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l obligations reflected in rpo engineering teams are choosing to allocate more spend to confluent as the quarter proceeds to me this signals the value customers are extracting from the confluent cloud product as they willingly spend more than they had budgeted this may reflect the larger overall theme that enterprises are scrambling to improve their data processing infrastructure in anticipation of leveraging ai to create new offerings for their customers employees and partners while the expected benefits from ai are still unfolding and enterprises are largely in proof of concept mode it is generally understood that effective ai requires good data quality data as an input for ai and ml processing isn t new but the priority has been increased this translates into more focus on consolidation filtering cleansing and categorization of silo ed data stores data recency is also recognized as an advantage which is creating a greater push towards real time data distribution it is this focus on reaching more data and delivering it in near real time that is served by confluent as other software infrastructure companies are experiencing rapid drops in nrr confluent appears to be holding up well while they don t share the actual values management reported that overall nrr is still over 130 and for cloud it exceeds 140 these are pretty remarkable numbers in this it environment for a product that is approaching an 800m annual run rate continue reading q2 2023 hyperscaler earnings review august 6 2023 poffringa headwinds from workload optimization may have peaked in q2 after nearly a year of negative drag on revenue growth for the hyperscalers it appears that cloud infrastructure customers are shifting their focus away from workload optimization and back towards the deployment of new ones at least that is what amazon s ceo told us in the opening statement of their earnings release while the hyperscalers delivered revenue growth rates that decelerated from the prior year they are now showing an uptick sequentially from q1 this circumstance makes sense after rapidly spinning up new cloud workloads during covid established enterprises and start ups alike postponed the normal post launch clean up cycles that address right sizing server instances tuning queries and rationalizing data retention in an environment of flush it budgets and pressure to ship fast it s easy for engineering teams to postpone this technical debt as budgets contracted and digital transformation project pace slowed down in 2022 engineering teams were encouraged to shift cycles back to paying down their technical debt with pressure to generate cost savings a lot of that work focused on optimizing existing application workloads engineering teams were able to review cloud resource utilization reset capacity allocations tune database queries clean up prolific logging and refactor code to run more efficiently all normal engineering best practices except that they happened all at once as this optimization work digested the oversized backlog of covid technical debt it created an unusually large reduction in resource consumption over a shorter period this translated into less revenue for the hyperscalers and software infrastructure vendors that are consumption based a normal application workload tuning exercise can reduce resource utilization by 20 30 or more excessively over provisioned capacity might even be cut by 50 simply by downsizing server instances the hyperscalers rightfully make this easy to execute as a consequence of elasticity but lower resource consumption means less revenue while this tuning is standard practice in the post covid catch up period it created a larger than normal reduction in spend enterprises were increasing consumption in some areas by launching new cloud workloads and digital transformation projects although probably at a slower rate but the immediate drop in cost for existing workloads created a large negative headwind to revenue growth this caused the prior cadence of sequential quarterly increases in hyperscaler spending to slow down rapidly and even go negative briefly the optimization catch up cycle can only last a finite amount of time eventually technical debt is worked off and covid workloads are tuned and right sized the effort to capture savings has diminishing returns or as microsoft s cfo said in april that workloads can t be optimized forever with the latest earnings commentary from the hyperscalers it appears the catch up period is wrapping up hyperscaler spend by enterprises will be increasingly driven by new activity with the negative drag created by post launch optimization returning to its pre covid levels where the steady state growth rate lands remains an open question data points during covid were clearly inflated as both large enterprises and rapidly growing start ups threw money at their cloud migration and digital transformation projects we can assume that this rate of new investment has slowed down from the peak but is still robust over the past 12 months negative consumption trends from optimization have obfuscated the post covid steady state when optimization stabilizes investors will get a clear idea of what the steady state growth rate will be unfortunately that growth rate won t be easily comparable to the pre covid period as another force has injected a new variable which is investment in ai as enterprises consider their ai strategy they are spinning up it projects to harness their data to create digital services powered by new ai models these efforts are generating demand for ai specific services from the hyperscalers there would be some spillover into demand for generalized software infrastructure services as well as growth rates for the hyperscalers potentially level out and even inflect back upwards investors can expect a similar pattern to follow for various software infrastructure providers logically if enterprises are consuming more compute and storage resources on the hyperscalers to support their new ai driven applications then a similar increase in demand should materialize for adjacent software services like databases monitoring delivery orchestration and application security serving content at scale to users worldwide over the internet still requires these things continue reading insights from a pair of data summits july 23 2023 poffringa ai has crashed onto the investing stage in 2023 driving significant stock price gains for several companies some like nvidia and microsoft have already projected a direct revenue benefit as part of recent earnings reports others have indicated they expect ai to drive demand tailwinds going forward as part of management commentary eventually most software service and infrastructure providers should benefit from increased demand as ai services proliferate and contribute to all areas of the economy because many ai services are delivered through internet based applications the same guardrails of security delivery monitoring and operational data storage will be needed this is in addition to the increased consumption of data services to collect prep distribute and process the inputs for various ai models ai driven expert systems and co pilots will raise the productivity of information workers enterprises will need fewer of them to accomplish the same amount of work this will free up budget to increase spend on ai software services similar to the efficiencies gained from the proliferation of saas tools over the last decade that helped internal business teams automate most aspects of their operations software development teams in particular will experience a significant boost in output per employee enterprises will be able to clear their application backlogs more quickly increasing the demand for hosting infrastructure and services at steady state fewer developers will be needed supporting a shift of it budget from salaries to software as data is the largest ingredient to these enterprise ai development efforts software vendors providing data processing and infrastructure services stand to benefit ai has further elevated the value of data incentivizing enterprise it leadership to review and accelerate efforts to improve their data collection processing and storage infrastructure every silo ed data store is now viewed as a valuable input for fine tuning an even more sophisticated ai model in the realm of big data processing enterprises need a place to consolidate clean and secure all of their corporate data given that more data makes better ai enterprise data teams need to ensure that every source is tapped they are scrambling to combine a modernized data stack with an ai toolkit so that they can rapidly efficiently and securely harness ai capabilities to launch new application services for their customers partners and employees at the center of these efforts are the big data solution providers these include legacy on premise data warehouses cloud based data platforms and of course the hyperscalers among these snowflake and databricks are well positioned representing the fastest growing modern data platforms that can operate across all three of the hyperscalers while the hyperscalers will win their share of business enterprise data team leadership often expresses a preference for an independent data platform where feasible fortunately for investors snowflake and databricks held their annual user conferences recently perhaps it was intentional that they fell within the same week at least they staggered the events between the first and second half both companies made major product and partnership announcements leading to many comparisons between the two and speculation about changes in relative product positioning the market for the combination of big data and ai processing will be enormous with some projections reaching the hundreds of billions of dollars in annual spend while the snowflake and databricks platforms are clearly converging in feature set scope they still retain different approaches based on their historical user types such a large market will likely support multiple winners continue reading mongodb mdb q1 fy2024 earnings review july 2 2023 poffringa after guiding for a sequential 4 drop in revenue for q1 mongodb delivered a strong beat more importantly their preliminary estimate for q2 revenue would achieve a reacceleration of annual growth if they outperform at the same level as q1 the revenue projection for q2 even leapfrogged past the analyst consensus for q3 while the market expected some conservatism this level of outperformance caught investors by surprise with the stock surging 28 the next day equally impressive were improvements in profitability in the past mongodb has been discounted for poor operating leverage the transition to 2023 has brought record levels of operating income and fcf closing the gap with peers in the software infrastructure space this also led to a significant beat and raise on eps which we don t often see with high growth saas companies even customer activity notched records both total customer additions and those with spend over 100k in arr represented all time quarterly highs of new customers over 200 companies are categorized in the burgeoning ai industry providing another catalyst as these start ups are landing new capital at levels on par with the covid beneficiaries of 2020 2021 mongodb has emerged as a hot stock once again with its valuation multiple now pressing up against the top of its peer group the stock has more than doubled in 2023 and reached its 52 week high recently mongodb is well positioned to capitalize on tailwinds from ai as enterprises revamp their data infrastructure to deliver new insights and services from their proprietary data sets with new product announcements at mongodb local in june mongodb is further supporting the case for consolidation of application workloads onto its developer data platform continue reading snowflake snow q1 fy2024 earnings review june 19 2023 poffringa snowflake s consumption business continues to feel pressure as large enterprise customers look for ways to optimize usage while it seemed that management had a handle on forecasting the impact of this effect when they lowered guidance back in q4 they still underestimated the trend with the q1 report they once again brought down the full year revenue target this had the expected effect of torpedoing the stock the day after earnings with a post earnings drop of 16 5 after that a strange thing happened the stock began appreciating again and surpassed its pre earnings price two weeks later as with other software infrastructure companies the market s perception that demand for ai services will drive incremental usage is propping up the stock snowflake management added to this momentum with a few key announcements including a preview of their upcoming snowflake summit conference which will include a fireside chat with nvidia s ceo this all implies that snowflake is positioning themselves to benefit from increased ai workload demand in the near term snowflake is being directly impacted by their consumption model which magnifies changes in customer behavior as they identify ways to reduce costs looking forward the market is anticipating the moderation of these optimization headwinds as enterprises work through the low hanging fruit of cost savings at that point snowflake s growth should return to its prior cadence driven by new cloud migration workloads and the expansion of existing ones given that enterprise benefit from ai hinges on access to a consolidated clean and secure data set snowflake is well positioned to serve as a primary data source their positioning is further solidified as the same environment could be leveraged to run jobs that enhance the ai models this applies to llms and other foundation models as well as more traditional types of machine learning output like recommenders snowpark steamlit and other extensions that make the environment more programmable started this process new acquisitions are bolstering the platform s capabilities as well investors are looking towards announcements at the upcoming summit user conference for more insight into snowflake s planned ai capabilities continue reading datadog ddog q1 2023 earnings review june 4 2023 poffringa datadog stock surged 45 over the month of may following their earnings report on may 4th the results aligned with the common theme of better than expected shared with several other software companies reporting results recently this outperformance appears to have set a baseline across the software sector with upward momentum building as more companies report results a new tailwind has been excitement around the potential for ai to drive an incremental demand cycle for software and security infrastructure while ai holds promise it will require several quarters or even years to play out in the meantime enterprise it spend moderation workload optimization and deal s...