Amplitude AMPL Q1 2026 Earnings TranscriptMay 7, 2026
Image source: The Motley Fool.
Date
Wednesday, May 6, 2026 at 5 p.m. ET
Call participants
Chief Executive Officer — Spenser Skates Chief Financial Officer — Andrew Casey Chief Commercial Officer — Nate Crook Chief Product Officer — Gab Menachem Head of Investor Relations — John Streppa
Full Conference Call Transcript
Spenser Skates: Good afternoon, everyone, and welcome to Amplitude's First Quarter 2026 Earnings Call. Today, I'll cover 3 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 analyze log data across cloud and on-prem similar to what Amplitude does for behavioral data. Gab then spent 6 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. Amplitude, as part of this partnership, Amplitude will take on Statsig's brand and customers.
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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 road map for the future of Amplitude and Statsig platforms together. We will work closely with the Statsig team at 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 4 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. The agent, after telling the agent this, it remembers it for future analysis instead of needing to be corrected every time.
90% 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 broken. A human still needed to file the linear ticket to the write up a notion 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 are 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 become 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 like Intercom Fin. 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 into one action, 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 actually, 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 Amplitude is a granola 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 use this feature last month and what does this mean 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. With cross-functional teams in growth, design, tech, using 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, Astra Tech increased fintech service entries by 4%, lifted engagement from top offers and 4U by up to 3% and grew fintech transacting users by 3x. That happened all within a span of 9 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.
Andrew Casey: 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 of 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 remains 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 2 products, up 3 points from last quarter.
Customers with 5 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. 47% of our customers now have multiple products with 77% 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 2 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 1 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 2 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 1 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 road map 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 first, 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 Agentic Analytics to the world. With that, we'll open it up for Q&A. Over to you, John.
John Streppa: Thank you, Andrew. We will now turn to Q&A. [Operator Instructions] Our first question will come from the line of Taylor McGinnis from UBS, followed by Rob Oliver from R.W. Baird.
Taylor McGinnis: Maybe first, Spenser, for you. Could you just maybe 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.
Spenser Skates: So, first, just to answer the question on the Statsig side. I mean, Vijay and I have known each other for years. Amplitude and Statsig have been competitors and kind of pushing the bleeding edge in their respective niches. I'm extraordinarily excited that we get to kind of 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 the kind of continued support of the Statsig customer base. And after looking at a number of different places, him and I agreed that the best place that would be Amplitude. We just executed that agreement on Friday.
So, we're kind of still just getting up to speed with all everything it entails and making sure it's a smooth trend, making sure those customers continue to be supported and then figuring out what the long-term plans for Amplitude and Statsig are together. But I'm just, I'm very, 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, but that will obviously be supported by Vijay and the existing Statsig team at OpenAI.
Andrew Casey: And Taylor, part of the guidance we have is incorporating the accounting associated 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. But by taking that fair value assessment, you actually take a haircut on the value. It actually reduces down. And 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. And I would tell you that 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.
So, for us, we think it's a huge opportunity for us to go build out a great product that a lot of customers will be very interested in.
Taylor McGinnis: Perfect. And just a quick follow-up, if I may. So, 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. So, did that at all have any impact in the quarter? And maybe you could just talk about what occurred and how you guys are thinking about that metric for the remainder of the year?
Andrew Casey: Whenever you make big changes in organizational structures or you're making changes in core processes, invariably, there is going to be an impact. I would tell you that we're pretty proud of the fact that given those changes that we made, we were still able to, one, 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, 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 kind of executing through it.
John Streppa: Our next question will come from Rob Oliver at R.W. Baird, followed by Jackson Ader at KeyBanc.
Robert Oliver: 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. I mean it feels like just yesterday, you guys 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.
Andrew Casey: Yes. I'd say we're pretty pleased with our progress on the pricing and package 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 that customers added more product associated with our platform because of the simplicity and the way they get cost predictability on that new strategy. So I do expect that 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 that customers are going to really want to adopt the new pricing and packaging.
Robert Oliver: 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. And I guess that creates a ton of exciting opportunity like around Statsig. It also creates some, a fair amount of uncertainty around both the gross and operating margin line. So just wondering, I know we've got updated numbers for you guys and thoughts around that.
But recognizing you just closed Statsig on Friday, can we expect at some point, perhaps this year, we'll get updated thoughts from you guys 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.
Spenser Skates: For sure, for sure. So yes, I mean, again, it's a few days old, so we did our best with the guidance that we put out, but we'll absolutely have a much better picture as we get into next quarter and subsequent quarters. Let me talk about Statsig specifically, and then I'll talk about more generally on the AI transformation of Amplitude. 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.
A lot of Amplitude customers are also Statsig customers, like we just talked to one yesterday, Atlassian, who is a big user on the Statsig experimentation side while being a big user on the Amplitude Analytics. And they're actually really excited because now the data from the two products will talk to each other, and that will drive a whole bunch more value and usage and good things for both Atlassian and Amplitude. And we expect to see similar things across the entire customer base. Now in terms of exactly quantifying them, again, it's very rough in the air because it's only a few days old, but we'll have a much better picture into it come next earnings call.
In terms of AI generally, it's absolutely going to be, so I think on operating margin leverage, it absolutely will be accretive. People get more efficient. We'll be able to get a lot more done with the same number of people. We'll be able to have 2x or 3x the impact without having to grow the team. And so, I'm extraordinarily excited about that. The mistake I see a lot of companies making is there, like we just said, hey, just go and be aggressive on your internal spend. I see a lot of companies that's like, oh, let's only have like a $200 budget per person for AI spend. And it's like that's nowhere near unleashes the full capabilities.
I mean you see some of our top engineers that are shipping 5x the amount of pull requests, but they're also spending thousands of dollars a month or more on tokens. So, we're figuring out exactly how to budget and price it out, but we absolutely expect that will translate to operating leverage long term. The last thing to call out is as part of using these products, there is inference spend. So right, if you're using Global Agent and you're asking Amplitude, hey, find me what's the cause of this drop in this conversion funnel, like that's going to cost us a bunch of tokens and all of that.
Now for the here and now, we've said, hey, let's just support it, and we're going to bundle it in with our core stuff because that just means more Amplitude usage. And you can see that in a little bit with what Andrew shared with the gross margin numbers. And so that has, that does put short-term pressure. But we, again, expect that to be accretive long term, most importantly to revenue growth, but then also to operating margin as it requires less people on our end to support more customers.
Andrew Casey: Just one clarification to Rob. I'll tell you, we did take into consideration the operating expenses associated with the Statsig business into our guide.
John Streppa: Our next question will come from Jackson Ader at KeyBanc, followed by Clark Wright at D.A. Davidson.
Jackson Ader: The first question I had, Spenser, on the command line interface and the MCP server that you're kind of turning live, making it frictionless, right, to actually adopt and use Amplitude. But if I think about the other side of maybe it's enterprise customers where you're having forward deployed engineers, right, who are ostensibly, I would think, trying to like make sure you go hand-in-hand with customers and make sure that they are adopting things. So those two things, like the frictionless and the forward deployed engineers just seem not an odds, but just like.
Spenser Skates: Yes, a little bit different. You need a one-line thing. "Why do you need someone to teach you? " Okay. So I think a few things. One is that a lot of the, by the way, I love the question. I love that you pay attention during the demos because not everyone does. So, thank you. So, first on the frictionless, it's so much of the process before to get set up with any data system, including Amplitude was extraordinarily manual. You have to define your objectives. You have to define a taxonomy, you put in the SDK, you put one line of code wherever you do it.
You have to then create a bunch of charts and dashboards on the basis of that. And so I there's tremendous opportunities to automate that with AI. And so that's what we did with Amplitude CLI with all of that installation process. It's actually pretty wild. I didn't, 3 months ago, that wouldn't have been possible. And so it's really cool to see it possible today. Now the flip side is what we see with customers is every single one of them is looking for education on "how do I adopt AI".
Arguably, you could say that all the growth in these AI natives, if you look at the private markets, comes from companies that are just doing a really good job of educating customers. It's actually no longer a technology bottleneck; in that the models are getting so fast, software is like there's, it's very easy to build. And so, the customer is choosing on, "okay, who do I trust to actually kind of get me through this. " So, if you're an AI-native bleeding edge, hey, it's just like, "Give me the one-line CLI and I'm off to the races.
" But if you're a traditional company, like, say, Fox Broadcasting or Walmart, you're going to want a lot of hands-on help from an Amplitude to make sure you educate your team. It's one thing to just have a bunch of software running and you can get that from anywhere, but it's another to say, "Hey, educate me on how to use analytics from a bleeding edge AI standpoint, what the future is going to be and help me reskill the hundreds or thousands of people I have with my organization. " And that you just need a human touch to do.
So it's, the forward deployed engineers are much like, yes, there is a, "okay, well, why do we even need that if we have the one line of code, " but actually getting adoption of Amplitude or any software product within the enterprise is much less about like do you have the widget or the specific feature and it's much more about, "hey, are you going to be the best person out there to educate my organization on what the future of this technology is. "
Jackson Ader: Okay. All right. That makes sense. The follow-up question I had is really, I guess, for both of you. I'm just thinking like you're shifting to an AI-first company, right, which has come from a lot of personnel, which has manifested itself in a lot of personnel changes, leadership changes, personnel, we're changing pricing and packaging, right? Now doing like an acquisition, right, like this integration of another product. So there's a lot going on. What is your plan to make sure that execution does, like execution risk doesn't bubble up with so many balls here.
Spenser Skates: I mean, look, just to be very candid and direct, I think vast majority of SaaS companies are being way too conservative with the change. And I've taken the opposite approach where it's like, look, market has spoken about its opinion of what the future is going to look like. We know from talking to customers what they want. We see the innovations from a technological standpoint. And so we want to run as fast as possible to where the puck is going on all of this stuff.
As part of that, acknowledging it is going to be bumpy and it is going to be chaotic and there will be things that we don't expect or can't perfectly plan for in advance. It is much more important to get there with a lot of speed for a lot of different reasons than it is to say, "Hey, let's try to protect some existing thing we have. " The existing thing we have, frankly, isn't valued much. And so what's much more interesting to me is can we generate billions of dollars in revenue in this new world.
And so whether that is changes on the organization in terms of leadership, whether that's changes on functions and roles, whether it's changing on product, on pricing, on working with partnering with OpenAI and Statsig through the future of Statsig, like we're just going to be really aggressive on making sure we reinvent the whole category. In my mind, the same thing that happened in the coding space over the last two years, where it just looks fundamentally different today than it did two years ago, that is going to happen in our category with analytics, experimentation, session replay and the whole thing. And so, it's a race to see who can do that the fastest.
And so that's what I'm really focused on is not the close to $400 million in ARR that we have. I'm much more focused on the billions and potential in the future that are going to be created.
John Streppa: Our next question will be from the line of Clark Wright, followed by Scott Berg at Needham.
Clark Wright: Any update on the ramp of events in the pricing curve that you've implemented to help enterprises scale usage previously and ongoing?
Andrew Casey: Yes. So, one of the things that we were talking about, Clark, is that our new pricing and packaging that we rolled out, we did a lot of testing on. We had kind of a soft rollout this quarter. It was still one that was handheld because we hadn't implemented many of our systems related to doing the quoting or letting reps actually do the quoting themselves. That's all been now implemented, and we're seeing great responses back from clients. I think that they're appreciative of the changes we've made. They see that as they add more events that they're getting a marginal incremental cost reduction from their perspective.
But for us, it's always going to be increasing the ARR as events increase. And I think they like the simplicity of how they can quickly adopt the modules that are surrounding analytics. Enterprises want cost predictability that they can align back to what their value propositions are. And as our sales team becomes more adept at showing and delivering what customers will get in value from Amplitude, I think that the pricing and packaging changes we've made will really reinforce their ability to move at pace.
Clark Wright: That's helpful. And then one of the other things that you noted during the prepared remarks was the TAM expansion with Statsig. Can you explain what budgets you're going after? I think the other piece that was consolidation that's unlocked with this partnership? And what could you do with that, that you couldn't as a stand-alone entity?
Spenser Skates: So the thing that, I mean, it's all kind of, there's overlapping. So, it's not like we don't have any of it. But they've done two things extraordinarily well. One, experimentation. The bleeding edge teams in AI are using them for experimentation. Like I don't know if you ever use ChatGPT, but if you ever get those like, hey, do you prefer prompt A or prompt B, that is stats internally at OpenAI powering that. And so, we're really excited that we get the opportunity to offer that out there just broadly to everyone. The other thing that they've done extraordinarily well is work with the data leader and specifically their data warehouse architecture.
While we obviously, we've done that a bunch of Amplitude, I mean, they are definitely the bleeding edge on that, where they actively both allow you to query on warehouses directly as well as run experiments and a whole bunch of other infrastructure. And so that's really exciting for us because especially at some of these larger, at the largest customers, when you start getting into the multimillion-dollar range, we often see this category of functionality owned by the data leader. And as part of that, we're excited to get much closer to them and unlock a lot of data warehouse and data warehouse adjacent budget.
John Streppa: Our next question will come from the line of Scott Berg at Needham, followed by Nick Altmann from BTIG.
Scott Berg: Spenser, I want to talk kind of an architectural type question. With the pressure on gross margins, how have you thought about things like Open Source models or some small language models being used within the broader Amplitude platform versus maybe some of the frontier models that you're using with reference and such today. There's a very large private software company that kind of announced a large, what we'll call, Open Source model and their new platform. It was really intriguing in terms of what they're doing with it. I'd love to hear what you have all considered through that process.
Spenser Skates: Yes. We're early on this to be clear. Inference spend is growing quite a bit, and you see that reflected in a few places, both the operating margin guide and the gross margin guide. Now ultimately, what we see from customers is, in most cases, they want the bleeding edge thing. So they want the latest Sonnet release or the latest Codex release from OpenAI or Anthropic or one of the other providers. And that also leads to higher scores on our benchmark. Like last quarter, we published a benchmark where we got a 76% accuracy rate.
And we could downgrade that in some places, but you'd probably be looking at maybe a 40% accuracy rate, and that's a pretty significant difference. Now over the long term, we'll obviously find places to use cheaper, more effective models where it makes sense. But in general, and we'll sort out the path on gross margin as part of it. In general, right now, we're just in the, "hey, like let's make sure we win the market first and foremost, " and then there's optimization down the line that comes with that.
So, we do expect our ability either with the Open Source models or some of the cheaper models like if you look at Anthropic's Haiku model, that's a really great one that for actually a good chunk of cases actually works decently well. But again, when you're doing some of the complex data reporting, especially with Amplitude, we see a lot of customer demand for high accuracy, and that means the most bleeding edge ones, and there's always a new one release. Now the good news in all of this is this, the curve on this is crazy. I mean you're seeing a factor of 10 or more improvements in the price performance of these models year-on-year.
And so, it's hard to say exactly what it's going to be in 12 months from now. But I know we're, it's going to be a lot better, and that means we can choose, okay, exactly where it makes sense on the price performance so we have reasonable gross margins.
Scott Berg: Understood. Very helpful. And then, Andrew, I wanted to dig into the Statsig acquisition a little bit more. I think when OpenAI acquired that business, they're doing about $40 million worth of ARR. Are you, I guess, saying or implying that the balance of the $16 million that you're bringing over versus that $40 million is effectively staying with OpenAI? And then I guess, did you happen to pay for any part of this business? Didn't know if there's a purchase price cash or stock, some sort of allocation that's committed to this that any of that any of that information we can?
Andrew Casey: Now a couple of things you need to understand about Statsig's former business prior to OpenAI acquiring them. OpenAI was a fairly large customer for them. And that was a substantial portion of their ARR, right? So, then your question is what is OpenAI's intention with Statsig? It's back to what Spenser mentioned earlier. They're intending to use it for internal noncommercial reasons, and they're continuing to use it to support their core products. So, as we go forward, what we've taken on is the customer contracts, all of them. And we're taking on all of the brand assets, and we are increasingly going to be developing on the product itself.
So, delivering great solutions for those clients and future clients, frankly, of Amplitude.
Spenser Skates: One minor technical point, we're also talking about Statsig as a partnership, not an acquisition. So it's nuanced, but yes, it's an important one as well.
John Streppa: Our next question will come from the line of Nick Altmann from BTIG, followed by Arjun Bhatia from William Blair.
Nicholas Altmann: Awesome. Andrew, we appreciate the color on the Statsig contribution. But you guys, you've kind of talked about this before, but there's overlapping customers. There's overlapping product sets. At the same time, you guys have kind of also made an effort to consolidate your customers onto more of the Amplitude products. And so in terms of that revenue contribution framework that you outlined, what does that sort of imply for those customers where there is overlap working with both you guys and Statsig and on the product side and on the customer side? Just any other details you can kind of unpack in the assumptions would be helpful.
Andrew Casey: We don't think, we don't actually see that there's huge overlap in the products that customers are using from Statsig and us. As Spenser mentioned, they're really good on experimentation. And they may, you may find customers where we did have overlaps if they were using analytics from Amplitude, but experimentation from Statsig. So there really isn't like overlapping revenue. It's an opportunity for us to actually add more and more to a consolidated platform. So if they didn't have such a replay or guides and surveys. In fact, we see a huge opportunity for us to go sell into the customer base that is overlapping.
Not to mention, there's a whole new group of customers that Amplitude now has access to.
Spenser Skates: Yes. It's not, Nick, I'll say it's not the cleanest like, okay, yes, theoretically, we both have experimentation. There's has been developed in a little bit of a different way. So it's a little bit of a different customer set. So there is a lot of great opportunity across both customer bases. Again, we're kind of three days into this thing. So we're still sizing that, and we'll have more detail when we go through on the Q2 earnings call.
Nicholas Altmann: Understood. And then the NRR continues to accelerate. The ARR growth remained at 17%. So Andrew, can you just maybe unpack why we're seeing those two metrics disconnect? Is it something on the new logo ACV side of the equation? Is it gross retention dynamic? Maybe just talk to us about the disconnect between those 2 metrics.
Andrew Casey: Yes. So in any given quarter, Nick, you have an amount of new ARR we're adding in new logo versus expansion. In fact, a year ago, you probably remember, we talked about having a really strong new logo quarter. and not making as much progression on net dollar retention. I would say in Q1 this quarter, you saw the opposite effect. You saw really good expansions happen. That may have been partially due to the really strong new logo quarter we saw in Q4. And in Q1, it shifted more to an expansion quarter than we anticipated.
And every quarter, we try to take a look at what is the real balance of our pipeline between new logo and expansions and do our best to estimate what the impact is going to be. It wouldn't surprise me given what we've seen in Q1 that you might see a shift into Q2 where there is more new logo versus expansion. So that's why I was commenting that in some cases, we're seeing quarter-to-quarter progressions on our long-term plan. In other quarters, you might see it not improve as much, and it's really related to that balance. Is it overweighted in new logos in any given quarter? Is it overweighted in expansion?
But the long-term view is that we're continuing to ship new products and add to the value that customers can purchase from Amplitude, which sets us up for additional expansions.
John Streppa: Our next question will be from Arjun Bhatia from William Blair, followed by Billy Fitzsimmons from Piper Sandler.
Arjun Bhatia: Perfect. I want to go back to the sort of the inferencing costs increasing. And Spenser, I'm just curious where the AI usage is coming in strong. And you've made a lot of sort of enhancements to your MCP server. And I'm curious if that's also driving a meaningful change in how your customers are using the Amplitude platform.
Spenser Skates: Yes. A lot of customers are using MCP. We're seeing huge uses of that, huge uses of Global Agent, a good chunk on specialized agents. That's kind of the bulk. Those all kind of hook up to the same underlying services. So you can say, hey, find the root cause of an issue in this chart and that can either come in through MCP, come in through Global Agent, which is a chat interface or come through specialized agents, which is purposely designed. All of those combined, they're what is the vast majority of the usage of those is the vast majority of what the inference costs are.
Arjun Bhatia: Got you. And then just your, I mean, your comment, and I think we see it broadly as well that the software development life cycle is changing very quickly. Obviously, it's starting in just code gen and code writing. And what is your, what is your perspective on the steps that will be required before you start to see it in your category? Like does the fundamental composition of the software team or the ops team need to change? And is there more change management ahead of us, I guess, before we see sort of this hockey stick in analytics and monitoring and experimentation.
Like it's just more of a philosophical question, but I'm curious where we are in this cycle in your mind?
Spenser Skates: It's early, yes, to be clear. So it's hard to prognosticate on exactly when. I'll tell you the best, so I'd say let me talk about tech and then I'll talk about non-tech. Within tech, they're already embracing this in terms of the automated instrumentation, in terms of automated analysis, automating the product development life cycle. You might have seen blog posts or Twitter posts about how companies are having an agent harness and then automatically building software. And so that's awesome. They're kind of already living in this future.
And a lot of the companies we work with Granola is a great example I called out, are pushing us on that being like, hey, here's what we want to be relevant. Agent Analytics was one of the outputs of that. And we're in the early days with it. We just announced it, I think, 6 weeks ago, something like that. We had the first post about it 6 weeks ago. And so we're early days in the adoption, but very exciting. A lot of these companies are living in the future, and now it's on us to like, okay, let's make sure we go capture it. On the non-tech company side, it's much, much earlier.
They're looking to get educated on the basics and hey, show me a maturity model, okay, if I'm not step 1, how do I get to step 2? And then, okay, I get like eventually, you guys will help me get to step 4 on having an agent harness and automatically building software, but that might be a few years away. And instead, they're just saying, okay, well, at least let me take some of my existing workflows and speed them up a whole bunch so that I don't need to do all this instrumentation.
I can just use your CLI wizard or I can use the chat Global Agent in the chat interface and have it generated a dashboard for me and now I'm saving time. So they're more focused on making a bunch of existing stuff more efficient than going straight to the bleeding edge. So anyway, early days, I do expect in the next few years, it will look substantially different. But hard to say exactly which quarter and when.
John Streppa: Our next question comes from the line of Billy Fitzsimmons from Piper Sandler, followed by Koji Ikeda from Bank of America.
William Fitzsimmons: I'll keep to one because I know we're getting close to the end of the call. Spenser, for you. I appreciated the commentary in the prepared remarks on some of the new members of the C-suite. With Nate coming in as the Chief Commercial Officer, I want to double-click there because it seems like you're seeing some solid go-to-market progress in some of the initiatives you've already been doing, multiproduct adoption. Any additional color you can provide on his plan or top priorities going in and expected changes to sales motions, sales incentives, partner strategies and just general changes relative to what you've kind of already done?
Spenser Skates: So yes, let me talk about the transition, and then I'll talk about going forward. So Thomas, our prior President, ran all go-to-market, did a phenomenal job over the last 3.5 years, really upgraded us to an enterprise company. Before, we weren't even engaging with executives consistently. Now we are, and we have all the services across the board to support them, and that's been fantastic. And you've seen that show up in terms of $100,000-plus customers, platform adoption, a whole bunch of other metrics. Nate actually, as our Chief Commercial Officer, was previously our Chief Revenue Officer and was in place reporting to Thomas for the last 3 years.
So he's not actually, so that part is not actually new when he was running sales. The change in his role now is he's now running the post-sales motion as well as revenue operations and enablement. And so those, we're thinking about, okay, how do we streamline to make sure that is seamless between the AEs and then the technical success managers and all the parts of the post-sales motion. In terms of going forward, I had a slide on this briefly, but, so a few different things. One, just more technical talent in post sales generally. We've renamed it from customer success managers, gotten rid of a bunch of the extraneous roles and just called them technical success managers.
We expect them to be able to educate prospects on the Amplitude product and platform, how to implement it, how to integrate it with their code base and be the go-to on AI when it comes to technical queries. We're also added 4 deployed engineers that can do a lot of the actual coding work. Now it's early in the function. We just spun up that group about a month ago, but that can do like actually code and hook up like a lot of the problem with AI adoption internally within companies is not just turn on the software, it's actually hook it up to the right systems.
So, make sure it's plugged into the back end, make sure the SDK is in, make sure the events are instrumented, make sure that the right people are getting reports. We now introduce the MCP clients to make sure it can hook up the Slack and Linear or Atlassian or Jira or what have you. And so that's what the forward deployed engineers is, actually do the coding work on behalf or alongside the customer. So that change has been received. It's early, but that change has been received very well by customers.
Really, what they're looking for is expertise on how AI is going to change analytics as well as the associated functions within product management, data and everything related. And so yes, like they're really excited to learn. Actually, one of the funny ones is like we've even gotten a lot of questions from customers about our AI week. It's like, "oh, I want to do the same thing. How do I do it? " And so even if it's not necessarily one-to-one amplitude related, just having people there that are able to speak about here is how you can upskill yourself with AI and transform your organization, that's the value.
They see a lot of value in it, and so we want to make sure to provide that in the post-sales motion.
John Streppa: Our next question will come from the line of Koji Ikeda from Bank of America, followed by Elizabeth Porter from Morgan Stanley.
George McGreehan: This is George McGreen on for Koji Ikeda from BofA. I kind of wanted to ask as a follow-up to that last question on go-to-market changes. And forgive me if I didn't hear it, are there any sort of tweaks being made as it relates to sales incentive comp? And then kind of as an unrelated follow-up, I'd be interested to hear if there's any update. I believe last quarter, 25% of queries on the platform were coming from AI agents. And if there's any update to that number, I'm sure it's growing healthily. And yes, just kind of like what's kind of the outlook and trends there?
Spenser Skates: For sure. In terms of sales incentive comp, we've had incentives in place both for platform adoption as well as for multiyear, and those are continuing. I mean we'll look at those, tweak those every quarter based on what it is we're trying to do with the business. Right now, like right this second, a lot of the priority is making sure that Statsig customers coming over are very successful, and we're able to continue to serve them and help them. So we're, Nate and the broader sales team are very focused on that, and we've put in a few specific incentives around that. But we're always tweaking that stuff.
It's not like there's a major massive shift in strategy there. And then in terms of the Agent adoption, it continues to grow, and there has been a significant increase. We're not sharing numbers on this particular call, but we will have an update next quarter on agent adoption relative to human usage of data analytics.
John Streppa: Our next question will come from the line of Elizabeth Porter from Morgan Stanley, followed by YC Wong from Citi.
Elizabeth Porter: I'm on for Elizabeth Porter. Spenser, you talked about seeing the puck as fast as possible early in the call. I just wanted to ask you to help us frame where your incremental AI-related investments are going this year, whether it's infrastructure, experimentation tools, workflow automation, like where is the puck going for you guys?
Spenser Skates: Specifically on expenses, the big one is inference costs. So to be able to support Global Agent, MCP, specialized agents as well as some of the other AI products, we've been spending quite a bit there. Those show up under cost of goods sold. So it's growing a lot. We'll monitor it and we'll figure out what the right long-term place for that to be and how do we value capture and get paid for it, too. But right now, we just want to drive adoption as the priority. The other big place is on internal tooling for the team. The big one we're using a ton is Quad. So that was what we standard on an AI Week.
I think Anthropic has done an amazing job with Claude and both the Chat Claude.ai as well as Claude Code in terms of integrating it with existing business systems. So we actually, you hook it up to Slack, e-mail calendar. We actually built Salesforce hasn't gotten their act together yet, I'm building MCP connectors. We built their own, built a few others. And so now you can access all Amplitude data through that interface. And so that's a significant spend internally. And then we also spend some on the team on Cursor as well. There's a tail of them. Obviously, Granola is another one I mentioned on the call that is both a customer of ours and vice versa.
But yes, those are the inference cost is the biggest one and then Claude for the team and then Cursor.
John Streppa: And our last question will come from YC Wong from Citi.
Yitchuin Wong: Just a quick one for Andrew, we just close in with AI. Last quarter, we talked about 25 AI customers like crossing over the 100 ARR mark, like if you're improving deep integration with the foundational models, the Cursor including, you are positioning Amplitude to capture a larger share of the AI market. Are these AI customers exhibiting like structurally different Net Retention Rate, consumption pattern that you're seeing compared to your traditional enterprise SaaS customer? And how do you view the opportunity longer term here?
Andrew Casey: So certainly, we believe that AI companies as they standardize on Amplitude will continue to see greater and greater value from using Amplitude and they embed and use it to drive marketing purchase, build better products, get better insights on how users are acting. And we're seeing, in some cases, some of the larger AI customers we have increase their data ingestion into our platform. And one of the things we talked about on the gross margin headwinds was related to greater data ingestion. I think there was a confluence of that's all related to the AI agents.
Well, there's also very classic cases where customers are using more of our capabilities and they're expanding their data usage and some of the AI companies are certainly exhibiting that. And one of the things that Statsig had in their customer base was a strong AI customer component. So we look forward to updating you all in Q2 on what that looks like with the combined product set and customers.
John Streppa: 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.
Spenser Skates: Awesome. Thank you, everyone.
Andrew Casey: Thank you.
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