 from theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. Earnings season has shown a conflicting mix of signals for software companies. Well, virtually all firms are expressing caution over so-called macro headwinds. We're talking about Ukraine, inflation, interest rates, Europe, FX headwinds, supply chain, just overall IT spend. MongoDB along with a few other names appeared more sanguine thanks to a beat in the recent quarter and a cautious but upbeat outlook for the near term. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis ahead of MongoDB World 2022, we drill into Mongo's business and what ETR survey data tells us in the context of overall demand and the patterns that we're seeing from other software companies. And we're seeing some distinctly different results from major firms these days. We'll talk more about Mongo in this session which beat EPS by 30 cents in revenue by more than $18 million. Salesforce had a great quarter and its diversified portfolio is paying off as seen by the stock's noticeable uptick post earnings. UiPath, which had been really beaten down prior to this quarter, it's brought in a new co-CEO and its business is showing a nice rebound with a small three cent EPS beat and a nearly $20 million top line beat. CrowdStrike is showing strength as well. Meanwhile, management said Microsoft Workday and Snowflake expressed greater caution about the macroeconomic climate and especially on investors minds is concerned about consumption pricing models. Snowflake in particular which had a small top line beat cited softness and effects from reduced consumption especially from certain consumer facing customers which has analysts digging more deeply into the predictability of their models. In fact, Barclay's analyst, Ramo Lenshaw published an especially thoughtful piece on this topic concluding that Mongo was less susceptible to consumption headwinds than for example, Snowflake. Essentially for a few reasons. One, because Atlas, Mongo's cloud managed service which is the consumption model comprises only about 60% of Mongo's revenue. Second is a premise that Mongo was supporting core operational applications that can't be easily dialed down or turned off and three that Snowflake customers and Snowflake has a more concentrated customer base and due to that fact, there's a preponderance of its revenue is consumption driven and would be more sensitive to swings in these consumption patterns. Now I'll say this first, consumption pricing models are here to stay and the much preferred model for customers is consumption. The appeal of consumption is I can actually dial down turn off if I need to and stop spending for a while which happened or at least happened to a certain extent this quarter for certain companies but to the point about Mongo supporting core applications, I do believe that over time you're going to see the increased emergence of data products that will become core monetization drivers and Snowflake along with other data platforms is going to feed those data products and services and it become over time maybe less susceptible and less sensitive to these consumption patterns it'll always be there but I think increasingly it's going to be tied to operational revenue. Last two points here in the slide software valuations have reverted to their historical mean which is a good thing in our view we've taken some air out of the bubble and returned to more normalized valuations was really predicted and look forward to look we're still in a lousy market for stocks it's really a bear market for tech the market tends to be at least six months ahead of the economy and often not always but often as a good predictor we've had some tough compares relative to the pandemic days in tech and we'll be watching next quarter very closely because the macro headwinds have now been firmly inserted into the guidance of software companies. Okay, let's have a look at how certain names have performed relative to a software index benchmark so far this year here's a year to date chart comparing Microsoft Salesforce Mongo and Snowflake to the IGV software heavy ETF which is shown in the darker blue line by the way it does not own the CTF does not own Snowflake or Mongo. You can see that these big super caps have fared pretty well whereas Mongo and especially Snowflake those higher growth companies have been much more negatively impacted year to date from a stock price standpoint. Now let's move on let's take a financial snapshot of Mongo and put it next to Snowflake so we can compare these two higher growth names. What we've done here in this chart is taken the most recent quarters revenue and multiplied it by 4x to get a revenue run rate and we've parenthetically added a projection for the full year revenue. Mongo as you see will do north of a billion dollars in revenue while Snowflake will begin to approach $3 billion 2.7 and run right through that four quarter run rate that they just had last quarter and you can see Snowflake is growing faster than Mongo at 85% this past quarter and we took now these most of these profit of these next profitability ratios off the current quarter with one exception both companies have high gross margins of course you'd expect that but as we've discussed not as high as some traditional software companies in part because of their cloud costs but also their maturity or lack thereof both Mongo and Snowflake because they are in high growth mode have been operating margins they spend nearly half or more than half of their revenue on growth that's the SG&A line mostly the sales and marketing is really where they're spending money and they're specialist so they spend a fair amount of their revenue on R&D but maybe not as high as you might think but a pretty hefty percentage the free cash flow as a percentage of revenue line we calculated off the full year projections because there was a kind of an anomaly this quarter in the Snowflake numbers and you can see Snowflake's free cash flow which again was an abnormally high this quarter is going to settle in around 16% this year versus Mongo's 6% so strong focus by Snowflake on free cash flow and its management Snowflake is about $4 billion in cash and marketable securities on its balance sheet with little or no debt whereas Mongo has about $2 billion on its balance sheet with a little bit of longer term debt and you can see Snowflake's market cap is about double that of Mongo's so you're paying for higher growth with Snowflake you're paying for the Slutman, Scarpelli, execution engine, the expectation there stronger balance sheet, et cetera but Snowflake is well off it's roughly $100 billion valuation which it touched during the peak days of tech during the pandemic and just as an aside Mongo has around 33,000 customers about five times the number of customer Snowflake has so a bit of a different customer mix and concentration but both companies in our view have no lack of market in terms of TAM okay now let's dig a little deeper into Mongo's business and bring in some ETR data this colorful chart shows the breakdown of Mongo's net score net score is ETR's proprietary methodology that measures the percent of customers in the ETR survey that are adding the platform new that's the lime green at 9% existing customers that are spending 6% or more on the platform that's the forest green at 37% spending flat that's the gray at 46% decreasing spend that's the pinkish at around 5% and churning that's only 3% that's the bright red for Mongo subtract the red from the greens and you net out to a 38% which is a very solid net score figure note this is a survey of 1500 or so organizations and it includes 150 Mongo DB customers which includes by the way 68 global 2000 customers and they show a spending velocity or net score of 44% so notably higher among the larger clients and while it's a smaller sample only 27 in Mia's net score for Mongo's 33% now that's down from 60% last quarter note that Mongo cited softness in its European business on its earning call so that aligns to the ETR data okay now let's plot Mongo relative to some other data platforms these don't all necessarily compete head to head with Mongo but they are in data in database platforms in the ETR dataset and that's what this chart shows it's an XY graph with net score or as we say spending momentum on the vertical axis and overlap or presence or pervasiveness in the dataset on the horizontal axis see that red dotted line there at 40% that indicates an elevated level of spending anything above that is highly elevated we've highlighted Mongo in that red box which is very close to that 40% line it has a pretty strong presence on the X axis right there with GCP snowflake as we've reported has come down to earth but still well elevated again that aligns with the earnings releases AWS and Microsoft they have many data platforms especially AWS so their plot position reflects their broad portfolio massive size in the X axis that's the presence and very impressive on the vertical axis so despite that size they have strong spending momentum and you can see the pack of others including cockroach small on the vertical on the horizontal but elevated on the vertical couch basis creeping up since this IPO Redis MariaDB which was launched the day that Oracle bought Sun and got my sequel and some legacy platforms including the leader in database Oracle as well as IBM and Teradata's both cloud and on-prem platforms. Now one interesting side note here is on Mongo's earning call it clearly cited the advantages of its increasingly all-in-one approach relative to others that offer a portfolio of bespoke or what we sometimes call horses for courses databases. Mongo cited the advantages of its simplicity and lower costs as it adds more and more functionality this is an argument often made by Oracle and they often target AWS as the company with too many databases and of course Mongo makes that argument as well but they also make the argument that Oracle they don't necessarily call them out but they talk about traditional relational databases of course they're talking about Oracle and others they say that's more complex, less flexible and less appealing to developers than is Mongo. Now Oracle of course would return we retort saying hey we now support a Mongo DB API so why go anywhere else? We're the most robust and the best for mission critical but this gives credence to the fact that if Oracle is trying to capture business by offering a Mongo API for example that Mongo must be doing something right. Okay, let's look at why they buy Mongo. Here's an ETR chart that addresses that question. It's Mongo's feature breadth is the number one reason lower cost or better ROI is number two integrations and stack alignment is third and Mongo's technology lead is fourth. Those four kind of stand out with notice on the right hand side security and vision much lower there on the right that doesn't necessarily mean that Mongo doesn't have good security and good vision although it has been cited security concerns and so we keep an eye on that but look Mongo has a document database it's become a viable alternative to traditional relational databases meaning you have much more flexibility over your schema and in fact it's kind of scheme unless you can pretty much put anything into a document database developers seem to love it. Generally it's fair to say Mongo's architecture would favor consistency over availability because it uses a single master architecture as a primary and you can create secondary nodes in the event of a primary failure but you got to think about that and how to architect availability into the platform and got to consider recovery more carefully. Now no schema means it's not a tables and rows structure and you can again shove anything you want into the database but you got to think about how to optimize performance on queries. Now Mongo has been hard at work evolving the platform from the early days when you go back and look at its roadmap it's been started as a document database purely it added graph processing, time series it's made search much, much easier and more fundamental it's added Atlas that fully managed cloud database service which we said now comprises 60% of its revenue it's Kubernetes integrations and it's kind of the modern microservices stack in dozens and dozens and dozens of other features Mongo's done a really fine job we think of creating a leading database platform today that is loved by customers, loved by developers and is highly functional and next week, the cube will be at MongoDB world and we'll be looking for some of these items that we're showing here in this chart this always going to be main focus on developers Mongo prides itself on being a developer friendly platform we're going to look for new features especially around security and governance and simplification of configurations and cluster management Mongo's likely going to continue to advance its all in one appeal and add more capabilities that reduce the need to spin up bespoke platforms and we would expect enhancements to Atlas further enhancements there is Atlas really is the future maybe adding more cloud native features and integrations and perhaps simplified ways to migrate to the cloud to Atlas and improve access to data sources generally making the lives of developers and data analysts easier that's going to be, we take a big theme at the event so these are the main things that we'll be scoping out at the event so please stop by if you're in New York City at MongoDB world or tune in to the cube.net Okay, that's it for today. Thanks to my colleagues, Stephanie Chan who helps research breaking analysis from time to time Alex Meyerson is on production as today is Andrew Frick Sarah Kenny, Steve Conti, Anderson Hill and the entire team in Palo Alto, thank you Kristen Martin and Cheryl Knight help get the word out and Rob Hoth is our editor in chief over there at Silicon Angle remember all these episodes are available as podcasts wherever you listen just search breaking analysis podcasts we do publish each week on wikibon.com and siliconangle.com you want to reach me email me david.valante and siliconangle.com or DM me at Dvalante or a comment on my LinkedIn post and please do check out ETR.AI for the best survey data in the enterprise tech business this is Dave Vellante for theCUBE insights powered by ETR, thanks for watching, see you next time.