using our new pricing and packaging in a pilot stage. So we already know that customers like this. We already know that customers look at it as more transparent. They look at it as less friction as you were saying, we also believe it positions us very, very well given that our focus on implementing AI products into our platform is, one, it’s reducing the barriers to adoption.

Meaning that customers walk away thinking they’re getting great value of what they’ve already invested in Amplitude and are less fearful knowing that they have greater cost predictability and transparency and how that usage is going to trend over a period of time.

Robert Oliver: Great. Super helpful. And then Spenser, 1 quick one for you. Just on InfiniGrow, you guys were very early to the AI acquisitions among our coverage, I think been very aggressive on it. And in particular, it looks like to us like this gives you guys a further opportunity to sort of go for that consolidation play that you guys have talked about. But if you could help us maybe understand what, in particular, what area or what response to what customers need InfiniGrow is going to help address and how that might accelerate that platform opportunity.

Spenser Skates: There were 2 big things that stood out to us on the InfiniGrow team. I mean, so first, we’re just always looking for great talent out there. And so when the right company and the right opportunity comes along and they are aligned with our vision and excited about it, we’re going to act. With InfiniGrow in particular, there were 2 big things that stood about the team. So Daniel, the CEO there as well as the rest of the group, they’ve been in it on AI analytics and automating workflows for the last few years and have a ton of perspective on how the future of the category is going to be shaped. And I mean we’re in uncharted territory. Like we’re inventing something new here, AI analytics. And so whenever you get a chance to partner with someone else who’s thought about that so deeply, it’s a huge deal, and we’re going to — yes, we want to figure out how we can set up a way to work with them.

So that’s the first one that really stood out about InfiniGrow. The other piece that stood out is they have a lot of familiarity with analysts more on the marketing side versus product management. And particularly as those personas merge over long term and more customers from legacy MarTech tools want to come off and use something bleeding edge like an Amplitude, we want to make sure that we’re ready to meet them and serve all their needs and help with that transition. And again, they know a lot of those buyers better than almost any other company that we’ve seen in the analytics space out there.

John Streppa: Our next question will come from Clark Wright from D.A. Davidson, followed by Koji Ikeda from Bank of America.

Clark Wright: You noted the cross-selling opportunities continue to be an area of strength, what is the natural pathway you’re seeing in terms of product adoption? And what is the role that agents are going to play going forward to help drive additional cross-selling motions?

Spenser Skates: I mean it’s great on both fronts. So analytics is the core. We’re an analytics platform, something we’ve been very consistent about. You want to be able to track the core — the base — the foundation of the user journey. And that makes every single other part of the platform more valuable. So it makes it easier to do experiments because you can target users as well as measure those more effectively, it makes it better do session replay because you can understand, hey, for a group of users that ran into this error, let me see what they did by looking at the session replays. It makes guides and surveys better because you can target guides to specific users based on if you see them confused. So analytics is the core and all of these — they become more valuable with analytics and vice versa.

In terms of agents, I think the big opportunity there, and I just showed the session replay one is that these other products are — while we launched AI analytics yesterday, and that was the main focus. These other products are actually capable of being leveraged by AI. So the session replay specialized agent demo that I shared earlier is a great example where you can watch 1, 2, 3, maybe 10 session replays, but watch 100. I’ll take you a few hours to get through them. And so to have an agent speed up that analysis, still get all the valuable data, summarize it up and kind of put it back to you. I mean that’s you’re talking a 100x fold increase in productivity versus what you might other do. Experiment is the same thing. I mean one of the things that people ask us a lot is like, cool, do you have a library of best practices for what sort of web pages or what sort of interactions work and what don’t?

And our experiment conversion agent will actually suggest those based on best practices of what we know from all the companies that we work with. And so it makes experimentation a lot more powerful, too. So — and then the really cool moment is when these tie together. So you can start out in analytics and say, okay, cool, give me my unhappiest users and suggest ideas for that I could do to improve them? And then it says, “Wow, okay, all of these users, they’re unhappy because they ran into a page that wasn’t working, and then you could have a session replay agent come in and say, okay, well, let’s look at what was it on that page. It’s like, oh, okay, hey, this button isn’t formatted correctly and wasn’t labeled and so that’s probably confusing to users.

And then you can — and then go even further and say, okay, great, let’s run. Can you propose a variant, an experiment variant to me that would actually fix it? And then it will propose it and propose another web page and you can run the test. And so you can not know anything about analytics, not know anything about your data taxonomy, not know anything about how to use session replay, not know anything how to do experimentation AB testing and do all the work of all of those projects — products from the Global Agents or specialized agents interface. So I just — it’s going to be a massive unlock in terms of the usage. We’re obviously most focused on analytics right now, but I’m really excited about some of the other things [ and a lot so ] it’s funny.

We already got some comments on Twitter that are like, hey, why does it only watch 100 sessions at a time? Why can’t you watch 1,000 or 10,000 like, all right, we’re working on it. We’re working on it. So.

Clark Wright: Appreciate that. And then, Andrew, there’s a reference to increasing win rates versus point solutions. Is that an output of the go-to-market changes as well as the pricing and packaging updates? Or are there any other factors that is helping to drive improvements in that metric?

Andrew Casey: I’d say the pricing and packaging is relatively new. So I wouldn’t attribute that necessarily increasing win rates. I think that the biggest thing is, one, our sales team has just worked really hard at demonstrating value of our platform to our clients, and that’s really resonating. And the other is you really have to credit our product team for creating just really great products that work well together. A lot of people claim they have a platform, but the reality is it’s a bunch of products that’s stitched together, doesn’t look really well. When you have a platform, you have workflows that are instrumented well and it’s easy to interact with the different modules in the product. And that’s the way I would characterize our platform today.

And every time that customers are adopting more than 1 product, it’s because they’re — that integration, those workflows seamlessly across our platform are coming through as real value. I mean I talked to a number of customers myself with the sales team, and they always come back and say, we’re just so far ahead of what everybody else is even representing an analytics platform to be.

Spenser Skates: On that, like if I just go through the last 30 buyers I’ve talked to in the last month, all they want to do is be educated about analytics. And sorry, how AI is going to transform analytics and the whole platform. And they see it coming. They see tons of automation ahead and they’re like, hey, teach me how I can be relevant. And so when we can offer that to them by, one, providing a view on how the future unfolds and then two, offering them the products, tools and services that actually enable them to be successful and relevant, they want to spend a ton of time with us. And so the competitive question especially against the smaller point solutions is kind of going away. It really is just is now the right time and can you help me get to this future fast enough and teach me.

Operator: Our next question will come from the line of Koji Ikeda from Bank of America, followed by Jackson Ader from KeyBanc.

George McGreehan: This is George McGreehan on for Koji. Taking a big step back, one for me on kind of the big picture. Where can we expect Agentic queries to grow to become in the mix from 25% today, maybe over the next 12 to 24 months?

Spenser Skates: Yes. I mean I — none of us have a full crystal ball, but my expectation is the vast majority are going to be done Agentically, where you’re just going to have agents that run over your data all the time. They’re looking at dashboards. They’re looking at KPIs, they’re trying to find underlying root causes of why things are changing. They’re creating suggestions for your product. They’re reviewing session replays. They’re constantly trying out and tweaking new experiments. So I mean, yes, like I don’t want to put a number out there, but I think the vast majority. I think what we’re seeing generally is if you look at query growth from direct usage of the Amplitude dashboards, it’s increasing in line roughly with the size of our business.

If you look at Agentic query use, it’s skyrocketing, as you saw on that chart in the last few months. And so the amount of leverage, I think the same thing happened to coding in the last 2 years, where if you look at it, the best software engineering teams, the majority is produced by the majority of lines of codes are produced by agents. It’s mostly humans editing, interpreting them, stitching them together. And kind of giving high-level direction and that’s where the best software engineers are. I think the same thing is going to happen in analytics and data analysts, where the vast majority of the data munging of the tool and figuring out what query means what thing? And how do you get to a — how do you do a segmentation, understand the root cause, like that’s all going to be automated by agents.

And our goal is to be the first company to do that in a big way.

John Streppa: Our next question will come from the line of Jackson Ader from KeyBanc, followed by the line from Scott Berg from Needham.

Unknown Analyst: This is Nate Ross on for Jackson Ader. So implied non-GAAP operating margin for 2026 is roughly 2.5%. I guess we were wondering what specific possible sources of upside do you guys see for that number?

Andrew Casey: Well, I’ll tell you, first and foremost, we’ve been on this path where we’re increasingly driving revenue growth faster than we’re driving expense growth. And rooted within that is efforts on changing our go-to-market, changing our processes, modernizing our own application architectures, doing the basics of running the business in a very efficient way such that we continue to go drive growth with leverage. Those same things are not just a onetime event. You continue to focus and learn and understand how you can drive and deliver services more effectively. And so we look at the path we have in front of us with respect to our growth opportunities, what our pipelines look like, how we’re managing our cost to serve with the expectation that sales and marketing will continue to improve on their efficiencies, G&A will continue to drive efficiencies as well. So it all kind of culminates in the plan that we put together for 2026.

Unknown Analyst: Great. Perfect. And I guess, one more follow-up. So regarding customers’ analytics budgets. Have you guys noticed any trends or changes specifically with AI affecting these budgets? Like has the current AI landscape affected customers’ propensity to invest in analytics in any way?

Spenser Skates: Yes. Yes. So I’d call it two things. I mean one, it becomes — it’s the bottleneck, right? So you remember that [ lumen ] I showed at the start, where it’s like, okay, you’re shipping all the software. Is it good? Are we even going in the right direction? So the comparative value of the analytics piece becomes a lot greater and more higher urgency. When you have like a year-long road map, it’s okay if it takes a while to measure the success of it. But when your iteration cycle is measured in weeks or days like it is with the best of the best companies now, it’s like, yes, you need to know if you’re going in the right direction all the time. And then I think the other thing is the buyers in addition to that being the pinch point for and the big need in terms of the next big step in product development, they know — they intuitively all know that this whole space is going to get reformulated with AI.

And so they — again, they’re just desperate for education and someone to show them the way. This is not a case of like — I think one of the differences between selling SaaS and selling AI is, in the SaaS world, it’s very much like, okay, talk to your customers, get a list to prioritize features from them and build it and you go back and sell it to them. In this AI world, they don’t know. Like they are like, is this model capable of this? Can it automatically look at a session replay for me? Can it analyze the root cause of a breakage in my funnel? And how — what’s the best way to make that happen? And so they’re looking at us for all those questions. And this is where sharing the vision of what the future is as well as being close to the leading edge of the technology is super important.

John Streppa: Our next question will come from the line of Scott Berg from Needham followed by Nick Altmann from BTIG.

Ian Black: This is Ian Black on for Scott Berg. With the new pricing and packaging, are you planning on separately monetizing your AI agents?

Andrew Casey: So most of our AI agents are embedded within our core platform. And so what you see there is we’re getting access to customers to utilize more of the platform. That exemplifies all the power of our modules together. So there’s a high propensity that customers who are utilizing our AI agents are both ingesting more data into the platform as well as expanding into other modules. Now we’re also going to introduce new products with continuing — we’ve done really well at innovating and some of those products will come out with more fee charges as well. So we’re not worried about the ability for us to monetize our AI capabilities. We’re actually very excited about the opportunities as we expand the use cases and usage of our platform.

Ian Black: Congratulations on the good quarter.

John Streppa: Our next question will come from the line of Nick Altmann from BTIG, followed by Elizabeth Porter from Morgan Stanley.

John Gomez: This is John Gomez on for Nick Altmann. With the shift to Agentic democratizing the end user and product analytics, can you just talk about whether you’re seeing new users or new lines of business leverage Amplitude and how that’s shifting to go to market. So just any commentary on new end users and how that’s impacting how you think about the go-to-market strategy would be helpful.

Spenser Skates: It’s not really new end users. I mean it’s the same. You’re talking about product teams. You’re talking about marketing teams, engineering and data teams. And so it’s the same people trying to leverage the data. What they’re really — again, what they’re really desperate for is education. And so when we can show them Global Agent, specialized agents, MCP, AI feedback, AI visibility, what we’re doing with our next products and Assistant and LLM analytics, there’s always a whole bunch they want to grab on to and say, okay, great, teach me, how to use this, make it successful, everything else. So that’s the biggest difference. It’s one where our go-to-market is — it’s about training, getting them to be able to educate, to be able to share the vision, to be able to demo these products and make customers successful.

John Streppa: Our next question will come from the line of Elizabeth Porter from Morgan Stanley, followed by YC Wong at Citi.

Lucas Cerisola: I’m Lucas Cerisola here for Elizabeth Porter tonight. So with the uptick in new app development that we’ve seen over the past few months, could you walk through your expectations for balancing this potential new demand from smaller customers with your move-up market as you evolve your go-to-market strategy?

Spenser Skates: Yes, we’re doing both. I think one of the things we see on the start-ups and newer customers is they’re very bleeding edge, and so they’re trying to push the capabilities of us. And so we’ve always had a motion where we’ve taken innovation that we’ve done with them and bring it to the enterprise in a deliberate way. So we’re going to continue to do that. I think there’s actually massive opportunities, particularly with the rise of Vibe Coded apps. There’s going to be Vibe-Coded analytics, too, that needs to go along with all those applications. So it’s early, but there’s a big opportunity for us there, too. Again, the core thing you want in terms of understanding your customers and knowing if you’re going in the right direction and building the product is the same, whether you’re — the newest start-up that was just founded yesterday or your 100 year-old long-lasting business.

And so for us, it’s like we’re here to serve all of them. And again, they’re very keen on learning the bleeding edge of what’s happening in AI analytics. And so if you’re able to teach them that, then it doesn’t matter your size.

Lucas Cerisola: Got it. That’s super helpful. And then could you speak to the 7-figure deal pipeline in 2026? And then are there any specific verticals in which you see outsized growth already?

Spenser Skates: I mean we’re seeing — like as I said on the call, we’re seeing a lot of AI companies use us. We have 25 over $100,000. And then we have a 7-figure contract with one of the largest foundational model labs out there, who’s been a customer starting last year. And so that’s very, very exciting because they obviously know what’s going on when it comes to what’s possible, and they see a future world where we’re a really big part in that.

John Streppa: Our next question will come from YC Wong from Citi, followed by our last question from Arjun Bhatia at William Blair.

Yitchuin Wong: Congratulations on a pretty strong close to the year here. I guess maybe I just want to touch on, Spenser, you talked about Amplitude being one of the largest database of user behavior. Just given the rapid progress of agentic capability across our data platform players called Snowflake and Databricks, where we are seeing also customer consolidation towards maybe bigger data platform players. Curious if you’re seeing any — I think customer blur of like your analytics use cases from Snowflake and Databricks versus Amplitude?

Spenser Skates: I want to make sure, YC, I want to make sure I understand what you’re saying, you’re saying do we see competition from Snowflake and Databricks because they have a lot of data too?

Yitchuin Wong: Well, it’s not just data. They are thinking about doing the application side as well. I mean if you think about Vibe Coding and then you think of application building, you can make it easier to build. I’m just curious if you see anything blurring between customer talking about just use cases between a customer you can use a data platform like Snowflake to build it, they have Cortex versus what you will see with Amplitude?

Spenser Skates: So one of the big things that we see is that customers always want the most advanced and bleeding edge capabilities. Like I heard this great analogy the other day where software is very much like sushi. So it’s fine that the gas station at 7-Eleven offers it, but Jiro in Japan is not — probably not going out of business. In fact, it’s going to create more demand for him. And so from our standpoint, what we think about is how can we offer the most advanced and robust system for analytics. So if you look at the benchmark that we — with hundreds of evals that we released, where we got a 76% accuracy rate, if you look at the Cortex or you look at DataBricks Genie, I mean they’re going to be in the 10% or sub that.

We’re working on releasing full metrics on that. And that’s because the text-to-SQL is only really one part of it. The other — there’s 2 other big parts. The first is the context layer. So what data sources are you bringing together in the right way, analytics data, session replay data, data from interactions and guides and survey data from other sources and interpreting those in the right way and then giving an LLM agent, the right set of tool calls so that they can iteratively query, okay, hey, what’s my onboarding funnel? Where is the biggest drop on it? Why is the biggest drop on it? What’s the biggest difference between users who went to the next step versus the previous step, right? So that’s like just in that example, that’s 4 queries that you’re going to have to do in a row all correctly.

And to do that, you need to prop the LLM in a very particular way. You need to give the right tool calls, you need to give it the right context. And so we have thought really deeply about because we have the largest repository of user behavioral data in the world, we have thought very deeply. We have seen what millions of analytics queries, what good looks like for millions of analytics queries and translated that into an agent that does the same. And so because, again, you’re going to need to give it all that context and then be able to iteratively query a data system, the differences in accuracy are really, really stark if you were just to roll your own or use a Genie or Cortex versus using an Amplitude. And when you’re an analyst, that difference between 76% and 10% is a massive difference in terms of your ability to leverage agentic analytics.

Yitchuin Wong: No, that’s helpful color. Maybe one for Andrew. The profitability definitely came in well ahead of expectations here. Maybe you guys are leveraging some agents internally that helps to drive better sales efficiency. But curious to see going into next year, like what can we expect, especially on the free cash flow that outperformed probably saw an expansion by 4 points. Like curious to see what your expectation into next year? And any other moving parts that we should be aware of or headwind from to be mindful from the strong performance this year?

Andrew Casey: I think what you’re seeing is that the efforts we’ve been doing on sales and marketing, on our cost to serve and our G&A and operating more effectively as a company, is not just a one effort, one activity. There’s multiple. And certainly, we’re introducing agentic capabilities into our own workflows within the company, and that’s certainly contributing to it. But there’s so many things structurally we’ve done to the business to create greater durability that that’s ending in greater abilities for us to drive efficiencies. I’ll give you one example. We’ve talked a lot about our ability to go drive increasing contract duration to our customers and that our RPO has been growing rapidly. Well, if you don’t have to renew your installed base every year or that installed base percentage goes down because you’re executing more and more longer-term duration contracts with your customers, then the sales team has more time to dedicate towards selling new and expansion deals rather than working on renewals.

And so it’s just a great example of a strategy we put in place that’s going to accrue benefits for a longer period of time.

John Streppa: And our last question is going to come from Arjun Bhatia, William Blair.

Willow Miller: I’m Willow on for Arjun Bhatia. Andrew, in terms of guidance, the full year revenue range seems a bit wider than normal at $8 million. Can you help us understand the reason for this? And what scenarios are contemplated at the low and high ends of the range?

Andrew Casey: I think when we approach we approach our guidance, we approach it with what we think we can go execute in the period. And I wouldn’t read too much into that other than we have a breadth of different opportunities that we’re going after both with our product set, with improvements in our targeting enterprise customers. So I wouldn’t read too much into it.

John Streppa: And that will conclude our fourth quarter earnings call. Thank you for your time and interest, and we look forward to seeing you on the road this quarter as we attend conferences hosted by Baird, Citizens, KeyBanc, Morgan Stanley and others. Take care.