Cloudera, Inc. (CLDR) Q1 2019 Earnings Conference Call Transcript

Cloudera, Inc. (CLDR)
Q1 2019 Earnings Conference Call
Jun. 6, 2018 5:00 p.m. ET

Contents:

• Prepared Remarks
• Questions and Answers
• Call Participants

Prepared Remarks:

Operator

Good afternoon. My name is Sheryl, and I will be your conference operator today. Welcome to the Cloudera first-quarter fiscal 2019 quarterly results conference call. [Operator instructions] Please note this conference is being recorded.

Your host is Kevin Cook, vice president, corporate development and investor relations. Kevin, you may begin your conference.

Kevin Cook -- Vice President, Corporate Development, Investor Relations

Thank you, Sheryl. Good afternoon and welcome to Cloudera's first-quarter fiscal 2019 conference call. We will be discussing the results announced in our press release issued after market close today. From Cloudera, with me are Tom Reilly, chief executive officer; Mike Olson, co-founder, chairman, and chief strategy officer; and Jim Frankola, chief financial officer.

During the course of this call, we will make forward-looking statements regarding future events and the future financial performance of the company. Generally, these statements are identified by the use of words such as expect, believe, anticipate, intend and other words that denote future events. These forward-looking statements are subject to material risks and uncertainties that could cause actual results to differ materially from those in the forward-looking statements. We caution you to consider the important risk factors that could cause actual results to differ materially from those in the forward-looking statements in the press release and this conference call.

These risk factors are described in our press release and more fully detailed under the caption, Risk Factors, in our annual report on Form 10-K and our other filings with the SEC. During this call, we will present both GAAP and non-GAAP financial measures. Non-GAAP measures exclude stock-based compensation expense and amortization of acquired intangible assets. In addition, we provide a non-GAAP weighted average share count.

These non-GAAP measures are not intended to be considered in isolation from, a substitute for, or superior to, our GAAP results, and we encourage you to consider all measures when analyzing Cloudera's performance. Additionally, our commentary today and the guidance we provide are under existing accounting standard, ASC 605. For a complete information regarding our non-GAAP financial information to most directly comparable GAAP measures and a quantitative reconciliation of those figures, please refer to today's press release regarding our first-quarter fiscal 2019 results. The press release has also been furnished to the SEC as part of a Form 8-K.

In addition, please note that the date of this conference call is June 6, 2018, and any forward-looking statements that we make today are based on assumptions that we believe to be reasonable as of this date. We undertake no obligation to update these statements as a result of new information or future events. Now, let me turn the call over to Tom Reilly.

Tom Reilly -- Chief Executive Officer

Hello, everyone. Thank you for joining us to discuss our first-quarter 2019 results. As I'll review shortly, in Q1, we executed the initial elements of our transition plan to position Cloudera to capture a larger portion of the market. I am very pleased with the performance of our team, given the significant changes we initiated in late Q4 and early Q1.

This multi-quarter transition is well under way and proceeding as expected. Let me quickly share our Q1 results and provide a more detailed update on the rationale, actions and progress concerning our plans. Total revenue for the quarter was $103 million, representing year-over-year growth of 29%. Subscription software revenue grew 33% year over year, our net expansion rate was 132%, and we generated operating cash flow of $24 million in the first quarter.

For several years, we have enabled large global organizations to collect and manage vast amounts of data in various forms at scale. Our offerings help enterprises in three ways; first, for them to grow with greater insights into their customers; second, give them the ability to connect their products and services via the Internet of Things to compete more effectively; and third, for them to protect their businesses in an environment of increasing digital risk. In many respects, we've led this market. We've continued to mature our innovative technology into a platform that is highly performing and operates in a hybrid and multi-cloud world.

As a result, today, we have hundreds of large enterprise customers managing many petabytes of structured and unstructured data on-premises and in the cloud. We're proud of these accomplishments but not satisfied. We must continue to lead this market and now align with the next market evolution, helping our customers get unprecedented insights out of data using techniques such as machine learning, artificial intelligence, self-serve analytics and stream processing. On a related note, because these technologies are all very compute-intensive and elastic in nature, they particularly benefit from public cloud infrastructure and are moving rapidly to the cloud.

Against this backdrop, we are making investments and business decisions required to capture more of the highest-value opportunities. As we discussed in our year-end call and at our Investor and Financial Analyst Day in April, we have initiated transitions and market advancements, and our understanding of customer behavior and use case adoption is refined. The rapidly connecting world is driving every enterprise in every industry to go through a digital transformation in order to rank competitive in the modern era. Data is the foundation of digital transformation.

A completely new architecture and set of technologies are required for enterprises to leverage and gain meaningful insight from data. To position Cloudera for this latest market evolution and long-term success, we are making transformational changes in primarily two forms. The first is to shift our technology and innovation focus to the needs of organizations that are striving to get insights out of data versus solely managing data. Truly gaining insights requires modern techniques, such as machine learning and AI combined with traditional analytics.

The power is in exploiting both methods. This orientation means ensuring that our offerings are effective, easy and cost-competitive in the public cloud as these compute-intensive workloads benefit from the elasticity of public cloud infrastructure. In support of this, organizationally, we have put in place three new general managers who are driving our investment plans in each of these areas. These leaders have completed the build-out of their business units and have aligned the company behind each of their objectives.

And I'll step through them. First, we aim to lead the adoption of machine learning and AI at our largest customers. With the recent announcement of Cloudera Data Science Workbench 1.4, we now have more than 150 large enterprises that have adopted Cloudera as their development environment for machine learning and AI applications. This past quarter, more than 20 large enterprises selected Cloudera as their ML/AI development platform.

Let me explain why winning these decisions are so important for our long-term success. The ML/AI market is early in its formation. There are no clear market definitions, industry standards or best practices. In addition to the direct sale of our machine learning offerings because of the data-intensive and compute-intensive nature of machine learning AI, these workloads will naturally drive substantial future expansions on our underlying platform.

As data scientists become more productive, our economics, of course, finally improve, thus our objective to lead our customers with product innovation, research, and advisory work. In the second area, we're also making great strides in our objective to disrupt the analytics market. Although analytics encompasses some of the more traditional workloads, our engineers have been busy innovating to deliver data warehouse capabilities at dramatically lower cost and better performance, including our newly announced cloud-based data warehouse, Cloudera Altus Analytic DB. With more than 20 new analytics customers gained in Q1, our analytic offerings now are being used at more than 850 accounts.

And third, we are capitalizing on cloud adoption by large enterprises. We have substantially expanded Cloudera Altus, our family of Platform-as-a-Service offerings. In addition to the recent announcement of Altus Analytic DB, we have made generally available Altus Data Engineering on Microsoft Azure. We have also released the beta of Altus Shared Data Experience, or SDX, providing unified data context for workloads running in the cloud.

Approximately 26% of our customer base is now using our public cloud offerings, many in hybrid and multi-cloud deployments. In Q1, the number of customers running Cloudera in the cloud increased 55% year over year. Our innovation in these three high-growth areas is being recognized by customers, and I cannot be happier with the leadership being demonstrated by our general managers in the alignment of our organization behind them. We are executing on a multi-quarter transition.

It will take time for the bookings from these initiatives to become evident, but every day, we are making progress. Shifting to the topic of bookings and selling. The second area of transformation is in our go-to-market strategy. Here, we are very hyper-focused on landing those enterprises with the greatest propensity to adopt multiple use cases and expand consumption of our software over time.

Also, in support of our technology and innovation focus, we are increasing our selling and marketing efforts to line of business executives. This is where decisions are being made for machine learning use cases, analytics and cloud adoption. Three areas I'll update here. First, we have instituted selling discipline by refining our target market to the top 5,000 global enterprises with the greatest propensity to buy and expand.

Every one of our field sellers is now assigned to specific named accounts in this refined target market. We're also in the early stages of developing a new channel to cost-efficiently address customers outside our current target market. Secondly, I've introduced two new selling roles as part of our field reorganization; the first role, dedicated new logo hunters targets the highest quality prospective customers; the second role, dedicated customer account managers drive expansions at our largest customers. We have completed the necessary field assignments, put management and teams in place, trained our specialists, and these teams are all diligently working with their prospects and customers.

We are seeing the initial results from this focus in the field. We are pleased that in Q1, we increased the total number of customers that spend more than $100,000 in software ARR with us by 38. During our Analyst Day, we described the Cloudera customer journey. As customers add use cases, utilization increases and our revenue grows, yielding a healthy business model and compelling customer unit economics.

$100,000 of ARR is a meaningful breakpoint, the first important milestone on a successful expansion journey. When customers exceed $100,000 in ARR, they have typically progressed on their data journeys and churn at a substantially lower rate than other customers. We now have more than 500 customers in this greater than $100,000 ARR category, and more than 60 of them have graduated to spending in excess of $1 million in ARR. Finally, we announced in Q1 a transition in our field leadership and an important hire in our VP of public sector.

I am pleased to report that we're off to a good start in improving our public sector performance. With respect to a new head of sales, we are actively engaged in recruitment of a top flight executive for the role and expect to complete the search in the next few months. Reflecting for a moment on the status of these transformational initiatives in product and go-to-market, the effort has gone as anticipated. We knew that a transition of field leadership, sharpening our focus on the right customers and prospects and refining go-to-market strategy would be hard, but that is what is expected of us as a leadership team.

We initiated these changes to enhance the company's posture for sustained long-term growth. We firmly believe that to be long-run competitive in the high-growth areas of machine learning, analytics, and cloud, we have to reprioritize our engineering efforts and align with the line of business decision-makers. We are deliberately making these investments with foresight, discipline, and vigor, and these changes will benefit our customers in their pursuit of digital transformation. Speaking of customers, let me share some customer use cases that reflect the power and promise of the work that we're doing.

Pizza Hut is a perfect example of a large enterprise undergoing a digital transformation by leveraging Cloudera's capabilities in machine learning, analytics and public cloud. Pizza Hut is streaming massive amounts of real-time data from over 5,000 stores and from people ordering on mobile apps or online. Before I explain how we do this, I have to say that no one out-pizzas The Hut. Pizza Hut is using our machine learning and analytics platform, running on AWS infrastructure to significantly improve pizza delivery time predictions and monitor sales, trends in real-time.

During the recent Super Bowl, Pizza Hut established a real-time operations center for their executive leadership to monitor and oversee one of the busiest selling days of the year. You can imagine the importance of elasticity on this day. All this work was powered by data collected, managed, and analyzed by Cloudera running in the cloud. And now, the world's second largest utility is another example of a large enterprise undergoing a digital transformation by leveraging machine learning, analytics, and public cloud infrastructure.

Enel has built a global smart metering system using Cloudera's platform. This IoT system analyzes customer demand data from nearly 60 million smart meters in real-time and runs machine learning model to predict equipment failure and to reduce operating cost. Enel is also taking advantage of our cloud-native architecture to improve agility to respond to ever-changing data volume and business needs. It should be noted that Enel is an all-AWS shop.

They run almost their entire system, including our cloud solution, on AWS infrastructure. This is a powerful example of our SDX technology enabling our customers the ability to take advantage of public cloud infrastructure without having to sacrifice enterprise-grade performance, metadata management, governance for security, especially in the world of GDPR. As we discussed in the past, the banking industry is going through rapid digital transformation as more transactions and relationships become digital. Barclays Retail Bank is a longtime customer that we have helped to improve visibility into customer activity and to detect fraud.

Today, using our platform, Barclays is transitioning from batch processing of separate data sources to processing real-time data feeds. The result, even faster fraud detection and a new use case that delivers customer offer recommendations in real-time using machine learning. Separately, Barclays Corporate is innovating in risk management, employing advanced, computational, real-world risk simulations using our platform along with that of our partner, Simudyne's technology. Our joint solution significantly improves risk management of regulatory compliance for global financial institutions.

As these customers indicate -- digital customer stories indicate, digital transformation is under way at many of the largest enterprises in the world. These transformations rely on a modern architecture in a world driven by machine learning, artificial intelligence, and analytics at scale. The rapid emergence of technology to fulfill digital transformation and the patterns of adoption of large enterprises require agility and innovation. We are thinking long term, and we are making the necessary investments to be one of the winners in this highly lucrative market.

I would now like to have Jim provide the financial results, and then we'll return to take your questions. Jim?

Jim Frankola -- Chief Financial Officer

Hello, everyone. Our Q1 results reflect the anticipated impact of Cloudera's go-to-market changes. We expected to take a few quarters to execute the transition that Tom described and for bookings to benefit from these initiatives. Consistent with the outlook that we've shared, our net expansion rate was a bit lower in Q1.

It is noteworthy that we exhibited strong financial controls and achieved positive operating cash flow for the quarter. Subscription software revenue was $86 million, an increase of 33% year over year. This represented 84% of revenue, up from 81% in Q1 of fiscal '18. In total, revenue was $103 million for the first quarter, representing 29% growth over the year-ago period.

For Q1, our net expansion rate was 132%. Recall that net expansion rate factors retention, expansion and churn on a dollar basis. Based on feedback that we received from our Investor and Financial Analyst Day, we plan to share more about our land-and-expand business model and the measures that matter most in analyzing and managing our business. As Tom highlighted, we now have 539 customers with annual recurring software revenue in excess of $100,000.

This measure best reflects our ability to both acquire target customers and advance customers along a journey toward attractive unit economics. For those interested in the progression of this measure over time, we have posted account by quarter in the investor materials on our website. As I review the remainder of the income statement, note that unless otherwise stated, all references to expenses and operating results are on a non-GAAP basis. Historical non-GAAP results are reconciled to GAAP results in the press release issued earlier today.

In Q1, subscription gross margin was 85%, up from 84% a year ago. Services gross margin for the quarter was 10% versus 11% a year ago. Total gross margin for Q1 was 73% compared to 70% last year. Turning to operating expenses, sales and marketing expense was $54 million for the first quarter or 52% of revenue.

This compares to 62% of revenue in the year-ago period. Research and development was $34 million for the first quarter or 33% of revenue, down from 35% a year ago. G&A was $12 million for the first quarter or 12% of revenue. This was up from 11% of revenue last year due to increased cost associated with operating as a public company.

Overall, operating loss was $24 million in Q1, representing a negative operating margin of 24%. This was an improvement of more than 14 percentage points compared to the year-ago period. Loss per share was $0.17 in the first quarter based on 147 million weighted average shares outstanding compared to a loss per share of $0.27 in the first quarter of fiscal '18. Please review the financial statement tables in today's press release for additional information regarding historical and forward-looking stock-based compensation expense and shares outstanding.

Now turning to the balance sheet and cash flow. We exited Q1 with $487 million in cash, cash equivalents, marketable securities and restricted cash, which is up from $26 million from the end of fiscal 2018. Operating cash flow for the first quarter was positive $24 million, driven by strong collections and continued improvement in operating efficiencies. This compares to positive operating cash flow of $5 million in the year-ago period.

This progress reflects the dynamics of the Cloudera business model with high customer acquisition cost, offset by much higher customer lifetime value. Capital expenditures were $4 million in the quarter. Total deferred revenue was $279 million at the end of the first quarter, up 31% year over year. Short-term deferred revenue was $247 million, up 32% year over year.

I will conclude with guidance. Our guidance for fiscal year 2019 is unchanged. Initial guidance for fiscal Q2 is as follows; We expect Q2 total revenue to be between $107 million and $108 million, representing approximately 20% growth over Q2 of last year, with subscription software revenue in the range of $90 million to $91 million, up approximately 22% year over year. Loss per share is projected to be $0.15 to $0.13 based on approximately 150 million weighted average shares outstanding.

With that, I'll turn it back to Tom.

Tom Reilly -- Chief Executive Officer

Thank you, Jim. At Cloudera, we believe in digital transformation and aim to empower for large enterprises. Data, machine learning, artificial intelligence, analytics, and cloud all play important roles in this transformation. And our solutions are elemental to these high-growth areas.

Our customer base requires an enterprise-grade platform that operates at scale, whether on-premises, in hybrid deployments or, increasingly, in multi-cloud environments. Equally important to our commercial success is enforcing discipline and selling to our refined target market and engaging line of business buyers as drivers of digital transformation in their businesses. Simply, these efforts are designed to lower our customer acquisition cost and increase our net expansion rates. I am proud of all the Cloudera's accomplishment in a short amount of time, and I am impressed by the energy displayed by our team as we embrace change to make Cloudera a stronger company.

The digital transformation is happening. We are intent on making the right investments to capture our share of this market for the benefit of all Cloudera stakeholders. The team and I remain grateful to our customers, our employees, our developer community, our partners and, of course, to our investors. Thank you all.

As a reminder, we are joined by Mike Olson, our co-founder and chief strategy officer for Q&A. Operator, let's begin the Q&A portion of the call. Thank you.

Questions and Answers:

Operator

[Operator instructions] Our first question comes in the line of Kash Rangan of Bank of America Merrill Lynch. Please go ahead, your line is open.

Jacqueline Cheong -- Bank of America Merrill Lynch -- Analyst

Hi. This is actually Jacqueline on for Kash. Thanks for taking my question. My first question is, Oracle recently acquired DataScience.

Does that make them more competitive against Cloudera?

Mike Olson -- Co-founder, Chairman, and Chief Strategy Officer

Hi. This is Mike speaking, and I'll take that question. No, we don't think so. Oracle made a good buy with the DataScience.com team.

I think Oracle is fleshing out its broad business software portfolio and applying data science techniques to traditional relational data with this addition. But look, our modern platform for machine learning and analytics optimized for the cloud is designed for much larger scale and much broader range of use cases than a traditional database. As you may know, Oracle is a good partner, delivering the Cloudera platform as part of its Oracle big data appliance, and that may help drive some additional appliance consumption.

Tom Reilly -- Chief Executive Officer

Yes Ashley. This is Tom just jumping to Mike's comment. We're excited to see that. It reflects what we knew all along that machine learning is going to be a high-growth area that is now validated by Oracle.

Their offering will be very good in Oracle environments. We intend to work in both hybrid, multi-public cloud environments in kind of broader underlying capabilities.

Jacqueline Cheong -- Bank of America Merrill Lynch -- Analyst

Got it. Thank you so much. And how are you doing in terms of win rates against competitors? And how is the win rate trending?

Tom Reilly -- Chief Executive Officer

Yes. So, Ashley, our win rates with our traditional competitors. Let's -- let me capture that as all the legacy guys with legacy data warehouses or MPPs as well as the on-premise competitors that we traditionally compete with. Our win rates are very strong and improving.

We are increasingly seeing more wins against the traditional players. We are competing more often, and increasingly, in the cloud against the public cloud house offerings. Here, our win rates are not as strong but we're improving them. And our focus with the new general manager of machine learning, our investments -- I'm sorry, general manager in cloud, our investments in Altus, our specialists in the field gives us great confidence that we will compete effectively in the cloud as well.

Jacqueline Cheong -- Bank of America Merrill Lynch -- Analyst

All right. Thank you so much.

Operator

Your next question comes from the line of Sanjit Singh of Morgan Stanley. Your line is open.

Sanjit Singh -- Morgan Stanley -- Analyst

Thank you for taking my questions and nice to see that the transition plan has gone off to a solid start. I had two questions, maybe one for Jim and then maybe a follow-up for you, Tom. Jim, on the quarter, I think the net expansion rate came in fully -- pretty solidly above where we were expecting. So I wanted to get your view on how do we -- how should we expect the net expansion rate to trend over the balance of the year? Is that sort of in line with the guidance that you laid out last quarter? And from a bookings perspective, any sort of commentary you can provide for Q1? How does bookings metrics -- booking performance came through this quarter?

Jim Frankola -- Chief Financial Officer

Yes. Well, I'll touch on bookings first. We don't comment on bookings within the quarter. We'll talk about the overall health business.

With respect to the net expansion rate, there really isn't much new news from what we disclosed 60 days ago on our conference call or even seven weeks ago in our Analyst Day. So when you look at the revenue projection for the year, we are guiding to 24% software revenue growth. In a typical year, you might get 6 points of growth, give or take, from new customers who've joined over the course of the year. That means that we'll get about 18%, 19% revenue growth associated with existing customers for the year.

So that would imply a net expansion rate at the low end of our historical range of 120% to 150%. So that guidance of 120% to 150% is the long-term net expansion rate that we expect for the business. And this year, given the transitions that are going on with the field, we expect to be operating at the low end of that range.

Sanjit Singh -- Morgan Stanley -- Analyst

Very helpful. And then maybe, Tom, for you, What I hear the message coming out of your introductory comments is that the company is really focused on the AI and machine learning use cases as well as streaming analytics, which to me means cloud. And so as we go through the transition, are you thinking about from incentives or pulling in new levers of the business to really accelerate that adoption in the cloud? You're at sort of 26% today. Is now the right time to really be aggressive in terms of trying to get your customers over to the cloud as fast as possible given that you're going through this multi-quarter transition anyway?

Tom Reilly -- Chief Executive Officer

The simple answer is yes, Sanjit. That is why we put in place three new general managers to bring focus and emphasis in these high-growth areas, machine learning and the analytics and the cloud. Our strategy in cloud is to capitalize on our customers' desire to take advantage of public cloud. That did not exist just two years ago, three years ago in our target market, large enterprises.

We now see that happening and we're -- I think our timing is very strong to help large enterprises capitalize in their desire to take advantage of public cloud. So that's why we put in place our general managers. We put in place dedicated specialists around understanding the public cloud environment, and we have been investing in Altus, our Platform-as-a-Service, which is basically our competitive strategic weapon in the public cloud.

Mike Olson -- Co-founder, Chairman, and Chief Strategy Officer

Hey, Sanjit, it's Mike. I want to just pile on very briefly. While it's absolutely true that machine learning and AI in the public cloud is a big deal, it's been a big deal for our existing customers on-premises for some time. We believe that our hybrid and multi-cloud strategy allows large enterprises to roll these capabilities out where they want it.

I'll point out machine learning is, in general for large enterprises, a new technique. We're investing in Data Science Workbench and the Fast Forward Labs team because we believe that winning the hearts and minds of our large enterprise, data science and analyst clients early is critical. That will set us up to win substantial expansions as they roll those workloads out on-premises, in the cloud or move them around among those places over the long term.

Sanjit Singh -- Morgan Stanley -- Analyst

I appreciate that, Mike. And maybe one really quick follow-up as it relates to the data warehousing use case, which has been, I think, a story of strength for the company. In terms of where that that parts of the internet take place, do you see data warehousing use cases moving to the cloud as well?

Mike Olson -- Co-founder, Chairman, and Chief Strategy Officer

Oh, we absolutely do. We see -- especially as customers consider some of the end-of-life products they might have invested in, in the past, say, Netezza, they think about replatforming those systems. They're looking not merely for a new platform to run them on, but they're thinking about doing away with the hardware investment altogether and moving those to the cloud. So we view that one in particular as a big opportunity for us.

Sanjit Singh -- Morgan Stanley -- Analyst

Great. Thank you very much.

Operator

Your next question comes from the line of Mark Murphy of JPMorgan. Your line is open

Mark Murphy -- J.P.Morgan -- Analyst

Yes. Thank you very much for taking my questions. So Jim, I'm -- I guess I'm trying to compare back and forth between the net expansion rate of 132% in the quarter and then the subscription software growth rate is 33% -- or just under 33%. Is that -- so that 1% spread, is that essentially the -- that's all of the new logo business that you had pulled in, in the last 12 months?

Jim Frankola -- Chief Financial Officer

There is one account that we exclude from net expansion rate, and that's Intel. They're a related party. And in our Qs and Ks, you can see how much revenue they have. So in any given quarter, there might be a 1% or 2% noise due to the Intel growth dynamics being different than everyone else.

The second thing is the 132% net expansion rate is an arithmetic average of the last four quarters' net expansion rates. So you'll have some math that may yield slight difference. But yes, the -- what you have going on there is the net expansion rate plus some revenue from new plus some of the dynamics on Intel and a four-quarter simple average versus a complex average.

Mark Murphy -- J.P.Morgan -- Analyst

OK. And then I think what you had said in a prior answer, I think you said in a typical year, you might get 6 points of growth from the new accounts, right? So -- and I realize it's a little bit of a swing factor. But essentially -- I mean, are you essentially trying to get -- so with that -- the 1%-ish -- or I wanted to call it 1% or 2%-ish kind of contribution that is coming in from all the sales and marketing investments that go into new logos and what you get out of them. How that converts to revenue in the first year? You want to take that 1% to 2% back up to about 6%.

Do you think that, that is in the cards, say, many quarters down the road if the -- if your transition is successful? Can you get -- do you think it gets back to that kind of a spread?

Jim Frankola -- Chief Financial Officer

Yes. So I mean, if you look at the number of new logos that we add and average values of new logos, you'll find that in any given period, we will have $20 million, $25 million of revenue associated with accounts that we picked up after the -- over the past year. So that's where that 5%, 6% of total mix comes from. I don't see that number radically changing over time.

It's the nature of the land-and-expand model. Customers start small. What we're looking to do is focus on the customer expansion pattern that we talked about, the Cloudera journey, where we are investing in moving customers from that landing at less than $100,000 into the phases above that, going from a single use case to multiple use cases to being an enterprise platform. And that focus is on the net expansion rate.

So to come back, I do think that we will see 5% or 6% of our revenue this year from new, and that won't be much of a change from historical patterns.

Mark Murphy -- J.P.Morgan -- Analyst

OK. And as well, did you disclose the Global 8000 customer account exiting Q1?

Jim Frankola -- Chief Financial Officer

No. So consistent with what we discussed at Analyst Day, the Global 8000, we tightened up our target market. So we -- using our own data and our knowledge of who is the most likely to both buy and expand, we have an internal list of targeted customers that is much tighter than that now. It's about 5,000 enterprises, give or take.

And that list changes dynamically as we update our model. So from a focus standpoint, what we're going to focus on is the number of accounts over $100,000. We actually got some feedback in Analyst Day that, that was a pretty good metric to show the customers that are really sticky. So Tom alluded to it, but to recap, our churn rate for customers under $100,000 is roughly 20%.

The churn rate for customers over $100,000 is in the mid-single digits. So it's an important milestone in our customer journey, and we're focusing our customer count on those customers over $100,000.

Mark Murphy -- J.P.Morgan -- Analyst

Yes, OK. So in terms of customer count, how are you handling customer counts from this point going forward? Are you going to tell us -- are you going to give us a number once a year rather than quarterly? Or is it -- are we not going to get a customer count?

Jim Frankola -- Chief Financial Officer

We will give you the number of customers that are north of $100,000 that if you look at the slide we shared in Analyst Day, those customers represent 92% of our revenue. So if you're trying to understand the meat of our business, it's the over $100,000 customer count. That's what we're going to share quarterly. And then yes, annually, we will provide supplemental metrics on total customer count, G2K, whatever other supplemental metrics we think will add transparency to our business model and allow you to better understand that journey.

Just like less than seven weeks ago, we actually broke the customer count up into five different segments to try to show you how customers moved through their expansion phases.

Mark Murphy -- J.P.Morgan -- Analyst

OK, got it, understood. And the last thing, Mike, I was wondering if you could just update us on percentage of Cloudera workloads on public cloud, I think last summer, you gave us a number of 20%. I think that included Altus. I guess, I'm just wondering, do you know the number that are -- would be specifically the public cloud percentage? Because I think we're trying to triangulate on just what your market share would be on-prem versus in the public cloud. Thank you.

Tom Reilly -- Chief Executive Officer

Hey, Mark, this is Tom. Let me jump on that. I'm closer to the numbers on what's happened there. So 26% of our customers are taking advantage of public cloud.

That is up 55% over the same period a year ago. That's a mix of both using the public cloud as Infrastructure-as-a-Service and our recent release of Altus Platform-as-a-Service, although we don't break that out. And these figures that we report are basically us monitoring our software and the use of our software in the cloud. So it gives us visibility into -- as our customers move workloads to the public cloud, we can see that visibility happening.

Mike Olson -- Co-founder, Chairman, and Chief Strategy Officer

Only thing that I would add, Mark, is that with the beta release of Altus Analytic Database on Azure joining the beta release on Microsoft -- on Amazon Web Services, rather, alongside the Altus Data Engineering offering GA on those platforms now as well, we've got a richer suite of capabilities. And we think, too, they play better together than either have played solo. So we think the combination of those past offerings is promising and ought to help us win more share there as well.

Mark Murphy -- J.P.Morgan -- Analyst

Thank you.

Operator

Your next question comes from the line of Karl Keirstead of Deutsche Bank. Your line is open.

Karl Keirstead -- Deutsche Bank -- Analyst

OK. Great. I've got a couple. Maybe to start with Jim.

Jim, you mentioned you intended to reaffirm all of your fiscal '19 guidance metrics. I didn't explicitly hear you reaffirm your total DR and short-term DR growth. Maybe you did, and I missed it. But do you mind just reaffirming that, that plus 21% on total DR and plus 23% on short-term DR are still your targets?

Jim Frankola -- Chief Financial Officer

Yes. So we don't guide to billings or, in effect, to deferred revenue. What I did last quarter was there were so much movement in the numbers, the overall numbers. I wanted to make sure that as everyone build out their models, they had a better idea of what deferred revenue was doing, and for those that's focused on billings could help them with that one.

We're in the middle of this transition. So we don't guide to billings, and I'm not going to reaffirm one way or the other the numbers that we shared last quarter on deferred revenue.

Karl Keirstead -- Deutsche Bank -- Analyst

Yes, OK, great. And then maybe Tom, you mentioned that you'd like to improve your win rates against the house offerings of the cloud vendors. I presume you're referring to services like EMR and Redshift on AWS and maybe BigQuery on GCP. I'm just wondering if you could summarize -- maybe this is partly a question for Mike as well.

What are the functionality gaps against that those house offerings have that Cloudera can sort of take advantage of and move up the stack and improve your win rates over time?

Tom Reilly -- Chief Executive Officer

Yes, this is where I get really excited. So -- and I'll use the -- I had a customer sales call with a large financial bank yesterday, the CIO and the whole executive team in Silicon Valley for their tech roadshow, and we meet with them every year. Last year and the years before, we weren't discussing cloud. It's all they want to discuss this time, and our pitch went as follows; right, do you want to take advantage of your data center and take advantage of public cloud with a hybrid offering? Yes, that's required.

Do you need all those enterprise features that you have available today in your data center to be the same in the public cloud? Yes. Do you know which public cloud you want to use? No. Do you want a multi-cloud offering? Yes. Do you want that portability? Are you shifting against the private cloud in the future? Yes.

I go, OK. You have a few choices when it comes down to that. Amazon is not going to offer you on-premise capabilities nor are they going to run on GCP or Azure. The same thing for Google.

And Microsoft, while they might offer you on-prem, are not going to give you the capabilities on Azure and GCP. And many of these players are lacking the enterprise security features. When we are competing in the cloud, we have so many advantages. Our No.

1 disadvantage is awareness of our capabilities, and that's what we're ramping up with our general manager machine learning, our marketing team to create awareness. And we think we'll compete very effectively.

Karl Keirstead -- Deutsche Bank -- Analyst

Got it, OK. That's helpful, Tom. Thanks. And if I could finish with a little bit of a technology question for Mike.

Mike, when I go to Cloudera's website, I don't even see the word Hadoop anymore. When I listen to this earnings call, I don't hear it anymore. And I'm just wondering whether the pivot that Tom laid out at the beginning of the call, where you're moving to more ML-centric workloads and cloud, I'm just wondering, does that sort of motivate you to pivot even faster away from that -- those core Hadoop elements to either more Cloudera proprietary IP or perhaps a different open-source software, whether these shifts Tom talked about are almost forcing an accelerated shift away from those Hadoop roots?

Mike Olson -- Co-founder, Chairman, and Chief Strategy Officer

No. Thank you, Karl. It's a perceptive question, and it's one that we've been talking with folks about a bunch. Look, we are in no way ashamed of Hadoop.

It is still a core, foundational element of our platform. But those projects, HDFS, [Inaudible] MapReduce a scale-out processing engine, they were all we had 10 years ago when we started the company. Today, we've got a rich suite of analytic engines, Impala for distributed query and Spark for stream processing and model training and so on. We've got a rich collection of storage technologies, not just HDFS but on Amazon S3 native storage, on Microsoft ADLS native storage, even IoT native storage for workloads that demand that, and the Apache Kudu project.

So it's just a much more interesting platform than before. What all of that technology shares is the use of the same design and architectural principles that Hadoop pioneered, scale-out, distributed, shared-nothing architecture. You can MapReduce your data. You can Impala your data.

You can Spark your data. It's the same data. You can get our governance, security compliance via our Shared Data Experience, SDX, across all those workloads so you know who ran queries, who trained models, who prepped in [Inaudible]. It's just a way more interesting platform.

Hadoop is important, but it's not where the action is these days, still in the platform but there's a lot more interest in these newer capabilities.

Karl Keirstead -- Deutsche Bank -- Analyst

Great insights. Thank you all very much. That's helpful.

Operator

Your next question comes from the line of Michael Turits of Raymond James. Your line is open

Michael Turits -- Raymond James -- Analyst

Hi. I'd like to -- thanks guys for taking my question. I'd like to come back to the question of the fact that in terms of your win rates are a little bit lower in the cloud, maybe you specify -- but just make it clear. Was that in general, or was that on analytics and data warehousing, or was it more on the engineering side where you might be competing Elastic MapReduce, for example?

Tom Reilly -- Chief Executive Officer

The comment is more in general. We track every compete, and then we evaluate win-loss, no-decision type things. So we're tracking our cloud win rates relative to our on-prem traditional win rates. And so that's an all-in, Michael, not one area or the other.

Mike Olson -- Co-founder, Chairman, and Chief Strategy Officer

Michael, this is Michael. And I want to add just one point.

Michael Turits -- Raymond James -- Analyst

Hey, Michael.

Mike Olson -- Co-founder, Chairman, and Chief Strategy Officer

When we win a workload on Azure, when we win a workload on Google or Amazon, the cloud vendor wins as well. So the compute that we drive, the storage that we drive as part of those workloads wind up paying substantial revenue to those vendors. And the strength of our partnerships at Amazon, at Microsoft, for example, is because we were able to help them drive consumption of their cloud platform. So yeah, we compete on a point basis, say, with EMR and Amazon, but we're not going after just the EMR workloads.

We're going after customers who have a broad range of processing and analytic requirements. And one, governance, security and regulatory compliance across all of them who need the ability to move those workloads from their data centers into the cloud and then back again later for whatever business reasons that might make a difference. We want to compete more often in the cloud. We want to see many more deals in the cloud.

We want to win our fair share.

Michael Turits -- Raymond James -- Analyst

And then I guess, I've got, Mike -- maybe for Mike and for Tom. Tom, mentioned -- I have to go back and check the number, but is there a certain number of customers that made Cloudera their AI machine learning platform. Does that mean that those are the number of customers that are actually taking Data Science Workbench? And if not, maybe you could talk about that attach rate or -- and/or how you're actually describing or defining that platform that they're standardizing on.

Tom Reilly -- Chief Executive Officer

Yes. So when we talk about standardizing on our platform -- so let me understand the building blocks of this. You need data, and so data lakes, where data is at. Apache Spark is the de facto kind of compute engine for machine learning and model training.

We're the first to commercialize Apache Spark. We're not capturing all just those customers doing that. When we talk about our machine learning and AI development environment, we're talking about Cloudera Data Science Workbench and Fast Forward Labs. And what Cloudera Data Science Workbench is it's a way to make data scientists more productive by giving them secure, well-governed access to the data lake, bigger data sets than they ever had before, to automate what they do as the data scientists from exploration to moving into production.

And then we have Fast Forward Labs, which is our research that we make available to them to also make them more productive.

Mike Olson -- Co-founder, Chairman, and Chief Strategy Officer

I'd add just one additional point to that, Michael. We have had customers doing machine learning for many years. So big banks and insurance companies were doing fraud detection and anti-money-laundering workloads on our platform by training up machine learning models way back in the day with MapReduce and Spark. We've made it much easier to do that with our acquisition of Sense.io and the release of our Data Science Workbench platform that's intended to accelerate our ability to onboard those workloads.

Two quarters back when we announced the Fast Forward Labs acquisition, I pointed out that is intended to let us be much more advisory, consultative and supportive of our enterprise clients who want to use our platform for machine learning. Machine learning drives platform consumption. Getting those early workloads, getting folks using our development environment gives us a solid platform for growing our revenue on the platform as well as on the [Inaudible].

Michael Turits -- Raymond James -- Analyst

OK. Thanks, guys.

Operator

Your next question comes from the line of Tyler Radke of Citi. Please go ahead, your line is open.

Tyler Radke -- Citi -- Analyst

Hey. Thanks for taking my questions. I like the Pizza Hut example. A question for Tom.

So you mentioned in your prepared remarks that you're -- as you're looking to kind of hiring a cloud sales leader and, hopefully, to improve the win rates, you're looking to the messaging of having Cloudera be cost-effective and easy to use. I'm just curious. If you were to click your finger on maybe the reason why win rates are down, how much of it kind of comes down to cost? And when you say cost effective, is this essentially you have to match EMR or Microsoft on kind of their like-for-like Hadoop service or -- I just wanted to interpret what that comment was referring to and how you're thinking about the drivers of cloud win rates.

Tom Reilly -- Chief Executive Officer

All right. Thank you, Tyler. Thanks for calling out Pizza Hut. I was challenged to use their tag line, "No one out-pizzas the Hut." And I'm sure Pizza Hut is very happy with that.

So first off, we're not hiring a new cloud sales leader. We put in place a new cloud general manager who drives our product road map, who understands the competition, who's responsible for enabling our field and builds out a business case of where and how we're going to move workloads to the public cloud. But we are putting in place cloud field specialists who understand the nuances of the dynamic nature of cloud infrastructure. So one of the things -- when we compete in the cloud, we're not losing on price.

We are very price competitive in the cloud. And quite frankly, we have a premium that we're able to get in the cloud because of our SDX capabilities. And the data cloud guys do not have any of the SDX capabilities, which means the data, the security, the metadata management, the governance and the multi-cloud capability. And for that slight premium to have no cloud lock-in is an easy sell for us.

When I talk about competitive win rates, our competitive win rates against the traditional guys are going up. We have no trouble competing with the traditional data warehouses, data marts, MPPs like Greenplum or Netezza or the Vertica. We have no problem competing with our traditional competitors called MapR, Hortonworks, the Hortonworks plus IBM partnership, all of that, we're very, very -- we're doing extremely well. Our cloud win rates are not as high as those traditional win rates, but we think our focus and our capabilities to take it directly to the cloud guys and to take our customers to the cloud quickly, we'll see our win rates improve there.

So I didn't mean to say that they were declining. I just wanted to share that they weren't -- our win rates there are not as high as the traditional guys because we haven't been competing there as long.

Mike Olson -- Co-founder, Chairman, and Chief Strategy Officer

Tyler, it's Mike. I want to make just one last point, and that is that notwithstanding some on the margin point competitive -- point-to-point competitive dynamic with all of the cloud vendors, we've got excellent partnerships with all of them as well. When we bring these enterprise workloads that need data governance compliance, regulatory support and so on into the cloud, we deliver those capabilities and allow them to consume more cloud storage, more cloud compute. So we've got excellent relationships with the leadership at the Big 3 cloud vendors, and it's driven by our ability to bring these enterprise workloads onto those clouds that need governance compliance, regulatory support and so on and then drive much more storage, compute, consumption and other service consumption in the cloud than they would have taken without Cloudera.

Tyler Radke -- Citi -- Analyst

Great. Thanks. And a follow-up for Jim. I think last quarter, you talked about bookings performance returning -- reaccelerating maybe in Q3 or Q4 of fiscal '19.

Just curious if you're still expecting that.

Jim Frankola -- Chief Financial Officer

Yes. So as Tom discussed, we're right in the middle of the transitions that we're making across the field in the business. So we are looking forward to getting beyond the period of peak disruption, which I think is right about now, and expect that in the back half of the year, we will start seeing the performance return back to historical levels.

Tyler Radke -- Citi -- Analyst

Thank you.

Operator

Your next question comes from the line of Chad Bennett of Craig-Hallum. Please go ahead, your line is open.

Chad Bennett -- Craig-Hallum Capital Group -- Analyst

Great. Thanks for taking my questions. So maybe I'll try this at a different angle on kind of the whole ML and AI and analytics push and up the stack push you guys are -- you're going after. So at your investor event, you talked about the Data Science Workbench, I think, being the fastest growing or fastest adoptive product first 12 months out of the gate that you've ever seen.

So if I lump Data Science Workbench in ML and AI and in Fast Forward Labs all in one bucket, and we look at your customer base the way you split up. So you have over 500 customers at $100,000 or better in ARR, and then 60 customers -- north of 60 customers, over $1 million, what is the penetration rate in those buckets for your Data Science Workbench, Fast Forward Labs, AI/ML capabilities today?

Tom Reilly -- Chief Executive Officer

So Chad, I don't have that exact analysis in front of me, what's the actual penetration rate. However, what we're seeing is our largest customers wanting to adopt us for their ML/AI platform. And what's exciting about that is when we sell CDSW, we're basically bringing data scientists to large data sets. And that allows them to start exploring and developing new use cases.

There's a lagging effect when those new use cases go into production. The exciting part is as we get those data scientists more productive, that's going to drive the underlying growth and reveal itself in the expansion side of our business. And this is the investment we're making, getting out to line of businesses, talking to the data scientists, bringing them to the data lakes or the data hubs that we've been building in our customers for the last few years, getting them productive and then seeing that expansion rate. And we didn't want to give up that real estate in the line of business to other tools or technologies.

So it's why we're making that emphasis right now.

Mike Olson -- Co-founder, Chairman, and Chief Strategy Officer

Chad, the only point I would add is that when you get to $1 million plus with Cloudera, you're not a data science or an analytic database customer. You're a data science and an analytic database customer. In general, our large customers are using all of the capabilities of our platform and a rich collection of different workloads. By and large, big means very broad as well.

Chad Bennett -- Craig-Hallum Capital Group -- Analyst

Got it, OK. That makes sense. And then from -- Tom, maybe from a go-to-market standpoint again, as you're focused more up to stack and toward AI and ML workloads and applications, maybe you've touched on this in the past, but does the type of seller you need in the field have to change relative to how you sold before? And if so, kind of how are we addressing that? Or where are we in addressing that? Thanks.

Tom Reilly -- Chief Executive Officer

Yes, Chad. So this is all part of transition. It's not the type of seller. It's not so much individuals.

It's the type of selling does need to change, and we work with our field and changing the selling. So if you followed us, we're in Phase 3 of our journey. The first phase, we were selling into IT. And when we've transitioned to Phase 2, we had elevated to selling to the CIO.

We trained our sales force on a higher value proposition to get to the CIO and seller data. We made that transition. Now, we need to help our field sell to line of business executives much more around business solutions and how machine learning and AI can deliver these transformative applications. We're training them, and we've identified the solutions.

We had the value propositions, and we've got the training materials. We've got specialists. We've invested in industry subject matter experts, and we're helping our sales force make that transition. And as we bring on new sellers, we look for more application solution sellers and infrastructure as we make that transition as well.

Chad Bennett -- Craig-Hallum Capital Group -- Analyst

Got it. Thanks.

Operator

Your next question comes from the line of Greg McDowell of JMP Securities. Please go ahead, your line is open.

Greg McDowell -- JMP Securities -- Analyst

Great. Thank you very much. I had two questions. First, I wanted to ask about the new metric, the customers over 100k.

I was actually encouraged by that metric just 38% year-over-year growth. It looks like on a sequential basis, 38 customers flipped over to over 100k per year. And so, I guess, the question is for Jim. As we sort of reorient our models to looking at that metric versus total customers or G8K customers, maybe what are some of the puts and takes on a go-forward basis that we should think about? Should it be in the 20 to 40 range, or was there something specific about Q4 to Q1 that made a difference in those customers flipping over? And then I have one follow-up.

Jim Frankola -- Chief Financial Officer

Got it. So there's -- let me try to get it quickly. So first of all, you mentioned both 38 and 38%. To be precise, it's 38 customers.

If you want to look at the history, the slide deck on the website will give you the last four quarters of sequential numbers. Regarding the numbers each quarter, it's going to vary. Right now, we're thinking somewhere between 100 and 150 a year fits within the business model, sort of analogous to -- our target last year was 125 or so G8K. So that is where the business model is built.

You will see some variability in each quarter. And then the part which -- I think I heard about the business model. I'd encourage you to think about our business in two pieces. You have customers less than $100,000.

Last year, that was about 8% of revenue. I think over time, that will go down slightly a few basis points each quarter. And then you can build your models on how many customers graduate from that less than $100,000 to the over $100,000 category. And then by definition, if they're over $100,000, their starting point of ARR will probably be between $100,000 and $150,000.

But there's -- specifically, there's really nothing special seasonally about the Q4, Q1 dynamic.

Tom Reilly -- Chief Executive Officer

And Greg, do you have another question?

Greg McDowell -- JMP Securities -- Analyst

Yes, just one real quick follow-up. I mean, the cash flow was certainly a highlight. I was just wondering if you could expand on that a little bit on why Q1 cash flow is so strong.

Jim Frankola -- Chief Financial Officer

Yes, So Q1 cash flow is always very strong because of the seasonal nature of our business. So, most of our deal -- 35% to 40% of our bookings are done in Q4. We typically collect the cash in Q1. We model collection of the cash both in Q1 and Q2.

So we never want to miss our number because some of our larger customers decided to pay us late by a couple of weeks. So what had happened in Q1 is the normal seasonality coupled with really good collection so everyone who needed to pay did pay. And then on top of that, you have the efficiencies. We're getting more efficient each year.

The Cloudera customer journey, where as customers grow, our unit cost economics increases, all added to the positive cash flow in the quarter.

Greg McDowell -- JMP Securities -- Analyst

Great. Thank you.

Jim Frankola -- Chief Financial Officer

Great. Operator, even though we're over the hour, we are fine taking a couple more questions if there's some out there.

Operator

Our next question comes from the line of Abhey Lamba of Mizuho Securities. Please go ahead, your line is open.

Abhey Lamba -- Mizuho Securities USA Inc. -- Analyst

Yeah, thank you. Thanks for taking my questions, guys. Tom, you mentioned this a couple of times. In what type of workloads are you seeing greater traction for -- of this offering? And who do you see as the competition in that area?

Tom Reilly -- Chief Executive Officer

Yes. So Altus predominantly has been in Data Engineering. And in Data Engineering, we compete directly against Amazon EMR. And that's where we've seen the greatest competition.

But now that we've introduced Altus ADB, Analytic Database, and Data Engineering and we're not only -- what we call, a multi-function, but we're also multi-platform running across the cloud providers. We expand our competition. So on Amazon, we compete against Redshift and EMR, so we've opened up that footprint. The next area we expect to eventually go into is in data science, opening up more of the footprint.

And one of our strategies is to be not only multi-function but also multi-cloud, and we do that through our SDX technology and hybrid. And so we think with every release, we get increasingly competitive and we open up more workloads in the cloud that we can address.

Mike Olson -- Co-founder, Chairman, and Chief Strategy Officer

Abhey, if I can add one point. This is Mike Olson. In the quarter, we also introduced, for the first time, our Altus Shared Data Experience offering, and that allows us to provide consistent governance compliance, regulatory support across all of those cloud workloads that Tom described. We think it'll help us win a larger share of large enterprise workloads, where those capabilities matter, where data governance compliance, regulatory support and so on make a difference.

Abhey Lamba -- Mizuho Securities USA Inc. -- Analyst

Got it. Thank you, very helpful. Jim, you mentioned about the $100,000 plus customer mark, which we understand is important. Two variables in that are the size of the landing customers and how quickly the ramp-up to the $100,000 mark.

Can you talk a little bit about how those two factors are kind of performing for you guys over the last few quarters? That's it for me.

Jim Frankola -- Chief Financial Officer

The landing mark and the expansion rate really hasn't changed very much. So historically, customers have landed in average of $70,000 a year, give or take. That dynamic didn't change in Q1. And then the expansion rate from that first use case and second case has been consistent with what we've seen in the past.

Abhey Lamba -- Mizuho Securities USA Inc. -- Analyst

Thank you.

Operator

Your last question comes from the line of Rishi Jaluria of D.A. Davidson. Please go ahead, your line is open.

Rishi Jaluria -- D.A. Davidson & Company -- Analyst

Hey, guys. Thanks for taking my questions and for squeezing me in the call. Tom, in your prepared remarks, you mentioned the desire to develop a channel partner to maybe serve the customers outside of your target market. And obviously, it's very early, but can you tell us a little bit more about the strategy, your thoughts here and maybe how you see this play out? And then I have a housekeeping question for Jim.

Tom Reilly -- Chief Executive Officer

Yeah, Rishi. So one of the exciting things about our technology is it is applicable to companies of all sizes, and there are many companies that can benefit from our technology. The way we're constructed today and the go-to-market though with the selling cost and the need to have expansions, we can't address all the market with our direct sales force. And so this is where we're now putting in place a channel that can more cost-effectively address a broader market on our behalf, so that channel -- we have so many inbound leads and opportunities that are kind of outside our current target market, at least 5,000 enterprises, so we're putting in place the ability to pass leads to them, to enable and train them to build our own qualification process so we make sure we're passing good opportunities to them, to provide support and training for the broader channel.

But that's early, and we have aspects of channel. We just haven't had the real, good discipline traditionally to use our resources only in our target market and have a channel [Inaudible] to the rest of it. In coming calls, we'll have more detail here, but it's one of the things when I'm talking to many of these candidates that want to bring onboard to be our field leader, they're experienced in building out this kind of channels.

Rishi Jaluria -- D.A. Davidson & Company -- Analyst

Got it. That's helpful. And Jim, just kind of from a housekeeping perspective, I know this has been touched on earlier in the call. I just want to make sure I fully understand.

So you've disclosed this new metric of customers running Cloudera on AWS, Azure, and GCP. And that's up 26% now. I know you've mentioned in the past that 20% of Cloudera customers are using Cloudera in the cloud. Can you just help me bridge the delta between why these metrics have a decent gap? That's it from my end.

Thanks.

Jim Frankola -- Chief Financial Officer

We've been disclosing the number of customers running in the cloud for several quarters now. So to be very precise, what that number is, is for customers who report diagnostic information to us, it is -- 26% of them are running in the cloud. If you actually go on our website, we have a Slide 22 which shows you the progression. So for example, in Q1 of last year, 22% of our customers were running in the cloud.

So if you go to the website, you should get all the historical information you need on this subject.

Rishi Jaluria -- D.A. Davidson & Company -- Analyst

Got it. Thanks.

Tom Reilly -- Chief Executive Officer

All right, Rishi. Any other questions, Rishi, before we wrap up?

Operator

We have no further audio questions.

Tom Reilly -- Chief Executive Officer

All right. Thank you, operator. Thanks all for staying long with us today. We wanted to keep our prepared remarks short.

We wanted to address your questions. We're happy many of you stayed with us long. We do appreciate your taking the time to understand, not only our exciting market but our strategy to how to tackle and win this market in the long run. We're making some exciting changes.

And my thanks to the Cloudera team and all our customers that might be listening here for working with us on this exciting journey. Thank you all. We look forward to talking to you in the quarter.

Operator

[Operator signoff]

Duration: 71 minutes

Call Participants:

Kevin Cook -- Vice President, Corporate Development, Investor Relations

Tom Reilly -- Chief Executive Officer

Jim Frankola -- Chief Financial Officer

Jacqueline Cheong -- Bank of America Merrill Lynch -- Analyst

Mike Olson -- Co-founder, Chairman, and Chief Strategy Officer

Sanjit Singh -- Morgan Stanley -- Analyst

Mark Murphy -- J.P.Morgan -- Analyst

Karl Keirstead -- Deutsche Bank -- Analyst

Michael Turits -- Raymond James -- Analyst

Tyler Radke -- Citi -- Analyst

Chad Bennett -- Craig-Hallum Capital Group -- Analyst

Greg McDowell -- JMP Securities -- Analyst

Abhey Lamba -- Mizuho Securities USA -- Analyst

Rishi Jaluria -- D.A. Davidson & Company -- Analyst

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