Amplitude, Inc. Q1 FY2026 Earnings Call

Good afternoon, and welcome to Amplitude's First 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, our Chief Financial Officer. During today's call, management will make forward-looking statements, including statements regarding our financial outlook for the second 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.

Good afternoon, everyone, and welcome to Amplitude's First Quarter 2026 Earnings Call. Today, I'll cover three things. First, our Q1 results; second, how AI is reshaping the software development life cycle; and third, a deep dive into our latest AI products and the customers putting them to work. Let me start with the numbers. Q1 revenue was $94 million, up 17% year-over-year. Annual recurring revenue was $374 million, up 17% year-over-year and up $9 million from last quarter. Non-GAAP operating loss was $3.1 million. Customers with more than $100,000 in ARR grew to 727, an increase of 18% year-over-year. Our progress in expanding the enterprise and growing our multiproduct footprint continued in the first quarter. Dollar-based net expansion improved sequentially to 106%. This reflects continued strength in our core business as we expand the capabilities of our platform to help the next generation of builders understand, improve and grow their digital products. I am focused on aggressively transforming Amplitude into an AI company. In Q1, we made broader changes to the leadership within go-to-market to remove layers and become a more technical team. Nate Crook is now our Chief Commercial Officer, overseeing sales, customer success, revenue operations and enablement. Nate and the team now own the entire path from landing a customer to ensuring they succeed long term. We restructured customer success and marketing to match customer buying trends. Customer success now has fewer handoffs and deep technical coverage with forward-deployed engineers. Marketing is now oriented around AI-native storytelling. We welcomed Gab Menachem as Chief Product Officer last month. Gab is a serial founder who built Loom Systems, an AIOps company acquired by ServiceNow. Loom Systems analyzed log data across cloud and on-prem, similar to what Amplitude does for behavioral data. Gab then spent six years scaling ServiceNow's IT operations management business to more than $1 billion in revenue. I'm excited about that combination of founder DNA and enterprise experience at scale. Gab is part of a growing group of founders we brought into Amplitude over the past 18 months to lead our AI transformation. A few weeks ago, we ran AI Week at Amplitude. We paused normal work across the entire company so that every function could build and shift AI-powered workflows to reimagine their daily jobs and functions. It is much more important for Amplitude's talent to be AI native over the next year than any short-term initiative in the business. The team shipped hundreds of amazing demos, including automatically creating custom demo websites per customer, automating part of the quarter close process and automating how we create new creative assets in marketing. Yesterday, we announced a strategic partnership with Statsig. As part of this partnership, Amplitude will take on Statsig's brand and customers. We will also maintain and develop the current Statsig platform across the cloud and data warehouse, including support for all existing Statsig customers. Amplitude will also begin building a more integrated roadmap for the future of Amplitude and Statsig platforms together. We will work closely with the Statsig team and with OpenAI during this transition. As context for the move, AI has dramatically lowered the barrier to building and shipping software, boosting productivity for experienced engineers and enabling nontraditional roles to become AI builders. While teams can generate more code than ever before, the software development life cycle remains bottlenecked in many other places. AI builders are generating code faster than they can understand its impact. The challenge is now evaluating code before it's released, tracking what's working after release, knowing when you need to roll things back, and turning behavioral signals into what to build next. Amplitude is the market leader and is focused on giving the best behavioral insights to product managers. Statsig has reinvented experimentation and feature management and done an amazing job with data leaders with its warehouse-native capabilities. Together, we can accelerate the software development life cycle. We now offer organizations access to the same capabilities that the world's most advanced AI companies use today. Initial customer feedback has been promising. Many of our existing customers have already expressed interest in the Statsig product. The pace at which Amplitude builds and ships products continues to accelerate. Over 90% of the code our team ships today is written by AI. I want to show you four quick demos today, each one reflecting a different dimension of what it means to close the product development loop. I want to start with Agent Analytics. Everyone building agents has one big question: are they working? With Agent Analytics, customers get complete visibility into every agent interaction, see every conversation's full thread, what the user asked, what the agent responded, which model was used, how many tokens it burned and how long it took to complete. Once the conversation completes, evaluators automatically run and judge your agent's performance across dimensions like user satisfaction, agent confusion, response quality and task completion. This happens on every conversation and is fully customizable so you can build evaluators specific to your use case. Then you can put all your Agent Analytics data together with your customer data. You can see how your agent's performance directly connects to real customer events, like what impact an original agent interaction can have on a customer later completing a purchase. We have been shipping faster around our Amplitude agents. We've added productivity updates to our Global Agent, including voice-to-text input for natural language prompting, image upload for providing deeper context, searchable chat history and conversation history across projects. In addition to that, we've also added memory, so agents now monitor when they're corrected or directed in specific ways and save that for the future. For example, a weekly active user in Amplitude is a user who saves a chart, not someone who simply logs in. After telling the agent this, it remembers it for future analysis instead of needing to be corrected every time. Ninety percent of these memories are automatically created as people use agents, so agents get smarter and better the more people use them. We also have MCP connectors built directly into agents. Agents are incredible at analysis, but the connection to action is often broken. A human still needed to file a Linear ticket or write up a Notion doc or read the Slack channel for context. Not anymore with MCP connectors. All of these actions can be triggered automatically. Non-event data can now be paired with Amplitude's data, connect financial information from BigQuery and quantify the real cost-benefit of an experiment, or with GitHub and Amplitude, retroactively track how specific releases affect error rates, session length or feature adoption. Now our agents can run and connect to all your data sources to surface insights and deliver the context wherever it's needed. This is exactly what one of our large financial institution customers experienced. They had agents running on Amplitude surfacing insights for a new interface rollout they were planning. One of those agents surfaced pages that were indexed incorrectly before they went live without being instructed to find the inaccuracies. That helped the team avoid serving incorrect data to customers without even being asked. That is what it looks like when the loop closes on the right side automatically. People don't check dashboards. The system catches the problem before it becomes one. We're also building new AI products to expand our platform for customers. Our most recent launch was AI Assistant. AI Assistant is a chatbot that answers customers' questions in real time, similar to Intercom. It's tied into Amplitude so it can know who users are, where they've been previously and where they are right now. If users want to know how to accomplish a task, instead of giving text instructions, it can create a visually guided tour that walks users through the interface. Here, I'm asking how to integrate with Slack and it's triggering a guide that helps me do so. It shows me where to click on the screen and guides me through the process. This is live for customers to purchase today and is a great way to highlight how we're using AI to infuse context and understanding of the user for our customers. The last demo I want to show with you today is our command line interface Wizard. AI builders need an automated installation of Amplitude. That is why we built the CLI Wizard. Setting up Amplitude used to be a sticking point for some users in the past. Now with our CLI Wizard, it's one line of code in the terminal. The rest is done for them. The CLI Wizard package runs against any code base, any programming language and it instruments Amplitude for you. It adds SDKs, creates the taxonomy and instruments all events and configures MCP. It will even create an initial dashboard for you. What used to take weeks now takes a few minutes; all initial setup in one action, a dead-simple install for humans. After this, we're going to give the ability for agents to install Amplitude automatically in the cloud. There is now no barrier to installing Amplitude. Let me tell you about a few customers who are putting this to work. Granola is one of the fastest-growing AI companies out there. They came to Amplitude before they had even launched because they wanted to understand from day one whether what they were shipping was actually working for users. Today, more than half the company uses Amplitude every day, and actually, I think everyone at Granola is an Amplitude user. They ship new features fast and rely on real-time behavioral signals to decide what to do next. They have grown with us horizontally and use the full platform. Granola is what a next-generation software company looks like. No separate analytics team, no weekly reporting cycle. The loop from ship to learn runs continuously and Amplitude is the infrastructure that makes it possible. Smartsheet is an intelligent work management platform that helps enterprises unite people, data and AI to turn strategy into results. As Smartsheet accelerated its push into AI-driven experiences, the team faced a real bottleneck. Their product managers were entirely dependent on the BI team for every single insight. A question as basic as how many people used this feature last month and what that means for retention could take weeks to answer. Today, with Amplitude Analytics, feature experimentation and guides and surveys, Smartsheet's product managers, engineers, designers and researchers have that answer instantly. They've used those insights to identify and fix drop-off in their onboarding funnel with a direct measurable impact on retention. As Smartsheet invests in AI, Amplitude gives them the velocity to understand whether new experiences are working at the speed their ambitions demand. Astra Tech chose Amplitude as its partner to support Botim & Botim Money's evolution into an AI-native fintech super app for over 150 million users across 150 countries. Botim uses insights from Amplitude to optimize fintech entry points, pinpoint critical journey drop-offs and establish clear engagement baselines for Botim AI across user segments, usage patterns and downstream actions. Cross-functional teams in growth, design and tech use those insights to steer a completely revamped Botim to reposition itself as a fintech-led communications platform. I'm very excited to share the business impact we had with them. Their revamp across services, including international transfer, local transfer, ad funds and gold, helped Astra Tech increase fintech service entries by 4%, lift engagement from top offers by up to 3%, and grow fintech transacting users by 3x. That happened all within a span of nine months of working with Amplitude. I want to note that the companies on the bleeding edge of the tech industry are Amplitude customers. That's because the faster you build, the more you need to know what to build next. AI natives understand that better than anyone. This underscores the long-term case for Amplitude. AI makes what we do more critical than ever. We are set up to close the right side of the product development loop, and we have the platform, the customers, the leadership and the conviction to see it through. I'm extraordinarily excited for what's next. Now over to Andrew to walk you through the financials.

Thank you, Spenser, and good afternoon, everyone. The first quarter was solid with incremental improvement in our dollar-based net retention to 106%, multiproduct accounts for more than 77% of our ARR and our ARR growth was 17%. We beat our guidance on both top and bottom line, and we are combining the best of Statsig with Amplitude. Reflecting on Q1, there were many changes in our go-to-market team. We've introduced a number of new AI products, and Amplitude has been implementing a host of new AI-based workflows to drive efficiency. We are in a moment of transformation. We are transforming the value our customers receive. We are transforming how we deliver value, and we are transforming our organization from the ground up. We've done this while continuing to execute on our core business. We are leveraging AI at scale across our organization and helping customers unlock incremental value faster. No longer is a good piece of code with a friendly UI good enough. We must deliver customer-valued outcomes. We are focused on becoming a true partner with our customers to understand how to apply technology in the most effective ways. We are building on a decade of understanding context and delivering this knowledge through our services, our platform and our know-how. The speed of change is accelerating, and we're leaning into that moment. We're seeing increased usage of our AI agents along with data ingested into our platform. This has created some headwinds in our cost to serve, but it's also aligned to our monetization strategy. Adapting quickly and delivering greater value to our customers will be the advantage of the next generation of winners in software, which is why we've made changes to our products, pricing and internal operations. Taking on the Statsig business is another great example of our ability to be flexible and act quickly. By combining Statsig's industry-leading warehouse-native experimentation with Amplitude's best-in-class analytics platform, we're expanding our total addressable market and meeting customers where their data needs are. We will build this business to be incremental and accretive to our core business. Spenser highlighted some of the changes our team has undergone, and we're instrumenting the business for long-term scale and efficiency so that driving business growth continues to result in greater leverage. That being said, our goals as a business remain steady. We want to grow our enterprise business, expand our multiproduct footprint and deliver great value for our customers. This focus has enabled us to drive consolidation in the market through our platform approach, now having over 77% of our ARR coming from customers with more than two products, up three points from last quarter. Customers with five or more products now account for 24% of our ARR, up from 20% last quarter. We believe that as customers continue to adopt our AI products, they will naturally expand their use cases into the full suite of our platform and drive incremental upsell opportunities. Turning to our first quarter and full year results. As a reminder, all financial results 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 can be found in our earnings press release and supplemental financials on the Investor Relations page of our website. First quarter revenue was $93.5 million, up 17% year-over-year versus 10% in the first quarter of 2025. Total ARR increased to $374 million exiting the first quarter, an increase of 17% year-over-year and $9 million sequentially. Total remaining performance obligations grew 31% year-over-year to $427 million compared to 30% growth in Q1 2025. Current RPO was up 20% year-over-year compared to 18% in Q1 of last year. Long-term RPO was up 60% year-over-year compared to 72% from the first quarter of last year. We had a strong quarter for both new and expansion deals in the enterprise. Platform sales were also particularly strong. Forty-seven percent of our customers now have multiple products with 77% of ARR coming from that cohort. We have made great progress on expanding our multiproduct footprint within our customer base compared to a year ago when only 30% of our customers had multiproducts and accounted for only 64% of our ARR. The number of customers representing $100,000 or more of ARR in Q1 grew to 727, an increase of 18% year-over-year and up 29 customers since the last quarter. In-period net dollar retention increased to 106% from 105% last quarter, led by cross-sell expansions across our customer base. We expect net dollar retention to improve over the long term as we continue to see customers adopt multiproduct. However, it may not be in a linear fashion. Gross margin was 75% for the first quarter, down two points from the first quarter of last year. This was largely driven by growth in inference costs as adoption of our AI tools by our customers outpaced our expectations. We now expect this adoption trend to continue given the feedback we received from our customers. In the short term, this will cause gross margin compression, but we believe this will help us to drive greater data ingestion and monetization of our core platform over time. Sales and marketing expenses were 45% of revenue, in line with the first quarter from last year. Some of the increase in costs included severance costs related to our organizational changes and other activities like our go-to-market kickoff that occurred in the first quarter. We have focused our entire go-to-market team on driving value for our customers, increasing adoption organization-wide and improving our internal processes, coverage and expanding the buyer personas that we can sell to. These changes will take time to manifest in net new ARR, but ultimately, they will increase the health of our customer base and drive greater opportunity to grow our net dollar-based retention. R&D was 20% of revenue, up one point from the same period last year. We will be adding to the team to scale the Statsig opportunity and continue to support those customers. G&A was 13% of revenue, down two points from the first quarter of 2025, and we expect G&A to improve as a percentage of revenue over time. Total operating expenses were $73 million or 78% of revenue, down one point from the same period a year ago. Operating loss was $3.1 million or 3.3% of revenue. Net loss per share was $0.02 based on 133.3 million basic shares compared to a net loss per share of $0.00 with 129.7 million shares a year ago. Free cash flow in the quarter was a negative $13.2 million or negative 14% of revenue compared to a negative $9.2 million or negative 12% of revenue during the same period last year. We continue to be active in the open market last quarter, retiring shares against our open buyback. 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 while our share price continues to not align with the value we believe we're creating. 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. In Q2, we will also take into consideration bringing the Statsig customers and technology over to Amplitude as of the beginning of May. To start, we will record an additional $16 million in incremental ARR from the Statsig customer base, aligning that business to our definition of ARR. As we take on the Statsig business, we will also be investing in the transition team as we ramp an internal team to continue to provide the best support for the Statsig customers. Over time, we will scale our internal team to continue to develop the warehouse-native and cloud aspects of Statsig. Additionally, there will be some pressure on gross margins for the remainder of the year as we integrate and optimize our hosting environment. 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 progression on driving adoption of our core platform, our different AI technologies and multiproduct adoption. Our new pricing and packaging rollout is progressing very well. And in the first quarter, 25% of total ARR contracted, both new business and renewals, was on our new pricing and packaging. We will continue to increase this percentage as we make it easier for our sellers to quote and make it easier for our customers to understand the path to platform adoption. We are already seeing early signs of willingness to test new features and products on the platform given the easier on-ramp from a contract view. This will also lend itself to allowing easier adoption of our AI agents as we continue to iterate and ship. So, for the second quarter of 2026, we expect revenue to be between $96.9 million and $99.1 million, representing an annual growth rate of 18% at the midpoint. We expect non-GAAP operating income to be between negative $3.6 million and negative $1.6 million. We expect non-GAAP net income per share to be between negative $0.02 and negative $0.01, assuming basic weighted average shares outstanding of approximately 134 million. For the full year of 2026, we expect full year revenue to be between $397 million and $403 million, an annual growth rate of 17% at the midpoint. This assumes a $5 million to $7 million contribution from the Statsig business, taking into account the assumption of the customer contracts and the impacts to deferred revenue. We expect our full year non-GAAP operating income to be between $2.5 million and $6.5 million. This reflects incremental investment we'll need to incorporate the Statsig business. We expect non-GAAP net income per share to be between $0.03 and $0.06, assuming weighted average shares outstanding of approximately 145.1 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 Agent Analytics to the world. With that, we'll open it up for Q&A. Over to you, John.

Thank you, Andrew. We will now turn to Q&A. Our first question will come from the line of Taylor McGinnis from UBS, followed by Rob Oliver from R.W. Baird.

Maybe first, Spenser, for you. Could you just explain why OpenAI is foregoing the Statsig business? And if there's any parts that OpenAI is retaining in that? And then, Andrew, maybe a second one for you. Helpful color on breaking out some of the Statsig impact this year to the guide. If we strip that out, does that mean that you're taking down, I guess, the organic growth guide a little bit on revenue this year? And maybe you could just unpack that and the margin impact.

So, first, just to answer the question on the Statsig side. Vijay and I have known each other for years. Amplitude and Statsig have been competitors and have been pushing the bleeding edge in their respective niches. I'm extraordinarily excited that we get to carry a bunch of that forward with both the customers, the technology as well as the brand. I think Vijay was looking for a home for continued support of the Statsig customer base. After looking at a number of different places, he and I agreed that the best place would be Amplitude. We executed that agreement on Friday, so we're still getting up to speed with everything it entails and making sure it's a smooth transition, ensuring those customers continue to be supported and then figuring out the long-term plans for Amplitude and Statsig together. I'm very, very excited about it. OpenAI will be continuing to run the technology internally that they have from Statsig, and so they'll be continuing to use it, and that will be supported by Vijay and the existing Statsig team at OpenAI.

And Taylor, part of the guidance we have is incorporating the accounting associated with an agreement like this, where you have to take a fair value assessment on the revenue that's aligned to the annual recurring revenue I mentioned. By taking that fair value assessment, you actually take a haircut on the value. It reduces down. So what you're seeing in the amount I'm indicating that comes from ARR and the lower revenue is really related to that fair value assessment. We had a good quarter in Q1. We beat expectations. We beat what our guidance was, and that's flowed through into our guidance for FY '26. For us, we think it's a huge opportunity to build out a great product that a lot of customers will be very interested in.

Perfect. And just a quick follow-up, if I may. If I look at the net new ARR numbers, it looks like maybe it was flattish on a year-over-year basis. I know you mentioned that there were a number of changes that you guys made in the quarter from leadership changes to pricing and packaging. Did that have any impact in the quarter? And maybe you could talk about what occurred and how you guys are thinking about that metric for the remainder of the year?

Whenever you make big changes in organizational structures or you're making changes in core processes, invariably there is going to be an impact. We're pretty proud that given those changes we were still able to exceed the guidance we had put out with respect to ARR and turn in a pretty good quarter, especially with respect to net dollar retention increasing and the number of $100,000 customers we added. So yes, there's always going to be some impact, but we did a pretty good job of executing through it.

Our next question will come from Rob Oliver at R.W. Baird, followed by Jackson Ader at KeyBanc.

I apologize for background noise. I'm out in the wind here a little bit. Yes. So, I guess first question, Andrew, for you. Really great progress on the new pricing model. It feels like just yesterday you were in pilot on that, and now you're at 25% of ARR. I guess a couple of questions there to start. One, how should we think about the progression of that? I think you said it's key to the selling motion. But is that something as customers come up for renewal this year, we can expect that number to continue to move higher? And any, recognizing it's very early, any early indications on what kind of pricing uplift or impact it's having on the contracts in terms of the combinations of usage? And then I had a quick follow-up as well.

Yes. We'd say we're pretty pleased with our progress on pricing and packaging as well, Rob. The response from our sales team has been tremendous. They love the simplicity in the way they can actually express value back to clients. That proxy on value from a price perspective and the methodology is one that customers really understand. I can tell you there are a number of deals in Q1 where customers added more product associated with our platform because of the simplicity and the cost predictability on that new strategy. So I do expect the percentage of our ARR that's going to be on the new pricing and packaging will increase. We're not going to force customers through hard migrations. We're going to give them the carrot and show them the value, and we expect customers will want to adopt the new pricing and packaging.

Great. Helpful. And then my follow-up, Spenser. In your prepared remarks, you made it clear that being an AI company right now is the most important thing. That creates a ton of exciting opportunity like around Statsig. It also creates some uncertainty around both the gross and operating margin lines. Recognizing you just closed Statsig on Friday, can we expect at some point, perhaps this year, to get updated thoughts from you around cost to integrate go-to-market and potential further impacts both on the gross cost to serve side as well as on the operating margin side?

For sure. It's a few days old, so we did our best with the guidance we put out, but we'll absolutely have a much better picture as we get into next quarter and subsequent quarters. On Statsig specifically, I think it will be long term very accretive to the business. A lot of Amplitude customers are very interested in their product. Many Amplitude customers are also Statsig customers. We actually spoke with one yesterday, Atlassian, which is a big user on the Statsig experimentation side while being a big user on Amplitude Analytics. They're really excited because now the data from the two products will talk to each other, and that will drive more value and usage for both Atlassian and Amplitude. We expect to see similar things across the entire customer base. In terms of exactly quantifying them, it's early because it's only a few days old, but we'll have a much better picture come the next earnings call. In terms of AI generally, on operating margin leverage, it will be accretive. People get more efficient. We'll be able to get a lot more done with the same number of people and have two- to three-times the impact without proportionally growing the team. I'm extraordinarily excited about that. The mistake I see a lot of companies making is being too conservative on internal AI spend. I see companies with tiny budgets per person for AI, and that's nowhere near what's required to unleash full capabilities. Our top engineers are shipping five times the amount of pull requests, but they're also spending thousands of dollars a month or more on tokens. We're figuring out exactly how to budget and price it out, but we expect this will translate to operating leverage long term. The last thing to call out is inference spend. If users employ Global Agent to analyze an issue, that will cost tokens. For now, we said we'll support it and bundle it with our core offering because it drives more Amplitude usage. You can see that reflected in Andrew's gross margin numbers. That does put short-term pressure, but we expect it to be accretive long term, first to revenue growth and then to operating margin as it requires fewer people to support more customers.

Just one clarification to Rob: we did take into consideration the operating expenses associated with the Statsig business into our guide.

Our next question will come from Jackson Ader at KeyBanc, followed by Clark Wright at D.A. Davidson.

The first question I had, Spenser, is on the command line interface and the MCP server that you're turning live, making it frictionless to adopt and use Amplitude. But I'm thinking about enterprise customers where you're having forward-deployed engineers who are ostensibly trying to make sure you go hand-in-hand with customers and ensure adoption. Those two things — the frictionless CLI and the forward-deployed engineers — seem like they could be at odds. Can you explain?

Yes, a little bit different. I appreciate the question. The frictionless CLI addresses the long-standing manual friction of instrumenting analytics. Historically, setting up analytics required defining objectives, defining a taxonomy, adding an SDK and creating dashboards. There's tremendous opportunity to automate that with AI, and that's what we did with the Amplitude CLI. It's actually wild — three months ago this wouldn't have been possible. Now it runs against any code base and instruments Amplitude for you. The flip side is adoption. Many customers need education on how to adopt AI at scale inside their organizations. You can hand a bleeding-edge team a one-line CLI and they're off to the races, but traditional companies — for example, Fox Broadcasting or Walmart — want hands-on help to educate their teams. It's one thing to have software running; it's another to reskill hundreds or thousands of people, ensure correct instrumentation and integrate into existing workflows. That's where the forward-deployed engineers come in: more technical talent in post-sales to educate, implement and sometimes actually code alongside the customer. They help plug the system into the back end, ensure SDKs are in place, the events are instrumented, MCP connectors are configured and the right people get reports. So the CLI reduces initial setup friction, and forward-deployed engineers help drive adoption and change management for larger, more traditional customers.

Okay, that makes sense. My follow-up for both of you: you're shifting to an AI-first company, with many personnel and leadership changes, pricing and packaging changes, and now bringing in another product through a partnership. There's a lot going on. What is your plan to ensure execution risk doesn't bubble up with so many initiatives happening at once?

To be candid, I think many SaaS companies are being too conservative about change. We've chosen to move fast to where the market is going. We know from customers what they want and see the technological innovations; we want to run as fast as possible toward that future. That does mean it's going to be bumpy and chaotic at times, and there will be things we can't perfectly plan for. But speed matters more than trying to protect an existing thing that, frankly, isn't valued as highly as the future potential. My focus is on the long-term opportunity, generating billions of dollars in revenue in this new world. Whether it's organizational changes, product changes, pricing, partnerships with OpenAI and Statsig, we intend to be aggressive in reinventing the category. The same transformation that happened in coding over the last two years is happening in analytics, experimentation and session replay. It's a race to see who can do it the fastest, and that's where we're focused.

Our next question will be from Clark Wright at D.A. Davidson, followed by Scott Berg at Needham.

Any update on the ramp of events in the pricing curve that you've implemented to help enterprises scale usage previously and ongoing? Also, you noted TAM expansion with Statsig. Can you explain what budgets you're now going after and what consolidation this unlocks?

One of the things about our new pricing and packaging is we did a lot of testing and had a soft rollout this quarter. Initially it was more handheld because we hadn't implemented many of the systems for quoting. Today, that quoting capability is implemented and we're seeing great responses from clients. Enterprises appreciate the simplicity and cost predictability. From their perspective, as they add more events they're getting a marginal incremental cost reduction. For us, it still increases ARR as events increase. As our sales team becomes more adept at showing customers the value they'll get from Amplitude, the pricing and packaging changes will reinforce our ability to move at pace.

That's helpful. On the TAM expansion with Statsig, what budgets are you targeting and what consolidation opportunities are unlocked by bringing these products together?

Statsig has done two things really well. One is experimentation: bleeding-edge AI teams use them for experimentation tasks. For example, when you see prompt A versus B testing in some products, that's the sort of functionality Statsig powers. The second is working with data leaders and data warehouse architectures. They allow direct querying of warehouses and running experiments in a warehouse-native way. That's exciting because at the largest customers, functionality like that is often owned by the data leader and tied to data warehouse budgets. We're excited to get closer to those teams and unlock data-warehouse-adjacent budgets, and to expand our reach into customers who prioritize warehouse-native experimentation.

Our next question will be from Scott Berg at Needham, followed by Nick Altmann from BTIG.

Spenser, I want to ask an architectural question. With pressure on gross margins, how have you thought about using open-source models or smaller language models within the Amplitude platform versus frontier models you're using today? There's a large private software company that announced an open-source model and new platform. What have you considered in that process?

We're early on this. Inference spend is growing and you see that in the operating and gross margin impacts. Generally, customers want the bleeding-edge models for higher accuracy. For some cases, downgrading to cheaper models would reduce accuracy significantly. Over the long term, we'll find places to use cheaper, more effective models where it makes sense. We're focused on winning the market first; optimization comes next. There are cheaper models, and some like Anthropic's models work well for many cases. The price-performance curve is improving rapidly — you see order-of-magnitude improvements year over year — so in 12 months things will likely look much better and we'll be much more selective about where to use which models to balance accuracy and cost.

Understood. Very helpful. Then, Andrew, on the Statsig business: when OpenAI acquired them, they were doing about $40 million ARR. You're bringing over $16 million of ARR. Are you implying the balance is staying with OpenAI? Also, did you pay anything for this business? Any allocation or purchase price details?

A couple of points to understand about Statsig prior to OpenAI acquiring them: OpenAI was a fairly large customer for them, and that was a substantial portion of their ARR. OpenAI's intention with Statsig is to use it for internal, noncommercial reasons and to continue supporting their core products. As we go forward, we are taking on the customer contracts — all of them — and taking on the brand assets. We will increasingly develop on the product itself and deliver great solutions for those clients and future clients of Amplitude. Regarding purchase price or other terms, we described this as a strategic partnership: nuances exist around the agreement structure, and at this early stage we focused our public commentary on the customer and product transition rather than detailed purchase-price disclosures.

One minor technical point: we're talking about Statsig as a partnership, not an acquisition. It's a nuanced distinction, but an important one.

Our next question will come from Nick Altmann from BTIG, followed by Arjun Bhatia from William Blair.

Andrew, we appreciate the color on Statsig contribution. There's overlapping customers and overlapping product sets. You've made an effort to consolidate customers onto Amplitude products. For customers using both you and Statsig, what does that imply for revenue assumptions? Any details you can unpack would be helpful.

We don't see huge overlap in products that customers are using from Statsig and us. Statsig is particularly strong in experimentation, and we often saw customers using Amplitude for analytics and Statsig for experimentation. So there's not a lot of overlapping revenue. It's actually an opportunity to consolidate onto a single platform and to add more to a customer's footprint. There's also a whole new group of customers that Amplitude now has access to via this partnership.

To add, Nick, it's not a perfectly clean overlap — each product was developed a bit differently and serves slightly different customer sets. There's a lot of opportunity across both customer bases. We're three days into the transition, so we're still sizing the exact overlap, and we'll have more detail on the Q2 earnings call.

Our next question will be from Arjun Bhatia from William Blair, followed by Billy Fitzsimmons from Piper Sandler.

I want to revisit the inferencing costs. Spenser, where is the AI usage coming in strongest? You've enhanced your MCP server — is that also driving meaningful changes in how customers use the Amplitude platform?

A lot of customers are using MCP heavily, and we're seeing huge usage of Global Agent and specialized agents. Those all hook into the same underlying services. For example, you can ask the system to find the root cause of an issue in a chart, which can come in through MCP, Global Agent (the chat interface) or specialized agents purpose-built for specific tasks. Combined, these are the bulk of the inference-driven usage and thus the majority of inference costs.

Got you. My second question is more philosophical: the software development life cycle is changing quickly, starting with code generation. What steps will be required before we see this change manifest strongly in analytics, monitoring and experimentation? Does the composition of software teams need to change? Is there more change management ahead before seeing a hockey-stick adoption in analytics and monitoring?

It's early. Within tech companies, adoption is already happening: automated instrumentation, automated analysis and automated product development flows. Many companies we work with, like Granola, already operate in this future and push us to build features like Agent Analytics. Adoption is earlier and accelerating in bleeding-edge companies. For non-tech companies, it's much earlier: they need education, a maturity model and help scaling adoption across many employees. For them, the near-term focus is making existing workflows much more efficient — for example, using the CLI Wizard or Global Agent to auto-generate dashboards and save time — rather than jumping straight to fully automated agent-driven software development. I expect substantial change in the next few years, but timing by quarter is hard to predict.

Our next question will be from Billy Fitzsimmons from Piper Sandler, followed by Koji Ikeda from Bank of America.

Spenser, I appreciated the commentary on new members of the C-suite. With Nate now Chief Commercial Officer, I want to double-click there. Any additional color on his plan or top priorities going in and expected changes to sales motions, sales incentives, partner strategies and general changes relative to what you've already done?

Thomas, our prior President, ran all go-to-market and did a phenomenal job over the last 3.5 years, upgrading us to an enterprise company. Nate was previously our Chief Revenue Officer and reported to Thomas for the last three years, so he's not new to the organization. The change is that Nate now runs the post-sales motion as well as revenue operations and enablement. We're streamlining to make the transition seamless between account executives and technical success managers. We've renamed customer success roles to technical success managers, removed extraneous roles and expect them to provide deep technical education and implementation guidance. We've also added forward-deployed engineers that can do actual coding work to help customers with instrumentation, SDKs, MCP connectors and integrations. That group began about a month ago and has been received well by customers. Ultimately, customers want someone to educate their organization on how AI will change analytics and related functions, and we're building that capability into our post-sales motion.

Our next question will be from Koji Ikeda from Bank of America, followed by Elizabeth Porter from Morgan Stanley.

A follow-up on go-to-market changes: are there any tweaks being made to sales incentive comp? And as an unrelated follow-up, I believe last quarter 25% of queries on the platform were coming from AI agents. Any update on that number and outlook?

On sales incentive comp, we've had incentives in place for platform adoption and multiyear deals and those continue. We'll tweak incentives each quarter based on priorities. Right now, a priority is ensuring Statsig customers coming over are well-supported, and we've put specific incentives around that. Regarding agent adoption, it continues to grow significantly. We are not sharing updated agent usage numbers on this call, but we will provide an update next quarter on agent adoption relative to human usage of analytics.

Our next question will be from Elizabeth Porter from Morgan Stanley, followed by YC Wong from Citi.

Spenser, you talked about seeing the puck as fast as possible. Help us frame where your incremental AI-related investments are going this year: infrastructure, experimentation tools, workflow automation — where is the puck going?

Specifically on expenses, the biggest is inference costs to support Global Agent, MCP and specialized agents, which show up in cost of goods sold and are growing significantly. We'll monitor it and determine the right long-term model for capturing value and monetizing it. Another big area is internal tooling for the team. We use Claude (Anthropic) and other tools heavily across the company for internal productivity, and that represents a material internal spend. We also use developer tools like Cursor and other assistants. So inference costs are the largest outward spend, followed by internal AI tooling and developer productivity tools.

And our last question will come from YC Wong from Citi.

Just a quick one for Andrew. Last quarter we talked about 25 AI customers crossing the $100k ARR mark. As you deepen integration with foundational models and tools like Cursor, are these AI customers exhibiting structurally different net retention, consumption patterns compared to traditional enterprise SaaS customers? How do you view the opportunity longer term?

We believe AI companies that standardize on Amplitude will continue to see greater value and will embed and use Amplitude to drive better products, marketing and insights. We're seeing in some larger AI customers increased data ingestion into our platform, and that contributed to the gross margin headwinds we discussed. Some of Statsig's customer base had a strong AI component, so we look forward to updating you in Q2 on how the combined product set and customers behave.

Thanks, YC. And that will conclude our first quarter earnings call. Thank you for your time and interest. We look forward to seeing you on the road this quarter as we attend conferences hosted by Needham, Jefferies, Bank of America and D.A. Davidson. Take care.

Awesome. Thank you, everyone.

Thank you.