Amplitude AMPL Q4 2025 Earnings Call Transcript

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DATE

Wednesday, February 18, 2026 at 5 p.m. ET

CALL PARTICIPANTS

• Chief Executive Officer — Spenser Skates
• Chief Financial Officer — Andrew Casey
• Vice President, Investor Relations — John Streppa

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TAKEAWAYS

• Revenue -- $91.4 million for the quarter, representing 17% year-over-year growth, up from 9% in the prior fiscal year.
• Full-Year Revenue -- $343.2 million, up 15% year over year compared to 8% the previous year.
• Total ARR -- $366 million exiting the quarter, an increase of 17% year over year and up $18 million sequentially.
• Enterprise ARR Growth -- 20% year-over-year increase from enterprise customers, identified as the primary growth driver.
• Net Dollar Retention -- Now above 105%, an improvement from 100% at the end of 2024.
• Current RPO -- Maintained over 20% growth throughout the year; total RPO grew 35% year over year in the quarter.
• Customers with $100,000+ ARR -- 698 in total, up 18% year over year and 45 sequentially, marking the largest sequential increase recorded.
• Customers with $1,000,000+ ARR -- 56, up 33% year over year.
• Multiproduct Adoption -- 74% of ARR from customers with more than one product, a 15 percentage-point rise from the previous year.
• Platform Attach -- 44% of customers utilize multiple products.
• Full Platform Deployment -- 20% of ARR from customers with five or more products, doubling year over year.
• Gross Margin -- 77% for the quarter, unchanged from the prior year, and up one point sequentially.
• Sales & Marketing Expense -- 42% of revenue, down one point from the prior quarter.
• Research & Development Expense -- 18% of revenue, flat compared to the previous year.
• G&A Expense -- 12% of revenue, down four points year over year.
• Operating Income -- $4.2 million, or 4.6% of revenue, reported on a non-GAAP basis.
• Non-GAAP Net Income Per Share -- $0.04 for the quarter, compared to $0.02 a year ago.
• Free Cash Flow -- $11.2 million for the quarter (12% of revenue), up from $1.5 million (2%) in the prior year; full-year free cash flow was nearly $24 million, or a 7% margin.
• Buyback Program -- The board approved an additional $100 million reserve for share repurchases.
• AI Agentic Analytics -- "Over the past six months, our Agentic Analytics platform has reached a 76% success rate on complex production grade queries. That is seven times better than a straight text-to-SQL approach."
• AI Agent Query Share -- By the end of the period, 25% of queries were triggered by AI agents, increasing from nearly none in October.
• InfiniGrow Acquisition -- Acquired AI-native marketing analytics startup InfiniGrow to connect spend, behavior, and revenue impact; brings additional AI engineering talent.
• Customer Additions -- Landed notable new enterprise and expansion deals, including one of the largest music streaming apps, The Cheesecake Factory, and NTT Docomo.
• Contract Duration -- Average contract length now exceeds 22 months.
• Rule of 40 -- Improved from 15 in 2024 to over 24 in 2025, measured by free cash flow yield and ARR growth.
• Pricing and Packaging Update -- Introduced new model: non-core products monetized as a percentage uplift on the events-based core platform charge, designed to incentivize greater platform consumption and cost predictability.
• Q1 2026 Guidance -- Revenue expected between $91.7 and $93.7 million (16% annual growth at midpoint); non-GAAP operating income guidance between negative $4.5 million and negative $2.5 million; non-GAAP net income per share between negative $0.02 and negative $0.01.
• Fiscal 2026 Guidance -- Full-year revenue guide of $390-$398 million (15% annual growth at midpoint), non-GAAP operating income of $7-$13 million, and non-GAAP net income per share of $0.08-$0.13. (Fiscal year ending Dec. 31, 2026.)

SUMMARY

The call highlighted that Amplitude (NASDAQ:AMPL) completed a year of accelerated enterprise adoption, with cross-sell expansion and new product launches central to financial and operational performance. Management stated, "The enterprise is now our core growth engine," citing a shift toward longer contracts and the broadening of multiproduct adoption within the customer base. Platform enhancements—including agent-driven analytics, integration with leading AI models, and workflow automation—are positioned as the backbone for future customer growth. The company announced an expanded share repurchase program, increased contract duration metrics, and the acquisition of InfiniGrow to further extend platform capabilities. Strategic pricing changes were discussed as foundational for driving further expansion, consumption, and cross-sell opportunities across both core analytics and recently introduced AI offerings.

• Management described rising AI agent usage as a material driver of query growth, stating, "What we are seeing generally is, if you look at query growth from direct usage of the Amplitude dashboard, it is increasing in line roughly with the size of our business. If you look at agentic query use, it is skyrocketing, as you saw on that chart in the last few months. And so the amount of leverage, I think the same thing that happened to coding in the last two years, where if you look at it, the best software engineering teams, the majority of lines of code are produced by agents. It is mostly humans editing and interpreting them, stitching them together, and giving high-level direction. And that is where the best software engineers are. I think the same thing is going to happen in analytics and data analysis, where the vast majority of the data munging of the tool and figuring out what query means what thing and how do you do a segmentation, understand the root cause — that is all going to be automated by agents. And our goal is to be the first company to do that in a big way."
• The new pricing and packaging model was said to incentivize customers to "ingesting more data to the platform," with improved cost predictability and incremental pricing.
• Multiproduct attach rates have increased, and only 51% of ARR now comes from customers using more than three products.
• The board authorized an additional $100 million for buybacks, targeting dilution minimization in light of strong free cash flows.
• AI-native companies, including over 25 customers with $100,000+ in ARR and a seven-figure deal with a leading foundational model lab, have become a targeted growth segment.
• Innovations in agentic analytics are credited with making complex analytics more accessible, with a cited 76% query accuracy rate significantly above alternatives.
• Gross ARR expansion in Q4 was driven 58% by expansions, with no individual expansion above $1 million, suggesting a broad-based expansion rather than concentration among large customers.
• The announced InfiniGrow acquisition is aimed at strengthening capabilities around connecting marketing spend to outcome analytics within the AI context.

INDUSTRY GLOSSARY

• ARR (Annual Recurring Revenue): Contracted, recurring revenue components normalized to a one-year period, a common SaaS performance measure.
• Agentic Analytics: Workflow where AI agents autonomously perform, interpret, and recommend analytical actions traditionally performed by humans, including data segmentation, experimentation, and insight generation.
• MCP (Model Context Protocol): Amplitude’s protocol enabling external AI products to connect to Amplitude’s analytics infrastructure for context-rich querying and insight extraction.
• RPO (Remaining Performance Obligations): Aggregate value of contracted future revenue on which performance has not yet occurred, reflecting backlog within SaaS models.

Full Conference Call Transcript

Operator: Non-GAAP operating income was $4.2 million or 4.6% of revenue. Customers with more than $100k in ARR grew to 698, an increase of 18% year over year. Over 25 AI companies are now included in that $100k cohort as well. This quarter was marked by balanced execution. No single deal was over $1,000,000 yet we had our highest ever number of multiproduct and $100k ARR lands. I want to talk more about AI in our strategy. Over the past year, AI coding assistants from Anthropic, OpenAI, Cursor, and others have compressed development cycles dramatically. The velocity at which companies are shipping new products has accelerated.

When software is this easy to build, it creates a gap between how fast teams can ship features and how fast they can learn if they are working. This shifts the pressure to the right side of the product development loop that you see here, the use and learn side. Understanding how users behave, what works and what does not, and what actions to take next becomes the bottleneck. The constraint is no longer knowing how to build, it is knowing what to build instead. This is the hardest problem in software today. I say that because builders and their AI need a system of context that combines multiple data streams. They need structured behavioral data.

They need the correct retention and funnel logic. And they need the right analytical tools exposed in a way that enables AI to reason effectively. The AI then needs to be able to iterate with that system, test hypotheses, refine queries, identify root causes, and recommend action accurately and repeatedly. This is not something that can be vibe coded over a weekend replicated accurately with an LLM on a data warehouse. However, it is exactly what Amplitude, Inc. is purpose built to do. We have worked with thousands of companies over the past thirteen years and amassed the world’s largest database of user behavior.

Our AI can explore patterns, explain changes, and guide teams on what to do next more accurately and reliably than any other system. Over the past six months, our Agentic Analytics platform has reached a 76% success rate on complex production grade queries. That is seven times better than a straight text-to-SQL approach. With the new agents we launched yesterday, teams can now move from insight to action in minutes, not weeks, using analytics, cohorts, experiments, and messaging in one continuous agentic workflow. Through our MCP integrations with Anthropic, Figma, OpenAI, GitHub, Lovable, and Slack, we are bringing behavioral intelligence to teams where they already work.

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

Using Amplitude Enterprise Analytics and session replay, they can now understand activation, engagement, retention, and monetization end to end. With Amplitude MCP, they can offer those insights directly within the AI environments their teams already use, dramatically improving the ability for them to automate development. And it is not just AI companies. Companies of all sizes need a system that gives them trusted data, insights, and action to successfully deploy AI in the real world. So they turn to Amplitude, Inc. as well. This momentum combined to one of our strongest quarters across gross bookings and new ARR alongside meaningful improvement in churn. Go-to-market motion has matured.

There is a tighter focus on value-based use cases in the enterprise and on expanding with multiproduct deployments. We continue to consolidate the fragmented market. Platform win rates are increasing against point solutions, and our newer products are gaining traction. Guides and Surveys, launched less than a year ago, is our fastest growing product to date. We are also seeing a large increase in AI native usage as agents connect directly to Amplitude, Inc. Over the past few months, the total number of queries triggered by AI agents has increased dramatically. In October, there were almost none, and today, it is 25%. Agents also drove the vast majority of overall incremental query growth.

This tells us that customers are trusting agents with analytics work. It also indicates that our platform offers the accuracy and the context needed in production environments. Taken together, this creates a powerful tailwind for Amplitude, Inc. as we continue building a durable, scalable company that can unlock the next frontier in software. Over the years, we have intentionally expanded beyond core product analytics and into adjacent workflows. We have continued that work and acquired InfiniGrow, an AI native marketing analytics startup that connects spend, behavior, and revenue impact. InfiniGrow brings strong AI native engineering talent to Amplitude, Inc.

This strengthens our platform as a system of context and expands our ability to bring acquisition, activation, and retention into one continuous feedback loop. Yesterday, we launched our global AI agents, specialized agents, and MCP. This represents the start of a fundamental shift in how teams work with their analytics data. Historically, analytics has required humans to do most of the heavy lifting. Writing queries, building dashboards, monitoring changes, interpreting results, and then figuring out what to do next. That process does not scale in a world where teams are shipping faster and faster. AI agents change that model.

Instead of asking questions one at a time, teams can now delegate work to agents that continuously analyze behavior, surface insights, and guide action. Our agents understand events, funnels, cohorts, experiments, session replay, and outcomes because they operate inside a context system specifically designed for them. Agents make life easier by doing the work that slows teams down today. That is very, very different from bolt-on AI tools from SaaS companies that sit outside the data and try to infer meaning after the fact. The best way to see this and understand this is to look at it in action.

I want to show you a quick teaser video and then I am going to show you a demo of what we have released. Let us go ahead and roll the video. The word of the year is

Spenser Skates: Slop. S-O-O-P. The word of the year 2025 is

Unknown Analyst: Slop. Today, you can build anything.

Unknown Analyst: So a different question emerges. What should you build? Because speed without direction is just noise. Teams move fast and still get it wrong. Not the right thing, just the next thing. But analytics today forces you to stop in a world that big. Queries, dashboards, analyses. It is slow. It is manual. And has not changed in a generation. So we started from scratch. From painful queries to a helpful copilot, from Ask the Data to just ask. From tedious grunt work to agents that actually work. And so now that anyone, anywhere can build the right thing, the question is, what will you build?

Operator: It is a great question all product builders should ask themselves now. What will you build? I want to now walk you through what we have launched in AI analytics yesterday. I am really excited about the future, and I want to show you Global Agent. Global Agent radically changes how our customers interact with their data. Starting your day with a dashboard is dead. Look at this interface. No dashboard. No graphs, no charts, just a chat box and a few simple prompts if customers need help getting started. I can talk to Global Agent like I talk to a colleague. I am going to go ahead and ask it, how is our loyalty program doing?

In seconds, it comes back with a summary. Notice I did not use any jargon about event totals or taxonomy. Just a regular question. It is calling out some pretty concerning numbers. Only 5% of users who view our welcome page actually go on to join the loyalty program. That is low, so I am going to click in and investigate more. The Global Agent has followed me to a deep dive on this chart. I can investigate with another simple question. Break this down by traffic source. Here is the breakdown. Facebook and Instagram are driving loyalty sign-ups at 5.6–5.2%, while Google and direct traffic lag behind. The Global Agent summarizes it perfectly.

Social media converts 10% to 15% better. Since social media outperforms Google, I might shift ad spend. But looking overall, all the rates are low. So before reallocating budget, I am going to go deeper. Is this a channel problem or an audience problem? Let me ask, do new users convert differently than existing customers? Without AI, this kind of analysis takes a lot of time. Segmenting users, comparing funnels, pulling insights together. The Global Agent does it in seconds. And here it is, 14% conversion for repeat purchasers, 5.4% overall. That is 2.6 times higher. It answers my question. It is an audience issue, not a channel issue. I should reallocate my budget towards repeat purchasers.

Again, simple language, fast answers, deep learning that anyone can use. Analytics is the perfect use case for agents. So I want to show you specialized agents. Our specialized agents work continuously on specific jobs that would usually take dozens, if not hundreds of hours. Monitoring dashboards, analyzing session replays, processing feedback, running conversion experiments. Legwork, now done automatically. We are going to be eating our own dog on this one. I already have a session replay agent set up to monitor our own session replay tool. I have it set, in addition, to send me a Slack when it has a strong finding. This specialized agent has been watching hundreds of replays and sent me some summarized findings.

Users with multiple saved filters type search terms but cannot find filters without scrolling through the full list. Power users cannot preview filter criteria before applying, forcing trial-and-error selection. These are all things we should improve. We could have had someone watch all those replays. We could have talked to customers for hours on end. Or we could have let these continue to be issues. Instead, I get these findings served to me on a daily basis with a full report and a detailed breakdown with key findings, suggestions on what to explore next, and even a highlighted reset of replays of these issues. Okay. We are going to save the best for last.

Finally, I want to show you what I am most excited about, which is Amplitude MCP. We are releasing a fast-growing library of expert-level workflows that customers can trigger in AI clients like Claude with a simple slash command. I am going to go ahead and use Amplitude and Claude by typing use/create-dash and create a dashboard that tracks our gross conversion performance. I hit enter, and it goes to work. Instead of me manually creating 15 charts, running the segmentations myself, and piecing together an explanation in a doc, this skill handles it in one click. With MCP apps, Claude is opening and building Amplitude charts right inside itself.

It is done it, so I have now gone to the link it gave me and a perfectly built dashboard with top-level metrics, conversion funnels, and segment breakdowns. Amazing. Moving on to customers. We had a great quarter for new and expansion deals with enterprise companies, including one of the largest music streaming apps, The Cheesecake Factory, Asana, PGA of America, CrossFit, Stewart Title Guarantee Company, Crunch Fitness, Whoop, Once Upon Publishing, and NTT Docomo. I am going to highlight three examples that demonstrate the power of the platform in different ways. Japanese telecom NTT Docomo is using Amplitude, Inc. across more than 1,000 active users to drive efficiencies at scale.

As an early design partner for our AI agents, their data platform team uses agents to streamline analysis across existing dashboards. In one project, an agent reduced campaign analysis time by over 90%. Our AI-powered session replay summaries, automatically localized into Japanese, help UX teams identify issues faster and improve the digital journey for millions of customers. We are now working closely with NTT Docomo to shape our agents’ roadmap with feedback on collaboration features and AI-powered insights. Siemens, the $70,000,000,000 global technology leader, partnered with Amplitude, Inc. over three years ago to power analytics across website presence and broader digital ecosystem.

By consolidating onto our AI analytics platform from a series of point solutions, Siemens gained a unified real-time view of user behavior. Recently, the team organizing their annual user conference used Amplitude, Inc. to identify their overreliance on direct email and organic channels. They experimented by reallocating spend into targeted web promos plus paid and organic social. This delivered a 90% year-over-year increase in web traffic and a projected 50% increase in registrations and attendance to their conference. Lastly, we landed one of the largest music streaming apps in the world.

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

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

Andrew Casey: 2025 was a year of innovation, execution, and we delivered a solid base for our future long-term growth strategy. When we met at our Investor Day last March, we laid out a deliberate road map to capture the enterprise and accelerate multiproduct adoption while leading the industry in innovation. Today’s results demonstrate that we have not just met those goals. We have established a new baseline for durable growth. The enterprise is now our core growth engine. ARR from our enterprise customer cohort is up 20% year over year with higher retention and expansion rates than the rest of our business. This was not by accident or luck.

Our AI analytics platform has been designed to be enterprise grade with trust and safety of our customers at the center. Our go-to-market team has worked for the past three years to orient our go-to-market motion to focus on the enterprise, increasing customer value through selling our platform and engaging in longer-term contracts. 2025 was the coalescence of this work, to focus on our customers’ value and creating durable base for future growth. We sustained growth of current RPO greater than 20% throughout the year. And in Q4, total RPO grew 35% year over year. Our average contract duration is now above 22 months.

In addition to our success in the enterprise, we have also formulated our product and our go-to-market team to embrace our AI platform strategy. By combining niche point product solutions surrounding analytics into a comprehensive platform, we are able to deliver greater value than stitching together point solutions. We also believe that having a platform is essential to the harnessing capabilities of AI to reduce friction in our customers’ workflows. In 2025, we did a great job expanding our multiproduct attach rate for our customers. 74% of our ARR is from customers with more than one product, up 15 percentage points from last year. We still have a great opportunity to expand our multiproduct customers as well.

Only 51% of our ARR comes from customers with greater than three products. Looking at a full platform deployment of five-plus products, that percentage is 20%, doubling year over year. We have a massive opportunity to expand with our customer base. We believe our market opportunity expands dramatically with the inclusion of our new AI products that promise to expand adoption and use cases. The progress in selling our platform is best exemplified through improvement of our retention and expansion motion with dollar-based net retention now above 105% after exiting 2024 at 100%. However, our work is not done. At the beginning of this year, we introduced a new pricing and packaging to our sellers.

Let us start with what is not changing. We are not changing our core billing metric of events. We believe this is a great representation of the value our customers receive from our platform and it is also an appropriate monetization strategy as we center AI engagement on our platform. What has changed is we are centralizing the monetization of our other products, such as Experimentation, Session Replay, Guides and Surveys, via a percentage uplift on the core platform charge, which is events-based. This reduces the friction of adoption of those products by making it easier to understand for our customers and reduces the need to estimate how many sessions or experiments they want to run in the near term.

Longer term, this will also encourage greater consumption as customers no longer fear overusing certain parts of their contract or underutilizing others. It is a radical simplification of our pricing that acknowledges our customers’ needs for greater cost caps and certainty on their costs as the volume of data ingested into our platform expands. It also supports our focus of integrating AI into all of our product offerings and expanding customer usage, which could be a tailwind longer term on easier lands and faster platform expansions. In summary, as we have transitioned to an AI analytics company, we have created a more durable base of our business focused on the enterprise.

We have driven expansion of our platform through innovation, and we are making it easier for customers to value quickly and encourage expansion. We have done all this while being disciplined in our spending and driving to non-GAAP profitability with record free cash flow. Looking at the rule of 40, which we measure based on free cash flow yield and ARR growth, we have now improved from a rule of 15 in 2024 to over 24 in 2025. We will continue to focus on driving top-line growth through a disciplined manner in 2026. Now turning to our fourth quarter and full-year results.

And as a reminder, all financial results that I will be discussing, with the exception of revenue, are non-GAAP. Our GAAP financial results along with a reconciliation between GAAP and non-GAAP results can be found in our earnings press release and supplemental financials on the investor relations page of our website. Fourth quarter revenue was $91.4 million, up 17% year over year versus 9% in fiscal 2024. Fiscal year 2025 revenue was $343.2 million, up 15% year over year versus 8% in fiscal year 2024. Total ARR increased to $366 million exiting the fourth quarter, an increase of 17% year over year and $18 million sequentially. Here are more details on the key elements of the quarter.

We had a strong quarter for both new and expansion deals in the enterprise. Platform sales were also particularly strong. 44% of our customers now have multiple products with 74% of ARR coming from that cohort. The number of customers representing $100,000 or more of ARR in Q4 grew to 698, an increase of 18% year over year and up 45 customers since the last quarter, representing the largest sequential increase in this cohort in company history. Additionally, the number of customers representing $1,000,000 or more in ARR grew in Q4 to 56, up 33% year over year, demonstrating our ability to land significant accounts and grow them over time.

In-period net dollar retention progressed to 105%, led by cross-sell expansions across our customer base. 58% of Q4 gross ARR was driven by expansions across a broad range of customers, with no individual expansion exceeding $1,000,000. It is still driving meaningful progress in net dollar retention. We will continue to focus on driving net dollar retention higher through our platform strategy. Gross margin was 77% for the fourth quarter, flat to 2024 and up one point since last quarter. We continue to make progress on optimizing our hosting, driving multiproduct contracts, and monetizing our services engagements. We will continue to look for opportunities to incrementally improve gross margin over time.

Sales and marketing expenses were 42% of revenue, a decrease of one point from the third quarter. We continue to focus on improving sales efficiencies, driving improvements through our changes in processes, coverage, expansion of enterprise customers. At the same time, we are investing for future growth while balancing those incremental investments with efficiency gains. In Q1 FY26, we will have higher sales and marketing expenses as a percentage of revenue, reflecting timing of events and our annual company kickoff. R&D was 18% of revenue, flat to 2024. We expect to continue to invest in the talent and capabilities of our team to drive greater innovation in the future. G&A was 12% of revenue, down four points from 2024.

We expect G&A to improve as a percentage of revenue over time. Total operating expenses were $66,000,000, 72% of revenue, down three points sequentially. Operating income was $4.2 million or 4.6% of revenue. Net income per share was $0.04 based on 141.5 million diluted shares compared to net income per share of $0.02 with 135.7 million dilutive shares a year ago. Free cash flow in the quarter was $11.2 million or 12% of revenue compared to $1.5 million or 2% of revenue during the same period last year. In the fourth quarter, we managed our cash collections and made meaningful progress on shifting contracts to annual payments in advance.

For the full year, we had a record free cash flow of nearly $24,000,000 or free cash flow margin of 7%. We have conviction in the long-term value of our business and have used and will use our cash to minimize the impacts of dilution. We have already purchased in the open market under our current buyback. Given the strength in our balance sheet and the underlying business, the board has approved an additional reserve of $100,000,000 to be used for buybacks. Our balance sheet position remains strong and allows us the opportunity to be more aggressive in our M&A strategy to accelerate our R&D roadmap when appropriate. Now turning to our outlook.

As a reminder, the philosophy of how we set guidance is through the lens of execution. We are confident we have the right strategy and the right platform to continue to consolidate the fragmented market. We continue to improve our go-to-market motion and are accelerating our pace of innovation. We have the right monetization strategy to encourage the adoption of our AI tools, and we believe those tools will reduce the barrier to adoption of our full platform, leading to greater monetization opportunities. Our strategy remains consistent with our go-to-market. It is being aided by our simplification of our pricing and packaging. We will continue to focus on gaining new enterprise customers and driving cross-sells with our existing customer base.

We also believe that with the release of our AI capabilities, our monetization of data ingested in our platform, and the cross-sell opportunities of new products gives us the right strategy to align the value of our platform with our growth opportunities and grow our business in a profitable way. For Q1 2026, we expect revenue to be between $91.7 and $93.7 million, representing an annual growth rate of 16% at the midpoint. We expect non-GAAP operating income to be between negative $4.5 million and negative $2.5 million. And we expect non-GAAP net income per share to be between negative $0.02 and negative $0.01 assuming basic weighted average shares outstanding of approximately 135.1 million.

For the full year of 2026, we expect full-year revenue to be between $390 and $398 million, an annual growth rate of 15% at the midpoint. We expect our full-year non-GAAP operating income to be between $7,000,000 and $13,000,000. We expect non-GAAP net income per share to be between $0.08 and $0.13, assuming weighted average shares outstanding of 145.9 million, as measured on a fully diluted basis. In closing, we are accelerating our pace of innovation, and we are 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 will open up for Q&A.

Over to you, John. Thank you, Andrew. We will now turn to Q&A.

John Streppa: For the sake of time, please limit yourself to one question and one follow-up. Our first question is going to come from the line of Taylor McGinnis from UBS, followed by Billy Fitzsimmons from Piper Sandler. Taylor, your line should be open. Go ahead.

Taylor McGinnis: Yeah. Hey, team. Thanks so much for taking the question. Maybe just first on, you announced a number of exciting agent offerings this week. And at the same time, you know, you have also seen good traction with third-party agents connecting into Amplitude’s platform. So and then, Spencer, you showed, you know, a really good example of being able to extract insights using Anthropic Claude. So I guess how do you see Amplitude’s agents and these third-party agents evolving? Maybe you could just talk about the differentiation that you anticipate with agents versus what is being done with the third-party agents today.

Operator: Yeah. So to be clear, they both use the same underlying infrastructure. What will happen with either MCP, Model Context Protocol, is a way for external products like Claude or OpenAI’s ChatGPT or Cursor to connect into Amplitude, Inc. and request a set of calls. But that is the same infrastructure that both our global agents and our specialized agents use. And so the way to think about it is there is a whole set of tool calls that are available to these agents. You can say, get me a list of events. Get me retention. You know? Get me the list of tools you have like Retention and Funnels. Get me the possible properties for this event.

And what we will do is we will expose that to an orchestrator that we have that basically interprets a query, whether it is in the chat with Global Agent or whether it is external from MCP. And then it will pull in all the different context I talked about and then, you know, spit out the answers.

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

Andrew Casey: So first, I would say Q4 was a great quarter for new logo ARR. We had a lot of new customers that are getting value from Amplitude, Inc., and they are starting their journey with us. Those tend to be ones which you are working throughout the quarter, and there was a large proportion of ARR that was booked later than we have seen in prior quarters. And as we mentioned before, we did not see a lot of really big expansions during the quarter. So it was one of those areas where you are building a lot of opportunity for future growth with these new customers. And, you know, it is always one where you are competing.

When you are going to do a new logo, you have to really compete and show value, and sometimes those take a little longer as well. But we are really pleased with all the new customers that have become Amplitude, Inc. customers, and we think that sets us up very well for expansions in the future.

Taylor McGinnis: Great. Thank you guys so much.

Operator: Good to see you, Taylor. Thank you, Taylor. Thank you, Taylor.

John Streppa: Our next question will come from Billy Fitzsimmons from Piper Sandler followed by Rob Oliver from R. W. Baird. Billy, good to see you. Your line is open.

Unknown Analyst: Good to see you guys, and thanks for taking the question. I guess maybe to start, can you help us think through the NRR improvement? And how much would you attribute to greater upsells and cross-sells

Unknown Analyst: Versus maybe more success in mitigating some of the churn in the business?

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

We are past most of those capacity-related issues that we created for ourselves. We are starting to see customers and their data ingested into our platform contribute towards net dollar retention improvements as well. And so now as we think forward, and I have said in the past that we have full intention to continue to set up our customers and expand with our customers, introducing new innovation, we think that both factors, both data ingested into the platform, meaning upsells, as well as cross-sells will contribute to further improvements.

Unknown Analyst: Makes sense. And I guess on that note, if I could sneak in one more, can you give us a sense of the role volume upsells will play in the FY2026 growth algorithm, especially as you start lapping some of the contract rightsizing from the first half of last year?

Andrew Casey: So one of the things we talked about in the call was introducing our new pricing and packaging that is aligning not only to our enterprise motion, but also towards the implementation of our new AI products. And in the past, I would say there were certain times where customers felt very leery about the amount they would have to pay based on increasing data rates ingested into the platform. Meaning, those rates were so high they would not be able to see the benefits associated with marginal incremental reductions in the cost of that data.

Well, our new pricing and packaging structure rewards our customers now for adding more and more data into the platform so that they are paying marginally incrementally less. Now that does not mean that it is not going to contribute growth to Amplitude, Inc. as our customers are getting greater value by ingesting more data to the platform. We believe it is fair for us to have some of that fair exchange of value.

But if you were going to ask where we are really focused on driving NRR and where the biggest benefit will be, it will continue to show from those cross-sell opportunities, that expansion of our products because we want our customers to not fear adding more data. We want them to take advantage of implementing more data into our platform, and we want that to scale, especially as they look at longer-term contracts with us.

Unknown Analyst: Perfect. Thank you. Appreciate it.

John Streppa: Thank you, Billy. Our next question will come from Rob Oliver from R. W. Baird, followed by Clark Wright from D. A. Davidson. Go ahead, Rob.

Rob Oliver: Great. Thanks. Can you guys hear me okay?

Andrew Casey: Yep. We hear you right there.

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

Andrew Casey: Yeah. It is a great question. We spend a lot of time working with our sales team and our customers and showing how, one, the instrumentation within the platform can give them great visibility into the data they are ingesting within it, and work with our sellers to help them better understand, you know, as the marginal incremental data into the platform grows, how that then translates into the cost that we are going to be charging to our customer. We are encouraging to have those conversations as part of the sales process.

It is a kinder, gentler way of showing and working with the customer on how they are going to adopt Amplitude, Inc. over a period of time rather than guessing what their data implementation of the platform is going to be. We are working with them very closely on it and showing how the instrumentation works. Now the piece that I think is really important, and you touched on it, but I think it is worth emphasizing: we did a lot of work with customers to understand whether we had the right billing metric, whether it is something that they aligned to the value proposition. We have been testing for quite a while.

In fact, nearly 20% of new ARR that we booked in the quarter was actually using our new pricing and packaging in pilot stage. So we already know that customers like this. We already know that customers look at it as more transparent. They look at it as less friction, as you were saying. We also believe it positions us very, very well given that our focus on implementing AI products into our platform is, one, reducing the barriers to adoption.

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

Rob Oliver: Great. Super helpful. And then, thanks, Andrew. Then, Spencer, one quick one for you. Just on InfiniGrow, you guys were very early to the AI acquisitions among our coverage, I think, and very aggressive on it. And, you know, in particular, it looks like to us like this gives you guys a further opportunity to sort of go for that consolidation play that you guys have talked about. But if you could help us maybe understand what in particular, what area or what response to what customer need InfiniGrow is going to help address and how that might accelerate that platform opportunity. Thank you. There were two big things that stood out to us on the InfiniGrow team. I mean,

Operator: So first, we are just always looking for great talent out there. And so when the right company and the right opportunity comes along and they are aligned with our vision and excited about it, we are going to act. With InfiniGrow in particular, there were two big things that stood out about the team. So Daniel, the CEO there, as well as the rest of the group, they have been in it on AI analytics and automating workflows for the last few years and have a ton of perspective on how the future of the category is going to be shaped. And we are in uncharted territory. Like, we are inventing something new here, AI analytics.

And so whenever you get a chance to partner with someone else who has thought about that so deeply, it is a huge deal, and we want to figure out a way to work with them. So that is the first one that really stood out about InfiniGrow.

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

Rob Oliver: Super helpful. Really helpful. Thanks, guys. Thanks, John. Appreciate

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

Rob Oliver: Awesome. Thank you. You noted the cross-selling opportunities continue to be an area of strength. What is the natural pathway you are seeing in terms of product adoption? And what is the role that agents are going to play going forward to help drive additional cross-selling motions? I mean, it is great on both fronts. So analytics is

Operator: The core. We are an analytics platform, something we have been very consistent about. You want to be able to track the core, the base foundation of the user journey, and that makes every single other part of the platform more valuable. So it makes it easier to do experiments because you can target users as well as measure those more effectively. It makes it better to do session replay because you can understand, hey, for a group of users that ran into this error, let me see what they did by looking at the session replays. It makes Guides and Surveys better because you could target guides to specific users based on if you see them confused.

So analytics is the core, and all of these become more valuable with analytics and vice versa. In terms of agents, I think the big opportunity there, and I just showed the session replay one, is that these other products, while we launched AI analytics yesterday, that was the main focus, these other products are actually capable of being leveraged by AI. So the session replay specialized agent demo that I shared earlier is a great example where you can watch one, two, three, maybe ten session replays, but watch a hundred, it will take you a few hours to get through them.

And so to have an agent speed up that analysis, still get all the valuable data, summarize it up, and put it back to you, you are talking a 100x fold increase in productivity versus what you might otherwise do. Experiments, the same thing. I mean, one of the things that people ask us a lot is like, do you have a library of best practices for what sort of web pages or what sort of interactions work and what do not? And our experiment conversion agent will actually suggest those based on best practices of what we know from all the companies that we work with. And so it makes experimentation a lot more powerful too.

And then the really cool moment is when these all tie together. So you can start out in analytics and say, okay, give me my unhappiest users and suggest ideas for what I could do to improve them. Then it says, wow, okay, all of these users, they were unhappy because they ran into a page that was not working. And then you could have session replay agent come in and say, okay, well, let us look at what was it on that page. It is like, oh, okay, hey, this

Unknown Analyst: Button

Operator: Is not formatted correctly and was not labeled, and so that is probably confusing the users. And then you can go even further and say, okay, great, can you propose a variant, an experiment variant to me that would actually fix it? And then it will propose it, and it will propose another web page, you can run the test. And so you can not know anything about analytics, not know anything about your data taxonomy, not know anything about how to use session replay, not know anything about how to do experimentation A/B testing, and do all the work of all of those products from the global agents or specialized agents interface.

So it is going to be a massive unlock in terms of the usage. We are obviously most focused on analytics right now, but I am really excited about some of the other things in the long run. It is funny. We already got some comments on Twitter like, hey, why does it only watch a hundred sessions at a time? Why can’t you watch a thousand or ten thousand? Like, alright. We are working on it. We are working on

Unknown Analyst: So

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

Andrew Casey: I would say the pricing and packaging is relatively new, so I would not contribute that necessarily to increasing win rates. I think that the biggest thing is, one, our sales team has just worked really hard at demonstrating the value of our platform to our clients, and that is really resonating. And the other is you really have to credit our product team for creating just really great products that work well together. You know, a lot of people claim they have a platform, but the reality is it is a bunch of products that are stitched together. It does not work really well.

When you have a platform, you have workflows that are instrumented well, and it is easy to interact with the different modules in the product. And that is the way I would characterize our platform today. And every time that customers are adopting more than one product, it is because that integration, those workflows seamlessly across our platform are coming through as real value. I talked to a number of customers myself, with the sales team, and they always come back and say, we are just so far ahead of what everybody else is even representing, you know, an analytics platform to be.

Operator: On that, if I just go through the last 30 buyers I have talked to in the last month, all they want to do is be educated about analytics, and how AI is going to transform analytics and the whole platform. And they see it coming. They see tons of automation ahead, and they are like, hey, teach me how I can be relevant. And so when we can offer that to them by, one, providing a view on how the future unfolds, and then, two, offering them the products, tools, and services to actually enable them to be successful and relevant, they want to spend a ton of time with us.

And so the competitive question, especially against the smaller point solutions, is going away. It really is just, is now the right time, and can you help me get to this future fast enough and teach me?

Unknown Analyst: Awesome. Thank you.

John Streppa: Very good. Thank you, Clark. Our next question will come from the line of Koji Ikeda from Bank of America, followed by Jackson Ader from KeyBanc. Go ahead, George.

Unknown Analyst: Hey, Spencer and Andrew. Appreciate you taking our questions. This is George McGrión on for Koji. Taking a big step back, one from me on the big picture: where can we expect agentic queries to grow to in the mix from 25% today, maybe over the next twelve to twenty-four months?

Operator: Yeah. I mean, none of us have a full crystal ball, but my expectation is the vast majority are going to be done agentically, where you are just going to have agents that run over your data all the time. They are looking at dashboards. They are looking at KPIs. They are trying to find underlying root causes of why things are changing. They are creating suggestions for your product. They are reviewing session replays. They are constantly trying out and tweaking new experiments. So I mean, I do not want to put a number out there, but I think the vast majority.

What we are seeing generally is, if you look at query growth from direct usage of the Amplitude dashboard, it is increasing in line roughly with the size of our business. If you look at agentic query use, it is skyrocketing, as you saw on that chart in the last few months. And so the amount of leverage, I think the same thing that happened to coding in the last two years, where if you look at it, the best software engineering teams, the majority of lines of code are produced by agents. It is mostly humans editing and interpreting them, stitching them together, and giving high-level direction. And that is where the best software engineers are.

I think the same thing is going to happen in analytics and data analysis, where the vast majority of the data munging of the tool and figuring out what query means what thing and how do you do a segmentation, understand the root cause — that is all going to be automated by agents. And our goal is to be the first company to do that in a big way.

John Streppa: Thank you, George. Our next question will come from the line of Jackson Ader from KeyBanc, followed by the line from Scott Berg from Needham. Nate, I believe you are on for Jackson. Your line is open.

Unknown Analyst: Great. Hey. This is Nate Rose on for Jackson Ader. Thanks for taking our questions, guys.

Andrew Casey: Hey, Nate.

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

Unknown Analyst: Well, I will tell you first and foremost,

Andrew Casey: We have been on this path where we are increasingly driving revenue growth faster than we are driving expense growth. And rooted within that are efforts on changing our go-to-market, changing our processes, modernizing our own application architectures, doing the basics of running the business in a very efficient way, so that we can continue to drive growth with leverage. Those same things are not just a one-time event. You continue to focus and learn and understand how you can drive and deliver services more effectively.

And so we look at the path we have in front of us with respect to our growth opportunities, what our pipelines look like, how well we are managing our cost to serve, with the expectation that sales and marketing will continue to improve on their efficiency and G&A will continue to drive efficiencies as well. So it all culminates in the plan that we put together for 2026.

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

Operator: Yeah. I would call it two things. One, it becomes the bottleneck. So you remember that loop I showed at the start where it is like, okay, you are shipping all the software. Is it good? Are we even going in the right direction? So the comparative value of the analytics piece becomes a lot greater and higher urgency. When you have a year-long roadmap, it is okay if it takes a while to measure the success of it. But when your iteration cycle is measured in weeks or days like it is with the best of the best companies now, you need to know if you are going in the right direction all the time.

And then I think the other thing is the buyers, in addition to that being the pinch point and the big need in terms of the next big step in product development, they intuitively all know that this whole space is going to get reformulated with AI. And so they are just desperate for education and someone to show them the way. This is not a case of, like, I think one of the differences between selling SaaS and selling AI is in the SaaS world, it is very much like, okay, talk to your customers, get a list of prioritized features from them and build it, and you go back and sell it to them.

In this AI world, they do not know. They are like, is the model capable of this? Can it automatically look at a session replay for me? Can it analyze the root cause of a breakage in my funnel? And what is the best way to make that happen? And so they are looking at us for all those questions, and this is where sharing the vision of what the future is, as well as being close to the bleeding edge of technology, is super important.

Unknown Analyst: Perfect. Very helpful. Thanks, guys.

John Streppa: Thanks, Nate. Our next question will come from the line of Scott Berg from Needham, followed by Nick Altmann from BTIG. Ian, I believe you are on for Scott. Your line is open.

Unknown Analyst: Hi. This is Ian Black on for Scott Berg.

Rob Oliver: With the new pricing and packaging, are you planning on separately monetizing your AI agents?

Andrew Casey: So most of our AI agents are embedded within our core platform. And so what you see there is we are giving access to customers to utilize more of the platform. That exemplifies all the power of our modules together. So there is a high propensity customers who are utilizing our AI agents are both ingesting more data into the platform as well as expanding into other modules. Now we are also going to introduce new products. We have done really well at innovating, and some of those products will come out with workflow charges as well. So we are not worried about the ability for us to monetize our AI capabilities. We are actually

Operator: Awesome. Thank you, and congratulations. Thank you. Thanks, Ian.

John Streppa: Our next question will come from the line of Nick Altmann from BTIG, followed by Elizabeth Porter from Morgan Stanley. Go ahead, Nick. Hi. This is John Gomez on for Nick Altmann. Thanks for taking my question. With the shift to agentic, democratizing the end user and product analytics, can you just talk about whether you are seeing new users or new lines of business leverage Amplitude, Inc. and how that is shifting the go-to-market. So just any commentary on new end users and how that is impacting how you think about the go-to-market strategy would be helpful. Thank you. It is not really new end users. I mean, it is the same. You are talking about product teams.

You are talking about marketing teams, engineering and data teams. And so it is the same people trying to leverage the data. What they are really, again, what they are really desperate for is education. And so when we can show them Global Agents, specialized agents, MCP, AI feedback, AI visibility, what we are doing with our next products on Assistant and LLM Analytics, there is always a whole bunch they want to grab onto and say, okay, great, teach me how to use this, make it successful, everything else. So that is the biggest difference.

It is one where our go-to-market is about training, getting them to be able to educate, to be able to share the vision, to be able to demo these products and make customers successful. Thank you, John. Our next question will come from the line of Elizabeth Porter from Morgan Stanley followed by Y. C. Wong at Citi. Go ahead. No. You are not Elizabeth. Go ahead. Yeah.

Operator: Hey, guys. I am Lucas here for Elizabeth Porter tonight. Thanks

Unknown Analyst: For taking my questions. So with the uptick in new app development that we have seen over the past few months, could you walk through your expectations for balancing this potential new demand from smaller customers with your move up market as you evolve your go-to-market strategy?

Spenser Skates: Yeah. We are doing both. I think one of the things we see on the

Unknown Analyst: On the startups and newer customers is they are very bleeding edge, so they are trying to push the capabilities of us. And so we have always had a motion where we have taken innovation that we have done with them and bring it to the enterprise in a deliberate way. So we are going to continue to do that. I think there are actually massive opportunities, particularly with the rise of vibe-coded apps. You know, there is going to be vibe-coded analytics too that needs to go along with all those applications. So, you know, it is early, but there is a big opportunity for us there too. You know, again, the core

Operator: Thing

Unknown Analyst: You want in terms of understanding your customers and knowing if you are going in the right direction and building the product is the same whether you are the newest startup that was just founded yesterday or you are a 100-year-old long-lasting business. And so for us, it is like, we are here to serve all of them. And, again, they are very keen on learning the bleeding edge of what is happening in AI analytics. And so if you are able to teach them that, then it does not matter your size.

Operator: Got it. That is super helpful. Then

Unknown Analyst: Could you speak to the 7-figure deal pipeline in 2026? And then are there any specific verticals in which you see outsized growth already?

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

Unknown Analyst: Awesome. Thanks, guys.

Spenser Skates: Thank you, Lucas. Our next question will come from Y. C. Wong from Citi, followed by our last question from Arjun Bhatia at William Blair. Go ahead, YC. Hey. Thanks for taking my question. Congratulations on the

Andrew Casey: Pretty strong close to the year here. I guess maybe I would just want to touch on, Spencer, you talk about Amplitude, Inc. being one of the databases of user behavior. Just given the rapid progress of agent capability across our data platform players called Snow and Databricks, where we are seeing also customer consolidation towards maybe bigger data platform players. Curious if you are seeing any, like, I think customer blur of your analytics use cases from Snowflake and Databricks versus Amplitude, Inc.

Unknown Analyst: I want to make sure, YC, I want to make sure I understand what you are saying. You are saying do we see competition from Snowflake and Databricks because of data too?

Andrew Casey: Well, it is not just data. They are thinking about doing app as well. I mean, if you think of white coding and then you think of application building, you can make it easier to build. I am just curious if you see any blurring between customer talking about just use cases between customer — you can use a data platform like Snowflake to build it. They have Cortex. Versus what you will see with Amplitude, Inc.

Unknown Analyst: So one of the big things that we see is that customers always want the most advanced and bleeding-edge capabilities. I heard this great analogy the other day where software is very much like sushi. So, you know, it is fine that the gas station at 7-Eleven offers it, but, you know, Jiro in Japan is probably not going out of business. In fact, it is going to create more demand for him. And so from our standpoint, what we think about is how can we offer the most advanced and robust system for analytics.

So if you look at the benchmark that we, with hundreds of evals, released where we got a 76% accuracy rate, if you look at the Cortex or you look at Databricks Genie, I mean, they are going to be in the 10% or sub that. We are working on releasing full metrics on that. And that is because the text-to-SQL is only really one part of it. There are two other big parts. The first is the context layer. So what data sources are you bringing together in the right way? Analytics data, session replay data, data from interactions in guides and surveys, data from other sources, and interpreting those in the right way.

And then giving an LLM agent the right set of tool calls so that they can iteratively query. Okay, hey, what is my onboarding funnel? Where is the biggest drop on it? Why is the biggest drop on it? What is the biggest difference between users who went to the next step versus the previous step? So that is, just in that example, four queries that you are going to have to do in a row all correctly. And to do that, you need to prompt the LLM in a very particular way. You need to give it the right tool calls. You need to give it the right context.

And so we have thought really deeply about this because we have the largest repository of user behavioral data in the world. We have seen millions of analytics queries, what good looks like for millions of analytics queries, and translated that into an agent that does the same. And so because you are going to need to give it all that context and then be able to iteratively query a data system, the differences in accuracy are really stark if you were just to roll your own or use a Genie or a Cortex versus using an Amplitude, Inc.

And when you are an analyst, that difference between 76% and 10% is a massive difference in terms of your ability to leverage agentic analytics.

Andrew Casey: No. That is helpful color. Maybe one for Andrew. The profitability definitely came in well ahead of expectation here. Maybe you guys are leveraging some agents internally to help you drive better sales efficiency. But curious to see, going into next year, what can we expect, especially on the free cash flow that outperformed probably by about four points. Curious to see what is your expectation into next year and any other moving parts that we should be aware of or headwinds to be mindful of from the strong performance this year? Thanks. I think what you are seeing is that

Spenser Skates: That

Unknown Analyst: The efforts we have been doing on sales and marketing, on our cost to serve, in our G&A, and operating more effectively as a company is not just one effort, one activity. There are multiple. And certainly, we are introducing agentic capabilities into our own workflows within the company, and that is certainly contributing too. But there are so many things structurally we have done to the business to create greater durability that is ending in greater abilities for us to drive efficiencies. I will give you one example. We have talked a lot about our ability to drive increasing contract duration with our customers and that our RPO has been growing rapidly.

Well, if you do not have to renew your install base every year, or that install base percentage goes down because you are executing more and more longer-term duration contracts with your customers, then the sales team has more time to dedicate towards selling new expansion deals rather than working on renewals. And so this is just a great example of a strategy we put in place that is going to accrue benefits for a longer period of time.

Spenser Skates: Great. Thanks. Thank you, YC. And our last question is going to come from Arjun Bhatia at William Blair. I believe Willow is on. Your line is now open.

John Streppa: Yep. Thank you. Hi, Willow on for Arjun Bhatia, and thanks for fitting us in and taking our question. Andrew, in terms of guidance, the full-year revenue range seems a bit wider than normal at $8,000,000. Can you help us understand the reason for this? And what scenarios are contemplated at the low and high ends of the range?

Unknown Analyst: I think when we approach our guidance, we approach it with what we think we can go execute in the period. And I would not read too much into that other than, you know, we have a breadth of different opportunities that we are going after both with our product set and with improvements in our targeting enterprise customers. So I would not read too much into it.

John Streppa: Understood. Thank you.

Operator: Right.

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

Operator: Take care.

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