Full Transcript: Amplitude Q2 2026 Earnings Call

Amplitude (NASDAQ: AMPL ) released second-quarter financial results and hosted an earnings call on Wednesday. Read the complete transcript below. APIs provide real-time access to earnings call transcripts and financial data. Visit to learn more.

's Q2 2026 revenue was $101 million, up 21% year over year, with total ARR at $410 million, a 22% increase. 7 million. The company has transformed into an AI-native business, focusing on integrating AI across engineering, product management, and marketing. AI initiatives have led to significant operational efficiencies, such as reduced bug reports and faster pull request cycles.

Amplitude has introduced new products: Amplitude, Statsig, and Wave, which support AI-driven product development and analytics. The company is simplifying pricing and packaging to encourage enterprise adoption. The acquisition of Statsig is contributing to growth, with its ARR addition surpassing expectations. Amplitude is focused on integrating Statsig and leveraging its platform for further innovation and customer expansion.

6 million and $108 million. 2 million, reflecting confidence in growth through AI adoption and product consolidation. Management emphasized the strategic importance of AI in driving customer engagement and expanding the addressable market. They are committed to maintaining a 20%+ operating margin in the long term.

Full Transcript John Strepa, Head of Investor Relations Good afternoon, recording in progress and welcome to Amplitude's second quarter 2026 earnings conference call. I'm John Strepa, Head of Investor Relations, and joining me today are Spenser Skates, CEO and Co—Founder of Amplitude, and Andrew Casey, Chief Financial Officer. During today's call, management will make forward—looking statements including statements regarding our financial outlook for the third quarter and full year 2026, the expected performance of our products, our expected quarterly and long—term growth investments, and our overall future prospects.

These forward—looking statements are based on current information, assumptions, and expectations and are subject to risks and uncertainties, some of which are beyond our control, that could cause actual results to differ materially from those described in these statements. Further information on the risks that could cause actual results to differ is included in our filings with the Securities and Exchange Commission. You are cautioned not to place undue reliance on these forward—looking statements, and we assume no obligation to update these statements after today's call, except as required by law.

Certain financial measures used on today's call are expressed on a non—GAAP basis. We use these non—GAAP financial measures internally to facilitate analysis of our financial and business trends and for internal planning and forecasting purposes. These non—GAAP financial measures have limitations and should not be used in isolation from, or as a substitute for, financial information prepared in accordance with GAAP. com.

And with that, I'll hand the call over to Spenser. Spenser Skates, CEO, Co-Founder Thanks, John, and good afternoon, everyone. Welcome to Amplitude's second quarter 2026 earnings call. Today I'll cover three things.

First, our Q2 results. Second, how we transformed Amplitude into an AI company and why every company I talk to now wants to learn how they can do the same. Third, a look at our product and a spotlight on our customer. Let me start with the numbers.

Q2 revenue was 101 million, up 21% year over year. Total annual recurring revenue was 410 million, up 22% year over year and up 36 million from last quarter. That was made up of two parts: inorganic ARR from Statsig of 17 million and organic ARR growth of 19 million. Andrew will walk through the details.

5 million. Customers with more than 100k in ARR grew to 824, an increase of 30% year over year. Both AI natives and large enterprises are driving this growth. Let me step back and tell you about our transformation and then how we're helping customers along their AI journeys.

We help companies build better products. Every company wants to transform to deliver software products in an AI—native way. We've made that transformation at Amplitude over the last two years, and now our customers are looking to learn from us. Becoming an AI company starts with the organization.

Two years ago we first transformed our engineering team by bringing in AI engineers who built with it for years. Then we moved into adjacent functions like product management, design, and the more technical parts of go—to—market. We also brought in AI expertise through acquisition. Founders and other members of the team from these companies have taken leadership roles across Amplitude.

I have focused on bringing in leaders who are former founders and who have a technical background. Gab, our Chief Product Officer, started multiple companies including Loom Systems, which sold to ServiceNow in 2020. In addition, Nate, our Chief Commercial Officer, has a degree in math and physics and started his career as an engineer, programming in C and Java and building databases. Most recently, we added Angela Ferrante as SVP of Marketing.

Angela founded Laudable, which went through Y Combinator Summer 2021, sold it in 2025, and is a technical marketing leader who builds apps with AI in her spare time. In addition to all of this, we're continually re—educating everyone at Amplitude through initiatives like AI Week, Unlimited Token Spend, and a Living Token Leaderboard. This has all resulted in three times the number of pull requests in six months. We've reduced our pull request cycle from five hours to 44 minutes.

Bug reports are down 55%. Five percent of our pull requests are submitted from designers and product managers with no engineering involvement. We've leveraged AI to shorten our closing process by a day. We built customer health dashboards that enable our sellers and leaders to track customer usage, bringing our own Amplitude data alongside Salesforce data and data from other sources.

When I talk with our customers, they are all focused on how they can transform their business to be AI—native like we have done at Amplitude. The AI landscape is changing rapidly and they want to learn how to adapt. Our customers are on a spectrum of AI adoption. Our job is to meet them where they are and then educate them on how to take the next step.

We work with leading AI companies to learn what the bleeding edge in product development looks like. We use that knowledge to educate the rest of the market, including the largest enterprises deploying at scale. More than 40 AI—native companies now pay us over $100,000 a year. Those customers include Harvey, Midjourney, Character AI, and one of the leading foundational AI model companies.

On the enterprise side, enterprises are now more than 68% of our ARR. This quarter included agreements with Paramount, Jaguar Land Rover, and Domino's Pizza. We've improved our pricing and packaging. We reduced down to a single meter to make it simpler for enterprises to add additional products.

We increased the amount of data on our free plan so we're the best for those just getting started. Amplitude has the best pricing whether you're a startup or a large enterprise. One of the biggest changes with building an AI—native company we're seeing at Amplitude and with our peers in private markets is in the cost structure. A lot of inference spend is required in order to deliver AI—native products, which increases the amount spent on cost of goods sold.

On the other hand, you do not need to add as much operating expense to continue to grow a business at scale. We are embracing this change in cost structure as part of our transition to an AI—native company. For now we expect gross margins to stay in the low 70s. We will offset that with a commensurate reduction in operating expenses that allows us to continue to show the same leverage in operating income as we have planned.

I am continuing to drive Amplitude to a 20% plus operating margin business over the long term. We offer three products to meet customers wherever they are on their AI journey. Amplitude gives you the deepest understanding of how people use your product. Our agents increasingly do that discovery for you.

Statsig gives you feature flagging and experimentation, built on the world's most advanced SaaS engine with an engineering—first view. It's also integrated natively with data warehouses. Wave is the future of product development: self—improving products where we automatically recommend what to build next based on signals from users. While we're early here, I'm actually excited to show you a demo today.

Together these three products close the product development loop: understand what's happening, measure what ships, and ship what matters. That loop is how AI—native business is built. Let me go deeper on Amplitude. Global chat is becoming the primary way our customers interact with their product data.

You ask it a question in plain language and it does the analysis—no dashboard building required. It's become the de facto way many companies do product analytics. Global Agent finds the root cause behind 75% of customer questions and hands you the answer. 3 million Global Agent interactions every week and root—cause discovery rates are improving by 1 percentage point every month.

As of today, over 40% of all insights come from AI agents as opposed to humans, and we expect this to continue to grow. Today for our demo I want to show you custom agents, Statsig, and Wave. Let's start with custom agents. Custom agents are teammates that automate recurring workflows on your product data and push that work to other tools and systems.

This is our chat interface. An increasing number of users are interacting with Amplitude mostly through chat and agents. I'll ask a question: Which group of users are most likely to purchase next week? Chat can now write its own code to perform this analysis.

This unlocks the ability to run deeper analysis and create more powerful new graphs and artifacts, including diagrams like you see here: out—of—time decile lift, an ROC curve, segment propensity. You can dig in by seeing the actual code used and step—by—step analysis. This type of deep analysis has never been available before in analytics tooling; we are no longer bound by the constraints of a UI. We can also create automatic and recurring agents that run in the background.

I give it these instructions: I want this analysis run every Monday morning. Cross—reference with marketing activity and Confluence. DM me the results in Slack. Amplitude then creates the agent that you see here.

This is the entire prompt, including connectors to Atlassian and Slack. It will run regularly every Monday and push the results to me. We are building the best analytics agent across all data sources. Statsig is the leading product for experimentation and feature management.

Statsig runs experiments natively on your cloud data warehouse, whether that is Snowflake, BigQuery, Databricks, or Redshift. Let me show you what this looks like. Here is the results page for one of hundreds of experiments that an e—commerce customer is running. This experiment is testing a larger product image versus the default size.

There's a lot of statistical machinery behind a good experiment, but the UI makes it simple for an engineer to run. Up top, they can monitor exposure, which is saying is the experiment healthy or not? We expect to see a 50/50 split, so we're doing good, and as you can see here, we're getting a healthy check. We move to the scorecard that has the results.

This has a confidence interval of 95%. Statsig uses advanced techniques like CUPED and sequential testing that allows engineers to speed up time to decision. We have those turned on. In monitoring, we see specific events we're tracking.

For this experiment, we're seeing positive results. 3%. Cart conversion is up, total purchase dollars is up, while carts per session is down. For the rollout of this feature, we have a progressive rollout, starting with employees, moving to early access users, then early release, and a scheduled rollout for everyone else.

Statsig has a variety of advanced experimentation capabilities for rollout like feature gating, dynamic configs, and automatic rollbacks. Together these are the mechanisms that a team uses to ship a change, gradually tune it while live, and pull back automatically if it goes wrong. Last, I want to show you Wave, the future of product development. Wave allows for self—improving products that automatically recommend what to build next based on signals from your users.

Wave is magical. Wave looks across all the different data sources you have—analytics, experimentation, session replay, guidance, surveys, feedback, and many others. It then synthesizes that data into a set of product recommendations, plans those recommendations, and then helps you create those changes in your product. I'm going to walk you through a real example Wave suggested and built for Amplitude's documentation site.

On our documentation site, Wave found a spike in failed searches through looking at session replay and analytics data. The core problem was that search on our docs page fired on every keystroke. Typing a single letter to start a search returned an empty no—result state before the person finished typing their search, leading to a bad experience for users. Wave explains the reach of this issue: every user views a search.

It has an expected impact of decreasing total search failures by 80%. Then Wave has automatically created a visual example of the problem below so it's easy to understand. It also has a full explanation of the evidence for the plan. Wave sketches a wireframe of the recommended update, setting a three—character minimum and a 200 millisecond debounce to trigger the search.

Wave can also drive execution. It automatically created the pull request and Cursor wrote the code. Mark, our technical writer, was able to merge this pull request and ship this. No engineers, no designers, and no product manager.

Finally, Wave measures the results of the change. There is a massive decrease in total search failures. Simply amazing. Simply amazing.

Now let's talk about some of our customers. We had a great quarter with both new lands and expansions. We added or expanded our relationship with customers including Paramount Global, Jaguar Land Rover, Teladoc Health, Chime, Disney Ad Platforms, F5 Networks, Coursera, Grammarly, Kraken, and Crunch Fitness, among others. I want to tell you three stories about how these customers are leveraging our platform.

First is Coca—Cola FEMSA, which sells to hundreds of thousands of small shops across Latin America. Every shop is different, but for years they have had to run the same broad campaign to everyone because there is no way to tailor a message to that many retailers by hand. AI changed that. They began sending each retailer its own recommendation every week, written by AI.

Their own teams were actually skeptical. A different message for every shop every week felt risky, and no one knew if it was going to work. They used Amplitude to find out. Their AI campaigns actually had an 11% click—through rate, four times higher than their previous approach.

Our cohort analysis also showed that this lift lasted—once a retailer engaged, its revenue stayed higher in the weeks that followed. That evidence turned skeptics at FEMSA into believers, and they scaled from a 2,500—store pilot to 690,000 retailers. The second is Replit. Replit is an AI app builder that allows non—technical builders to turn an idea into an app using AI.

Replit has a large global user base of passionate builders that provide feedback. Replit is using Amplitude AI Feedback to understand how customers are engaging with their agents. They've connected AI Feedback to Zendesk, app store reviews, Twitter, and Reddit, and surfaced and prioritized what problems should be solved to increase their retention and engagement. It changed weeks of manual work on their end into a simple click with Amplitude.

This is the next generation of product development at work. Third is The Economist. The Economist is a print magazine that's in the midst of a transition to digital delivery and subscription. Their research arm built an AI assistant called Lens that answers questions for analysts and strategists using The Economist's content.

Their normal analytics could show what users did, but not whether the AI's answers were any good. The team was reading sessions by hand, but they couldn't keep up. Amplitude Agent Analytics now scores every answer Lens gives automatically. They went from reading a handful of sample sessions to being able to see across all of them.

9% task success rate and weekly failures are down 84%. That is the loop working—build with AI, measure whether it is good, and fix what is not. To wrap up, the companies on the bleeding edge are choosing Amplitude. We've transformed Amplitude to be AI—native and we're building the future on what can be done in analytics.

Self—improving products are closer than ever with Wave.