Title

Presentation

GitLab Inc. misses on earnings expectations. Reported EPS is $-0.3 EPS, expectations were $-0.14.

Darci Tadich: Thank you for joining us today for GitLab’s First Quarter of Fiscal Year 2024 Financial Results Presentation. GitLab’s Co-Founder and CEO, Sid Sijbrandij; and GitLab’s Chief Financial Officer, Brian Robins will provide commentary on the quarter and fiscal year. Please note, we will be opening up the call for panelist questions. [Operator Instructions] Before we begin, I’ll cover the Safe Harbor statement. During this conference call, we may make forward-looking statements within the meaning of the federal securities laws. These statements involve assumptions and are subject to known and unknown risks and uncertainties that could cause actual results to differ materially from those discussed or anticipated. For a complete discussion of risks associated with these forward-looking statements in our business, please refer to our earnings release distributed today in our SEC filings, including our most recent quarterly report on Form 10-Q and our most recent annual report on Form 10-K.

Our forward-looking statements are based upon information currently available to us. We caution you to not place undue reliance on forward-looking statements, and we undertake no duty or obligation to update or revise any forward-looking statement or to report any future events, or circumstances, or to reflect the occurrence of unanticipated events. We may also discuss financial performance measures that differ from comparable measures contained in our financial statements prepared in accordance with US GAAP. These non-GAAP measures are not intended to be a substitute for our GAAP results. A reconciliation of these non-GAAP measures to the most comparable GAAP financial measures is included in our earnings press release, which along with these reconciliations and additional supplemental information are available at ir.gitlab.com.

A replay of today’s call will also be posted on ir.gitlab.com. I will now turn the call over to GitLab’s Co-Founder and Chief Executive Officer, Sid Sijbrandij.

Sid Sijbrandij: Thank you for joining us today. I want to start off by thanking so many of you for the well-wishes I’ve received regarding my health. I’m doing well and I remain committed as ever to GitLab success. I’m pleased with how our business performed in the first quarter of FY’24. We exceeded our own guidance for both revenue growth and non-GAAP profitability. We executed well towards our goal of making our customers successful on our AI-powered DevSecOps platform. This quarter we generated revenue of $126.9 million. This represents growth of 45% year-over-year. Our dollar based net retention rate was 128%. Our first quarter results continued to demonstrate improving operating leverage in our business. Our non-GAAP operating margin improved by almost 1700 basis points year-over-year and we remain committed to growing in a responsible manner.

I want to start this call with one of the most exciting technology developments of our time. AI and ML. AI represents a major shift for our industry. It fundamentally changes the way that software is developed, and we believe it will accelerate our ability to help organizations make software faster. I’m excited about this new wave of technology innovation, and we continue to focus on incorporating AI throughout our DevSecOps platform. We’re innovating at a fast pace. In 1Q, we delivered five new AI features and in the first half of May alone, we delivered five additional features. All of these are available to customers now and we continue to iterate on Code Suggestions. This feature allows developers to write code more efficiently by receiving Code Suggestions as they type.

Code Suggestions is available on gitlab.com for all users, while in beta, we expect Code Suggestions will be generally available later this year. One of the guiding principles with Code Suggestions is to make it available and accessible to all developers everywhere. We also extended language support, so that more developers can realize the benefits of AI on our platform. In 1Q, we increased language support from the initial six languages to now 13 languages. Code Suggestions is uniquely built with privacy first as a critical foundation. Our customers proprietary source code never leaves GitLab’s cloud infrastructure. This means that their source code stays secure. In addition, model output is not stored and not used as training data. AI is not only changing how software is developed, it’s also amplifying the value of having a DevSecOps platform.

DevSecOps is a category that we created and we’re seeing it enter a mainstream adoption phase. We are seeing industry analysts recognizing this. I’m pleased to share that GitLab was recently recognized as the only leader in the Forrester Wave for integrated software delivery platforms 2023. We are excited to see the market mature and recognize the value of an integrated software delivery platform, a strategy that GitLab has followed from the start. This quarter we had many conversations with senior level customers, but one with a CTO from a top five European bank really stands out. At first, we focused on many of our differentiated features that only a DevSecOps platform can provide. For example, we talked about the benefits of value stream dashboards, DORA metrics and compliance on a single platform.

When the conversation moved into AI, the CTO said something extremely interesting. He said, cogeneration is only one aspect of the development cycle. If we only optimize cogeneration, everything else downstream from the development team, including QA security and operations breaks, breaks because these other teams involved in software development can’t keep up. This point, incorporating AI throughout the software development life cycle is at the core of our AI strategy. Today, our customers have the ability to use Code Suggestions for co-creation, suggested reviewers for code review. Explain this vulnerability for vulnerability remediation, value stream forecasting for predicting future team efficiency and much more. We’re proud to have ten AI features available to customers today, almost three times more than the competition.

Applying AI to a single data store for the full software development lifecycle also creates compelling business outcomes. We believe that this is something that can only be done with GitLab. We see a lot of excitement surrounding AI at the executive level. We are hearing from customers that AI is motivating them to assess how they develop, secure and operate software through a new lens. Enterprise level companies who may not have been in a market until 2024, 2025, 2026 are re-evaluating their strategies. On top of that, there’s new personas entering the mix. As chief information security officers navigate these new AI powered world, they are working to empower their teams to benefit from AI and apply appropriate governance, security compliance and auditability.

In all, we believe that AI will increase the total addressable market for several reasons. First, AI will make writing code easier, which we believe will expand the audience of people such as junior and citizen developers who build software. Second, as these developers become more productive, we see software becoming less expensive to create. We believe this will fuel demand for even more software. More developers will be needed to meet this additional demand. And third, we expect customers will increasingly turn to GitLab as they build machine learning models and AI into their applications. As we add ModelOps capabilities to our DevSecOps platform, this will invite data science teams as new personas and will allow these teams to work alongside their DevSecOps counterparts.

We see ModelOps as a big opportunity for GitLab. Expanding the addressable market will also create an opportunity to capture greater value. Later this year, we plan to introduce an AI add-on focused on supporting development teams. This new add-on will include Code Suggestions functionality. We anticipate this will be priced at $9 per user per month billed annually. This add-on will be available later this year across all our tiers. All of this innovation accentuates a broader theme for our business. The differentiation between a Dev and a DevSecOps platform. We believe that an AI-powered platform focused solely on the developer persona is incomplete. It is missing essential security operations and enterprise functionality. Remember, developers spend only a small fraction of their time developing code.

The real promise of AI extends far beyond code creation. And this is where GitLab has a structural advantage. We are the most comprehensive DevSecOps platform in the market. Features like Code Suggestions and Remote Development are important accelerants for developer efficiency. And today, GitLab has more AI features geared towards developers than our competitors. However, that isn’t enough. In order to achieve a ten times faster cycle time on projects, enterprises need an end-to-end platform that works across the entire software development life cycle. Let me describe some of GitLab’s key security operations and enterprise differentiators. For security only GitLab has dynamic application security testing, container scanning, API, security, compliance management and security policy management.

In operations, only GitLab has feature flags, infrastructure as code, error tracking, service desk and incident management. And for enterprises only GitLab has portfolio management, OKR management, value stream Management, DORA metrics and design management. Let me illustrate the value of a DevSecOps platform with one of our customers, Lockheed Martin. Lockheed Martin’s customers depend on them to help them overcome their most complex challenges and to stay ahead of emerging threats. Their customers need the most technologically advanced solutions. Lockheed Martin’s engineering teams require speed and flexibility to meet the specific mission needs of each customers. They also require shared expertise and infrastructure to ensure affordability.

Lockheed Martin has a history of using a wide variety of DevOps tools and needed to improve automation, standardize security practices and collaboration. They choose to go big with GitLab, greatly reducing their tool chain and cutting complexity while reducing costs and workload. Lockheed Martin team has reported eighty times faster CI Pipeline builds 90% less time spent on system maintenance. They’ve retired thousands of Jenkins servers. Lockheed Martin continues to grow with GitLab and is looking to migrate even more projects to their DevSecOps platform. One of their software strategy executives said by switching to GitLab and automating deployment teams have moved from monthly or weekly deliveries to daily or multiple daily deliveries. Lockheed is a great example of the power of a DevSecOps platform and we see this in other use cases as well, such as compliance.

In the quarter, a large health care provider purchased GitLab Ultimate for a platform features. They needed to meet specific compliance requirements from their auditors. They determined that GitLab is the best way to achieve their objectives. Another customer we expanded business with in Q1 is NatWest Group, a relationship bank for the digital world. NatWest Group is focused on delivering sustainable growth and results of fostering a better, simpler banking experience. Last year, NatWest Group chose GitLab dedicated. He wanted to enable their engineers to use a common cloud engineering platform to deliver a better experience for customers and colleagues. Five months into the program, we are pleased that NatWest has reported shorter onboarding times and productivity gains.

This led to NatWest choosing GitLab professional services to accelerate their transformation by supporting training certifications and developer days. In summary, we’re confident in a strong value proposition that GitLab provides to customers. GitLab is the most comprehensive AI-powered DevSecOps enterprise platform. The significant return on investment, quick payback period and well-documented positive business outcomes are resonating globally. We’re trusted by more than 50% of the Fortune 100 to secure and protect their most valuable assets. We also believe we’re in the early stages of capturing an estimated $40 billion addressable market, a market that we’ve seen evolve from point solutions to a platform from DIY DevOps to a DevSecOps platform.

And AI will speed up different aspects of software creation and development. This in turn creates the need for a more robust security compliance and planning capabilities. In today’s era of rapid innovation, the power of a platform like GitLab to enable faster cycle times truly shines. I’ll now turn it over to Brian Robins, GitLab’s Chief Financial Officer.

Brian Robins: Thank you, Sid, and thank you again for everyone joining us today. I’d like to spend a moment discussing the macro environment, the financial impact of our recently implemented premium pricing change and provide some insights into the financial impact of our AI products. Then I will quickly recap our first quarter financial results and key operating metrics and conclude with our guidance. Let me first touch on some of the watch points I discussed on prior calls. We continue to see sales cycles remaining at 4Q levels due to more people involved in deal approvals. Contraction improved over 4Q, but is higher than prior quarters. Like 4Q, contraction is driven almost entirely by lower seat counts with minimal down tearing.

I was pleased with the bookings predictability in 1Q. It was much better than 4Q. As we mentioned on the prior call, we raised the price of our premium skew for the first time in five years. Over that time frame, we added over 400 new features, transitioned from a Dev platform to a DevSecOps platform. We shared that we expected the premium price increase of minimum impact in FY’24 with greater impact in FY’25 and beyond. The price increase which took effect on April 3rd is going as planned. We only had one month of renewals impacted by the price increase in the quarter. To-date, customer churn is unchanged for the premium customers who renewed in April and our average ARR per customer increased in line with our expectations. Now on to the way we are thinking about the financials and the impact of our AI products.

We continue to invest in people and infrastructure to support AI. While we have had some teams working on AI features, we recently shifted additional engineers from other teams to support the work on AI. As a result, this has not led to significant incremental expenses on engineering talent. Additionally, we have made investments in our cloud provider spend to support our AI and R&D efforts. In addition, we also continue to leverage partners help drive our AI vision. This has included partnership announcements with Google Cloud and Oracle. The Google Partnership allows us to use Google Cloud AI functionality to make our own AI offerings better by leveraging their toolset. The partnership with Oracle makes it easier for our customers to deploy their own AI and machine learning workloads using Oracle’s cloud infrastructure.

Both of these partnerships help create strategic differentiation for our customers in a financially responsible manner. Now turning to the quarter. Revenue of 126.9 million this quarter represents an increase of 45% organically from the prior year. We ended 1Q with over 7400 customers with ARR of at least $5,000 compared to over 7000 customers in the fourth quarter of FY’23 and over 5100 customers in the prior year. This represents a year-over-year growth rate of approximately 43%. Currently, customers with greater than 5000 ARR represent approximately 95% of our total ARR. We also measure the performance and growth of our larger customers who we define as those spending more than 100,000 in ARR with us. At the end of the first quarter of FY’24, we had 760 customers with ARR of at least $100,000 compared to 697 customers in 4Q of FY’23 and 545 customers in the first quarter of FY’23.

This represents a year-over-year growth rate of approximately 39%. As many of you know, we do not believe calculated billings to be a good indicator of our business. Given that prior period, comparisons can be impacted by a number of factors, most notably our history of large prepaid multiyear deals. This quarter, total RPO grew 37% year-over-year to 460 million and cRPO grew 44% to 324 million for the same time frame. We ended our first quarter with a dollar based net retention rate of 128%. As a reminder, this is a trailing 12 month metric that compares expansion activity of customers over the last 12 months with the same cohort of customers during the prior 12 month period. The dollar based net retention of 128% was driven by lower seat expansion and contraction due to seats.

The ultimate tier continues to be our fastest growing tier, representing 42% of ARR for the first quarter of FY’24, compared with 39% of ARR in the first quarter of FY’23. Non-GAAP gross margins were 91% for the quarter, which is slightly improved from both the immediate preceding quarter for the first quarter of FY’23. SaaS represents over 25% of total ARR, and we’ve been able to maintain non-GAAP gross margins despite the higher cost of delivery. This is another example of how we continue to drive efficiencies in the business. We saw improved operating leverage this quarter, largely driven by realizing greater efficiencies as we continue to scale the business. Non-GAAP operating loss of 15 million or negative 12% of revenue compared to a loss of 24.8 million or negative 28% of revenue in 1Q of last year.

1Q FY’24 includes 5.6 million of expenses related to our JV and majority owned subsidiary compared to 3.7 million in 1Q FY’23. Operating cash use was 11 million in the first quarter of FY’24 compared to 28.2 million use in the same quarter of last year. Now let’s turn to guidance. We are assuming the macroeconomic headwinds and trends in the business we have seen over the last few quarters continue. There has been no change to our overall guidance philosophy. For the second quarter of FY’24, we expect total revenue of 129 million to 130 million, representing a growth rate of 28% to 29% year-over-year. We expect non-GAAP operating loss of 11 million to 10 million and we expect a non-GAAP net loss per share of negative $0.03 to negative $0.02, assuming a 153 million weighted average shares outstanding.

For the full year FY’24, we now expect total revenue of 541 million to 543 million, representing a growth rate of approximately 28% year-over-year. We expect non-GAAP operating loss of 47 million to 43 million and we expect non-GAAP net loss per share of negative $0.18 to negative $0.14 assuming a 153 million weighted average shares outstanding. On a percentage basis, our new annual FY’24 guidance implies a non-GAAP operating improvement of approximately 1200 basis points year-over-year at the midpoint of our guidance. Over a longer term, we believe that a continued targeted focus on growth initiatives and scaling the business will yield further improvements in unit economics. The guidance has us on track to achieve cash flow breakeven for FY’25.

For modeling purposes, we estimate that our fully diluted share count is 173 million. Separately, I would like to provide an update on JiHu, our China joint venture. Our goal remains to deconsolidate JiHu. However, we cannot predict the likelihood or timing of when this may potentially occur. Thus, for modeling purposes for FY’24, we now forecast approximately 29 million of expenses related to JiHu compared with 19 million in FY’23. These JiHu expenses represent approximately negative 5% of our total implied negative 8% non-GAAP operating loss for FY’24. Our number one priority as a management team is to drive revenue growth, but we’ll do that responsibly. There has been no philosophical change in how we run the business to maximize shareholder value over the long-term.

Before we take questions, I’d like to thank our customers for trusting GitLab to help them achieve their business objectives. Also want to thank our team members, partners and the wider GitLab community for their contributions this quarter. With that, we’ll now move to Q&A. To ask a question, please use the chat feature and post your question directly to IR questions. We’re ready for the first question.

Operator:

A – Darci Tadich: Our first question comes from Rob with Piper Sandler.

Rob Owens: All right. I think I did that correctly after three years of using Zoom. Good afternoon, guys.

Sid Sijbrandij: Hey, Rob. Good afternoon.

Rob Owens: Curious to hear an update on customer conversations. Obviously a stronger than expected quarter, but we are seeing this deceleration, I think, across all high-growth tech companies. So both Gen AI — in the macro, how should we think about pressure on net retention rates, customer acquisition that’s coming from customers taking a more prudent approach in the current budgetary environment versus, I guess, rethinking needs for Dev headcount and re-evaluating which Dev tools to purchase just given all the Gen AI innovations lately?

Sid Sijbrandij: Yeah. Thanks, Rob. And before I answer that question, maybe an update on my health. I just completed my last round of systemic chemotherapy. So happy about that.

Rob Owens: Congratulations.

Sid Sijbrandij: Thanks. And also no sign of detectable disease, and I’m excited about GitLab’s future and continuing my role as CEO and Chair. Yes, lots of things to unpack in your question. We see the macro trends continuing, and that’s putting pressure on seat count. That was the same last quarter, and we anticipate that trend to continue. At the same time, we’re super excited of what the macro is doing to the mindset of customers, because they say, hey, now we — it’s time to consolidate. And at the same time, we see that the analysts are seeing that, hey, this is consolidating as a market. So we believe that DevOps platform is going to be the way that people will consolidate. And we have the most comprehensive DevSecOps platform, which is also great if you look at the application of AI.

We’re able to apply AI not just for Code Suggestions, but apply it across the entire spectrum. We have more than 10 features that we were able to ship. And those 10 features, they drive value at every part along the stage. And as for how that influences the TAM, which you alluded to, we think AI is going to make it easier for more people to enter the fray. So we think it was a supply of more people using the product. At the same time, when you see that software development becomes easier, we believe there’s going to be more demand for it. Software development used to be very expensive. AI makes it more affordable. There’s going to be more demand. And more demand, again, means more people entering the fray. And last but certainly not least, it’s an opportunity for us to manage not just the code that companies have, but also their models.

And that’s what we do with our MLOps functionality. We already allow you to run experiments with GitLab. We want to extend to a full MLOps managed platform where we add the data engineers to the constituents that use GitLab.

Rob Owens: Great. And if I can sneak a quick one in for Brian. Just regarding DBOs in the linearity of the quarter, was that either large deals at the end? Or was it very back-end weighted? And if I look at that receivable base and assume collections on it, looks like you could turn the corner from a cash flow perspective relatively soon. So any commentary on turning free cash flow positive? Thanks.

Brian Robins: Yeah, I’ll touch on DSOs, and I’ll touch on free cash flow breakeven. And so from a DSO perspective, we were more weighted towards the end of the quarter. But the good news is that we — our amount of bad debt over the last three years has not exceeded 1%, and our age receivables has been very, very consistent. And so some of our European customers have requested Net 45, Net 60. And so we’ve accommodated that just because of the macro and the bad debt expense being so low. From a free cash flow breakeven perspective, we committed to be free cash flow breakeven in FY 2025. And we’ve also stated some of the actions that we’ve taken previously will accelerate our path to profitability, but haven’t given a specific time line on that.

Rob Owens: All right. Thanks, guys.

Brian Robins: Thanks, Rob. Darci, you’re muted.

Darci Tadich: Up next, we have Joel with Truist.

Joel Fishbein: Thank you. And Sid, I’m sending prayers to you, and congrats on making it through the treatment.

Sid Sijbrandij: Thank you.

Joel Fishbein: Brian, just a quick follow-up for you on Rob’s question. Congrats on the margin improvement. I think that’s — you’ve done a really good job. Can you give us a little bit more color on some of the things that you’re doing to continue to drive towards cash flow breakeven while still investing in some of these new initiatives that you’re doing, which obviously you’ve spent a lot of time talking about some of these AI programs that are coming out. And then just as a follow-up to that, have you like tested this $9 increased — license increase to your customer base and whether or not that they’ll — there’s going to be any pushback there? Thank you.

Brian Robins: Yes, Joel, absolutely. Thanks for the question. As Sid and I have always stated since we went public is the number one objective at GitLab is to grow, but we’ll do that responsibly. And we’ve tried to demonstrate that every quarter. And so nothing has changed in that front. Our non-GAAP gross margin percent went up to 91%, even though we continue to have really high SaaS growth and SaaS is greater than 25% of our overall revenue. And so we’re continuing to look at all areas within the business where we can optimize, but we aren’t doing that at the expense of growth because that’s the number one objective at the company. I think we demonstrated that across all cost categories and we’ll continue to look at that quarter-over-quarter.

On the $9 increase, we haven’t tested that yet. From a guidance perspective, most of the cost for that is in headcount and cloud costs, and that’s included in the guidance that we gave. And so we don’t expect any changes from a guidance perspective.

Joel Fishbein: Thank you.

Darci Tadich: Next, we have Sterling with MoffettNathanson.

Sterling Auty: Thanks. Hi, guys. Sid, congratulations as well on the completion of the treatments. Hopefully, you got a chance to actually ring the bell. Brian just — and Sid just another follow-up question just on the pricing. So you touched upon it, but I want to make sure to put a fine point here. Did it have any impact on win rates or length of deals where maybe customers were asking and negotiating a little bit harder because of the price increase? Or anything in terms of size of initial lands that may have been impacted because of the price increase? And if not, does that actually change when you think some of the benefits of the pricing increase will actually flow through the revenue line?

Brian Robins: Yes. Thanks, Sterling. I guess for everyone on the call, let me just briefly touch on the price increase. We haven’t raised prices in five years. And over that time period, we added 400 new features to the platform. And so that was the genesis of the price increase. The guidance we gave last quarter and today include the price increase. As you know, the price was effective in early April. And so we really only had a short period of less than a month for that. But I am happy to say that the renewal rates and the churn and the land of new customers have been better than expected. And so we’re happy with the results that we’ve seen in just that one month time period.

Sterling Auty: All right. Great. Thank you.

Darci Tadich: Our next question comes from Matt with RBC.

Matthew Hedberg: Hey, guys. Great. Thanks for taking my questions and I’ll offer my congrats, Sid. That’s the best news of the call, really good to hear you doing well. I noticed Ultimate ticked up. I believe, Brian, you said it was 42%, which, last year, was kind of flattish, really the whole year. I was curious what was driving that? Is that sort of AI showing up some of those migrations? Is it more of the not security? Or perhaps is it — is there any of the price increase on premium that’s maybe driving folks to Ultimate?

Brian Robins: Yes. Thanks, Matt. When we talk about Ultimate, as we said before, is we don’t set the sales compensation to basically compensate on Ultimate versus Premium. We want to try to take as much friction out of the process. For the consumer as well, we do the same on SaaS and self-managed as well. And so Ultimate, the strength in Ultimate is really based on the underlying value that we’re driving to our customers. The ROI on Ultimate, Forrester did a study, it was 427% over three years, and payback was around six months. And so when I looked at the quarter and looked at sort of Premium, Ultimate and sort of the breakout between contraction, churn, first order and expansion, Ultimate had — churn was consistent with a bunch of prior quarters.

Contraction was very consistent. Our growth was just as good as prior quarters, and we had a really strong first order quarter as well. And so Ultimate continues to do well. It’s our fastest-growing tier, and we’re happy with the results.

Matthew Hedberg: That’s fantastic. And then maybe just if I could follow up with one with Sid. One of the questions that we get from developer — from investors the most is, does Gen AI put pressure on Dev, developer seat count. I think you talked about a little bit in your prepared remarks, but maybe could you put a finer point on sort of the question of P times Q. And does the number of seats go down in the future? Or do you think it stays consistent or maybe even goes up?

Sid Sijbrandij: Yes. We believe that generative AI will expand the market. So first of all, you make the product easier. Like coding today is hard, and AI makes it easier. So we expect the citizen developers, these junior developers to start coding. That code needs to be managed somewhere. And that is in GitLab. The second thing is you make it — you — when a developer can do more, you bring down the price, and that should increase demand for development and software development activities. Third, what you have is today is a DevSecOps platform, but we’ve already articulated that we want to be a place where you manage not just code, but also MLOps. MLOps is the management of data and the management of models. Models are harder to manage than code.

They change over time, and they have a lot of risks, security risks, discrimination risks, risks that you’re doing the wrong thing, risk that they are outdated. So it’s a really interesting space to expand the product to. And for example, today, if you have an experiment in MLFlow, you can link it to the experiment in GitLab. And in the future, we’ll plan to come out with a model registry in GitLab. So those are all reasons why we think the market will expand. One other way to look at it is you have generative AI. It produces more code. All that codes also needs to be secured, also need to put in operations. So if you don’t have a good DevSecOps platform, you create a bottleneck at the beginning. That bottleneck is solved with the DevSecOps platform.

Matthew Hedberg: Thank you

Darci Tadich: We will now hear from Koji with Bank of America.

Koji Ikeda: Hey, guys. Thanks for taking the questions. Maybe a question for Sid or Brian here. I wanted to ask you a question about how you plan on attacking the other 50% of the Fortune 500 or I’m sorry, the Fortune 100 that you don’t have. Is it still a primary land-and-expand strategy? Or is it going to be more of a higher level sale for these customers? I was just kind of hoping you could dig into that a little bit more, please.

Sid Sijbrandij: Yes. I think it’s certainly that it is both the bottoms-up sale but also the top-down sales. So we have a direct sales motion, but also a channel sales motion that’s getting more important. Channel sales, think of our partners, AWS and GCP, where we work with them to go to customers. And we’re talking to CTOs, CSOs, CIOs, and we help them see the picture. What we commonly do as a value stream analysis. We point out all the different tools they use throughout the cycle and how that adds up in cycle time. And with GitLab, they’re going to save on tooling costs, they can save on the cost of integrating that tooling. They can make their people more productive, and they can go faster through that cycle and get initiatives out. So it’s certainly something we’re going to market with. And as you said, our goal is 100% of the Fortune 100.

Koji Ikeda: Got it. And maybe a follow-up here for Brian on kind of going back to free cash flow. This quarter, free cash flow is higher than non-GAAP operating income. And I recall there’s some cash flow mechanics around contract duration that should be mostly be out of the model by this point. So is that right with the cash flow mechanics? And does free cash flow trend higher than non-GAAP operating income from here on an annual basis? Just could you just dig into that just a little bit more for me, please?

Brian Robins: Yes, absolutely. When we joined — when I first joined the company, we were not incentivizing the sales force to do multiyear deals because we had such a high gross retention rate. And so we really pushed for one year deals in this. That’s why you saw billings and RPO is — go down and wouldn’t grow at the same rate as cRPO or short-term calculated billings. But we still continue to have prepaid multiyear deals within our existing book of business. And so as those contracts renew, you’ll see some lumpiness in our billings and collections, and Q1 was one of those quarters.

Koji Ikeda: Got it. Thank you.

Brian Robins: Thank you.

Darci Tadich: Next, we have Michael with KeyBank.

Michael Turits: Hey, guys.

Brian Robins: Hey, Michael.

Michael Turits: Can you hear me? Sorry about that.

Brian Robins: We can. Go ahead.

Michael Turits: So can you talk again you know Brian you said about how competition has gone. Microsoft, obviously, they have been very visible around Copilot. You announced a lot of features. But how has the sort of day in and day out competition gone. As you said, Brian, sales cycles have not extended, but are people sizing you up against each other and differently. How are they entering this discussion about whether or not [Technical Difficulty]

Brian Robins: Yes. I think I got most of it, Michael. And I think I’ll repeat the question was how has the sales cycle changed with between us and Microsoft, and what — if you had noticed any change — noticeable things within the quarter. So one thing to note this quarter is on last earnings call, I talked about how the first month of the quarter was very different than the second and third month of the quarter. This quarter is really predictable. And so I was happy with the predictability of the quarter. Week Three, we called the quarter and landed really close to that. The sales cycles in first quarter remained at fourth quarter levels. And so there wasn’t a lot of change there. As I talked about earlier, Ultimate being greater than 50% of the bookings and continue to do well.

I think that shows some of the differentiation between us and Microsoft. The hyperscalers as well had a great quarter as well. They grew over 200% year-over-year from a bookings perspective. And also this quarter, we had lower discounting than the previous quarter. And so the trends with Microsoft remain pretty consistent where we still don’t see any competition at about 50% of the deals. We see them in very little deals, but there is more discussion around OpenAI, ChatGPT and Copilot. All right. Darci, we’ll go into the next one.

Darci Tadich: Derrick with Cowen is next.

Derrick Wood: Great. Thanks. And Sid congrats on the news. I wanted to start, in the press release, you talked about an expanded partnership with Oracle and a new AI/ML offering, enabling customers to speed up model train and inference. Can you give us a little more detail around those new partner initiatives? And then just from a broader perspective, how you’re thinking about the Gen AI related revenue opportunities in the quarters ahead?

Sid Sijbrandij: Yes, thanks for the question. So we’re really excited about our partnership with Oracle Cloud. They have a great customer base. And what it means is that our customers now can now run AI and ML workloads on DPU-enabled GitLab runners on the Oracle Cloud infrastructure, and that’s a great powerful infrastructure. Additionally, we’re available in Oracle’s marketplace, expanding our distribution. So our strategy, with AI in mind, is to partner closer with the hyperscalers. And the toughest one is Microsoft. We try to partner there too, but with everyone else, we see a lot of momentum, and that’s AWS, GCP and Oracle. We want to get closer. We want to enable our customers to run their normal workloads, their AI workloads there, and where you can expect us to have more announcements going forward.

Derrick Wood: Okay. Maybe a quick one for you, Brian. Appreciate getting more exact numbers on net revenue retention rates. Kind of looking forward and with respect to your guidance for the rest of the year, is there any kind of target ranges that you’d guide us towards? Or how we should be thinking about trends around gross retention and expansion factors?

Brian Robins: We didn’t give out the specifics of those metrics. What I will say is this quarter — last quarter was more predictable. And so it makes it easier from a modeling perspective. And everything is factored into guidance. And so we didn’t give specific metrics for those.

Derrick Wood: Got it. Okay. Thank you.

Darci Tadich: Kash with Goldman Sachs.

Kash Rangan: Okay. Great. Thanks for taking my question. Sid good to see that you’re recovering very well and congratulations on the quarter. It looks like business stabilized for you guys. I had a question on the generative AI capabilities. At what point are we looking to — is there any need for further differentiation of GitLab versus the competition? This auto code generation feature that has been made much off, right? Is that a real sticking point in conversations? Do you think the customer base really values and appreciates the broader set of AI capabilities that GitLab has to offer? So it looks like there is a bit of a perception issue in the market that you don’t have those kinds of features that the competition appears to have.

If you can debunk that mix for us, that will be great. And then one for you, Brian, what does the month of May look like from a linearity standpoint? The net expansion rates that you saw as improving in the March quarter, it does hold up in the month of May as well. Thank you so much.

Sid Sijbrandij: Thanks, Kash. Like in AI, you have the code generation. If you just produce a whole bunch more code, then it’s going to get log jammed later down the pipeline. You also need to do more security fixes. You need to deploy more. So we’re really fortunate that we have a single application, a single data store for the entire DevSecOps cycle, and we can apply to AI to all of them. And that’s led us to having three times as many publicly usable AI features as our competition. That is a big advantage. As long as at the beginning that, of course, you also need the code suggestions. But having the whole rest make sure that if you get more effective there, it works, and you get a faster cycle time throughout and that’s a really exciting development.

Kash Rangan: And Brian I had one for you. Yeah, thank you.

Brian Robins: And just on the second part of the question, as you would expect, we track a number of metrics internally from top of the pipeline to bottom conversion rates, piecing, expansion, churn, contraction and so forth. And I’m happy quarter-to-date, things are as expected. And so like I’ve mentioned last quarter, it was more predictable in fourth quarter and quarter-to-date and we’ll see how the quarter finishes out, but it’s as expected on all those metrics that we track internally.

Kash Rangan: Great. Good to see the quarter and the results. Thank you so much.

Sid Sijbrandij: Thanks, Kash.

Darci Tadich: Next is Karl with UBS.

Karl Keirstead: Thank you. Maybe, Brian, I’ll point this to you. So as all of us try to run back of the envelope math about what the $9 per seat monetization plan might mean for fiscal ’25, can you offer any guardrails as to things we should keep in mind so we’re — maybe we’re a little bit tight on what it could mean. And I guess maybe as two quick follow-ups. Is there any reason to believe that it wouldn’t be applicable to all of your paying users? Or does it feel like it would be relevant only for a subset? And then on top of that, do you think this could actually accelerate the conversion of the free user base to the paid user base such that the opportunity set is beyond our estimate of what you’re paying user base looks like? Thank you.

Brian Robins: Lots in there to unpack. Just on FY 2025, we haven’t given out guidance for next year yet. And so I really can’t comment on that. The $9 that Sid talked about in the script is baked into our guidance for this year.

Karl Keirstead: Okay. But Brian does it — could it accelerate a free-to-paid conversion? I’m not asking you for fiscal ’25 guidance, just kind of framework as we try to model out what it could mean. Anything you’d offer up as we take our best shot?

Brian Robins: I think that all that we’re doing is to make the developer, the security and operations persona is more efficient and to allow and make better, faster, cheaper, more secure. And so I think anything that you do that enables that should help out on all the metrics that you track and model.

Karl Keirstead: Okay. Great. Congrats on the quarter.

Brian Robins: Appreciate it, Karl.

Darci Tadich: We will now hear from Jason with William Blair.

Jason Ader: Yeah. Hi, guys. Can you hear me okay?

Brian Robins: We can.

Jason Ader: All right. Great. I wanted to ask about whether you’re exploring a consumption element to your pricing model and how that might work, especially on the cloud side.

Sid Sijbrandij: Yes, thanks for that. We already have consumptives elements in our model. So for example, for compute and for storage, you pay on a consumption basis. We’re adding features to that consumption, for example, in GitLab 16 released on June 22nd, we released MacOS runners, we released Linux runners, we had the Oracle partnership where we have more AI runners, DPU runners. So that is a small part of our revenue today, but we’re releasing additional features. I think over time, you see that the licensing is going to become more flexible. We have cloud licensing today and that allows us to be more flexible in what you pay for. For example, the add-on we are envisioning for AI, right now, it’s efficient to something if you use it, you pay for it, otherwise not.

We’ll see what we end up releasing, but that’s what we’re thinking about. So I think you’re right that the mindset of customers is going more consumption, and we don’t — we want to be meeting the expectations there.

Jason Ader: Got you. All right. And then one quick follow-up just on that AI SKU. What is going to be included in that SKU beyond Code Suggestions?

Sid Sijbrandij: Right now, we’ve only talked about Code Suggestions being part of it.

Jason Ader: Perfect. Thank you. Good to see you looking good, Sid.

Sid Sijbrandij: Thanks, Jason. Appreciate it.

Darci Tadich: Gregg with Mizuho.

Brian Robins: We don’t see him. We can go to the next one.

Darci Tadich: Pinjalim with JPMorgan.

Pinjalim Bora: Great. Thank you for taking the questions. Sid, good to see you doing well. Sid, maybe one on MLOps. Can you help us understand where are we in the maturity curve for GitLab with respect to MLOps. Is DataOps kind of the gap at this point? I’m trying to understand with the current craze of kind of developing Gen AI application, are you seeing new or existing customers kind of talking about using GitLab as part of their MLOps workflow when they’re thinking about building this Gen AI apps? And then one follow-up. The $9 per user per month add-on is that basically an extension into visual code? Is there a difference between a SaaS user or a self-managed user?

Sid Sijbrandij: Yes. Thanks for that. So to answer the last question first, that $9 will be the same $9, whether you’re a SaaS user or a self-managed user. You’ll be able to use the Code Suggestion features in our Web IDE as well as in the usual editors like Visual Studio Code. Regarding ModelOps, we’re really, really early. So I don’t want to oversell this. It’s a vision of where we’re going to the future, of where we see the TAM expanding. Today, we have the functionality to link experiments in MLFlow to GitLab, and the next feature that will come out is a model registry. And when you have a model registry, that’s going to form the basis of new functionality we can do is then you have the model kind of control in GitLab as well, and you can start adding more functionality.

We expect that MLOps functionality to come before the DataOps functionality. The model learning looks a lot more like code in many ways than the data. So it’s kind of the logical step is first models and then data. With data, it’s — we don’t have functionality yet and that will come later. I think it’s — the thing to know is that we have the ambition. We have the ambition to go beyond code. We have the ambition to manage your code, your models and your data because we think the application of the future is going to have all three, and all three are going to be governed. All three are going to have security and compliance questions that you want your tool, your DevSecOps platform to figure out for you. And that’s why we are doing this, not because it’s easy, but because it’s super, super useful, and because every application is going to have interactions between the three, if we can bring all those constituents together, that’s going to be super valuable for our customers.

Pinjalim Bora: Very helpful. Thank you

Darci Tadich: Next is Mike with Needham.

Mike Cikos: Hey, guys. You have Mike Cikos on the line here and thank for taking the question. First one for Sid, and Sid, great to hear on the health. That’s tremendous news, and I appreciate you giving us all an update. Wanted to circle up on the AI add-on that we’ve been talking about. And I know the Code Suggestions is the only one that we’re talking to today that’s going to be part of that add-on. Can you help us think through, will GitLab be offering up AI features or certain products, however you want to phrase it, independent of that add-on? Or are you going to have to adopt that AI add-on be able to reap the benefits of the AI technology investments that you guys are making today? And then I have one follow-up for Brian.

Sid Sijbrandij: Yes, it’s a great question. Like will every AI, piece of AI functionality be in that add-on? And how does it work? Will there be additional add-ons? Will it be part of Premium or Ultimate? Those are pricing and packaging questions. We’re still looking into today so I can’t comment on that. It’s a valid question though.

Mike Cikos: Okay. And to Brian then, if I just look at Q1, obviously, the revenue was well ahead of the guidance and your expectations. Can you help us think through what was better than expected during the quarter? And similarly, what is management embedding in its guidance, if I look at the much more, I guess, modest sequential revenue growth that we’re now looking for in 2Q?

Brian Robins: Yes. Thanks for the question, Mike. I was happy with the predictability in the quarter, as I states earlier. When we talked about guidance on the last call, because we had more variability in fourth quarter, the range got higher. And so we looked at the bottom end of the range and selected that. And so if you compare us 1Q to 4Q, sales cycles remained at 4Q levels. I did discuss how the hyperscalers bookings were over 200% year-over-year. We also had the lower discounting, and I touched on the strength of Ultimate in the quarter. And so the guidance approach hasn’t changed. When we look at the history of what we’ve done and we look at the assumptions that we have in the model, we have a very detailed bottoms-up model to come up with guidance. And we use the same guidance approach given the macro conditions, and that’s how we planned.

Mike Cikos: I’ll leave it there. Thank you guys.

Brian Robins: Appreciate it.

Darci Tadich: Let’s try Gregg with Mizuho. He has reconnected.

Gregg Moskowitz: All right. Thank you very much. Glad the connection is holding. And Sid very glad to hear the encouraging news regarding your health. I’d like to follow up on ModelOps, and I know it’s really early. I do think the native registry is an interesting enhancement. And just curious to get your expectation with regard to attracting data science teams to the platform going forward as that starts to ramp? And then I have a follow-up for Brian.

Sid Sijbrandij: Yes, because it’s really early, we want them to work together hand in hand. You see that many changes need both the change in the code and a change in the models and it’s going to lead to different data being outputted. So these changes that today happen in different platform, different tool chains and sometimes very manual. We expect that it’s going to be more and more important to happen on the same level. You think about the financial industry, what you execute, what you have to prove to your auditors is going to be based on procedural code plus a model you’re running, plus that model you’re changing based on data that you need to prove like what data did you use to train the model that, that was then called from your code, that’s the questions we need to answer, that our customers need to answer, and we want to help them do that in a way that’s friction-free where it’s not up to the developers to document it each and every time but the platform just takes care of it and you only have to point out a transaction and you can immediately see how you did that.

And that’s really hard to achieve today without a platform. And that’s what we’re going for. As I said very, very early, but I hope a compelling ambition.

Gregg Moskowitz: All right. Very helpful. And then for Brian, in the Q4, you mentioned that your NRR decreased almost equally, I think, across seats, tier upgrades and price yield. Any change to that mix in the Q1?

Brian Robins: It’s been relatively the same. And so seats is about 50%. Price increase is about 25%, and the last is 25%. So there really hasn’t been any change whatsoever.

Gregg Moskowitz: All right. Perfect. Thank you.

Darci Tadich: Next is Nick with Scotiabank.

Nick Altmann: Awesome. Thanks, guys for taking the questions and Sid great to hear you’re doing well. Just a follow-up on Matt’s question on the Ultimate mix ticking up. It sounds like some of the strength there was driven from a business that was up for renewal in a smaller price point delta between Premium, Ultimate, and it also sounds like there was some strength there just on net new customers landing at Ultimate. But I’m just curious given there’s more renewal businesses as sort of we progressed through 2Q in the second half, should we expect the Ultimate mix to continue to uptick here? Thanks.

Brian Robins: Yes. Thanks for the question, Nick. As we said before, and I think it’s worth saying again, we don’t compensate the sales team to sell Ultimate versus Premium. And so that is an output and not something that we’re solving for. We want to deliver the best solution for the customer and get them a quick time to value and a positive business outcome. And so Ultimate had strength in the quarter. It’s really driven by compliance, security and all the additional product features that Ultimate has. When you go through and look at Ultimate and look at expansion, first orders and so forth, Ultimate performed well in a lot of the categories as expected. And so where we saw some pockets of weakness was really in Premium on expansion of our existing clients as well as the contraction.

Churn was relatively low, but we still saw some contraction as well. And so like I said, Ultimate had a good quarter. There was some pockets of weakness in premium, I’ll call them watch points that we continue to watch. But overall, happy with what we delivered.

Nick Altmann: Great. Thank you.

Darci Tadich: Our final question comes from Ryan with Barclays.

Ryan MacWilliams: Thanks for squeezing me in. Sid, how are enterprises evaluating adopting AI for their code development today? So like what are some of the key items that they would grade you on? And would this happen via something like an RFP process? Or would this be something that they handle internally? Thanks.

Sid Sijbrandij: Thanks. I believe it’s more organic today. They’re trying different things. I think what is really important to a lot of customers is the privacy of their code. And what they’re looking for is a provider who can guarantee that, for example, the output of the models that they ask questions to isn’t used for other models. So that’s something that’s top of mind for us as we build our features. Other than that, it also has to be kind of accessible to everyone in the company. It has to work on the most popular editors. And we have a lot of revenue from self-managed. So we want to make sure that, over time, functionality also is available to self-manage customers where they can connect to the Internet to offer that functionality.

Ryan MacWilliams: So are you seeing a lot of questions from customers around securing the output of code from large language models?

Sid Sijbrandij: I think it’s top of mind for customers is that the — with some of the third-party services today, you don’t get a guarantee that the output isn’t used to train the Code Suggestions for another organization. And that’s certainly top of mind for them.

Ryan MacWilliams: Appreciate that. And one for Brian. Do you see any pull forward of demand or early contract negotiations from customers looking to take advantage of that $24 transition price in the quarter?

Brian Robins: I’ll answer this, but this is the last one, Ryan. We got to close out and get back on the call backs. We did not allow early renewals. Your contract had to be up renewal two weeks prior to expiration. And so there was no pull forward in the quarter related to that.

Ryan MacWilliams: Okay. Thanks, guys.

Darci Tadich: That concludes our 1Q FY’24 earnings presentation. Thanks again, once more, for joining us. Have a great day.