Companies are spending billions on AI, but the models are only useful if they can reach the right corporate data. That puts databases, data clouds, search, and observability at the center of the buildout.
MongoDB reports Tuesday after the close, followed by Snowflake Wednesday, giving you two fresh tests of the data layer underneath enterprise AI.
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Theme: Databases, Data Clouds, Search, Observability, Governance, and Enterprise AI Infrastructure
Every Company Has a Data Problem
Most large companies do not have one neat database containing everything they know.
Customer information sits in one system.
Transactions live somewhere else.
Product data, software logs, documents, employee information, and decades of historical records may be scattered across several clouds and older internal systems.
AI makes that fragmentation more obvious.
A model cannot intelligently use information it cannot find, access, understand, or trust.
That makes the data layer increasingly important.
AI Changes What Databases Need to Do
Traditional applications mostly retrieved structured information.
Modern AI applications may need customer records, text, images, documents, search results, historical context, and continuously changing operational data.
That creates demand for platforms capable of storing more types of information and making it available quickly.
For the companies in this basket, AI is not just another feature.
It can increase the amount of data customers store, move, search for, and monitor.
What's Driving It
MongoDB Is Tuesday's Main Catalyst
MongoDB releases fiscal Q2 results after Tuesday's market close, followed by its earnings call at 5:00 p.m. ET.
The company enters the report with strong momentum.
Fiscal Q1 revenue increased 25% to $687.6 million, while MongoDB Atlas revenue grew more than 29%.
MongoDB finished April with more than 67,700 customers, including over 66,400 Atlas customers and 2,895 customers generating at least $100,000 of ARR.
That scale matters.
MongoDB is increasingly trying to become the operational database underneath both traditional applications and new AI systems.
Atlas Is Becoming the Center of the Story
MongoDB's Atlas cloud platform generated about $512.5 million of Q1 revenue.
That represented nearly three-quarters of company revenue and continued growing faster than the broader business.
The attraction is straightforward.
Developers can run applications, search data, perform real-time analytics, and increasingly connect AI workloads without stitching together as many separate systems.
MongoDB has also added vector search and integrations designed to give AI applications access to live operational data.
The more work Atlas handles, the more valuable the platform can become.
Snowflake Gives Us a Second Test Wednesday
Snowflake follows with fiscal Q2 results after Wednesday's close.
Its latest quarter was even faster.
Q1 product revenue increased 34% to $1.33 billion, while remaining performance obligations jumped 38% to $9.21 billion.
Snowflake also reported a 126% net revenue retention rate and 779 customers generating more than $1 million of trailing product revenue, up 29%.
That tells you that existing large customers continue to spend more.
Snowflake guided Q2 product revenue to $1.415 billion to $1.420 billion, representing roughly 30% growth.
MongoDB handles much of the operational data behind applications.
Snowflake specializes in bringing together enormous amounts of enterprise data for analytics and, increasingly, AI.
Different products.
Same underlying trend.
Datadog Shows What Happens After Applications Go Live
Data does not stop mattering once the AI application is built.
Companies also have to understand what their systems are doing.
Datadog's Q2 revenue jumped 36% to $1.12 billion, while free cash flow reached $279 million.
The company finished June with roughly 4,720 customers generating more than $100,000 of ARR, up 23% year over year.
Datadog monitors applications, infrastructure, security, and increasingly AI systems.
More applications create more logs.
More AI agents create more actions.
More complexity creates more things that can break.
That makes observability another layer of the data opportunity.
Elastic Adds Search to the Stack
Elastic provides yet another part of the puzzle.
Its fiscal Q1 revenue increased 15% to $478 million, while current remaining performance obligations grew 21%.
Sales-led subscription revenue rose 18%, and the company recorded its strongest quarter yet for additions to the group of customers spending more than $100,000 annually.
Elastic's core technology helps companies search large quantities of information.
That capability becomes particularly useful when AI applications need to retrieve relevant information before generating an answer.
Sometimes the hardest part of AI is not producing text.
It is finding the correct information first.
The Chain Reaction
Companies deploy more AI → more corporate data needs to become accessible
Data moves onto modern platforms → cloud consumption rises
AI applications need live information → database workloads increase
More systems generate more logs → observability demand expands
Companies consolidate data platforms → customer spending rises
IT budgets tighten → cloud optimization slows consumption growth
What's Working
Large Customers Are Spending More
Snowflake's 126% net revenue retention rate indicates that existing customers are still substantially expanding their usage.
MongoDB's number of $100,000-plus ARR customers increased from 2,506 a year earlier to 2,895.
Datadog's equivalent customer group grew 23%.
Elastic just reported record quarterly additions to its $100,000-plus cohort.
That pattern matters.
The data opportunity is not relying entirely on finding new customers.
Existing customers are making these platforms more central to their technology stacks.
AI Can Increase Consumption
Several companies here use consumption-driven business models.
That means the customer does not simply buy a fixed number of seats.
Usage matters.
More queries can mean more revenue.
More data storage can mean more revenue.
More applications, logs, searches, and AI workloads can mean more revenue.
If enterprise AI moves from experimentation into production, these businesses can participate in the activity rather than simply selling another software license.
What to Watch
MongoDB Needs Atlas to Stay Strong
MongoDB guided Q2 revenue to $729 million to $734 million after raising its full-year outlook last quarter.
Watch:
Atlas growth
Total customer additions
$100,000-plus ARR customers
AI workload commentary
Gross margin
Operating margin
Full-year guidance
The key question is whether Atlas can keep growing around 30% as more enterprise workloads move onto the platform.
Snowflake Needs Consumption to Hold Up
Snowflake's report Wednesday provides the second half of the week's test.
Watch product revenue, net revenue retention, RPO, large-customer growth, operating margins, and AI-product usage.
Snowflake's product revenue growth accelerated to 34% last quarter after management initially guided for 27%.
Another strong quarter would make it harder to argue that enterprise data spending is simply riding a temporary AI experiment cycle.
MongoDB (MDB)
What it does: MongoDB provides a modern database platform used by developers to build and run applications across cloud and on-premises environments.
Why it fits: This is Tuesday's direct catalyst and one of the cleanest plays on operational data becoming more important to AI applications.
What stands out: Q1 revenue rose 25%, Atlas grew more than 29%, and the company now serves more than 67,700 customers.
What to watch: Atlas growth, customer additions, AI workloads, margins, and guidance.
The Takeaway: Buy this if you want the database layer underneath the next generation of applications. The risk is cloud consumption slowing if customers optimize spending.
Snowflake (SNOW)
What it does: Snowflake provides a cloud platform for storing, analyzing, sharing, and increasingly using enterprise data with AI.
Why it fits: It gives you Wednesday's major catalyst and the strongest large-scale data-cloud growth in the basket.
What stands out: Product revenue increased 34%, RPO grew 38%, and net revenue retention reached 126%.
What to watch: Product revenue, consumption, large customers, RPO, AI adoption, and margins.
The Takeaway: Buy this if you want the broad enterprise data platform with some of the strongest growth metrics in the group. The risk is high expectations if consumption growth cools.
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Datadog (DDOG)
What it does: Datadog monitors cloud infrastructure, applications, security systems, logs, and AI workloads.
Why it fits: As the technology stack becomes more complicated, companies need better visibility into what is actually happening inside it.
What stands out: Q2 revenue jumped 36% to $1.12 billion while free cash flow reached $279 million.
What to watch: Large-customer growth, AI workload adoption, platform expansion, margins, and free cash flow.
The Takeaway: Buy this if you want the monitoring layer that benefits as software and AI systems become more complicated. The risk is that customers consolidate or optimize cloud workloads.
Elastic (ESTC)
What it does: Elastic provides enterprise search, observability, and security software built around Elasticsearch.
Why it fits: Search becomes increasingly important when AI applications need to retrieve relevant information from enormous datasets.
What stands out: Fiscal Q1 revenue increased 15%, cRPO grew 21%, and RPO increased 27%.
What to watch: Subscription growth, cRPO, large customers, AI search demand, and margins.
The Takeaway: Buy this if you want the smaller enterprise-search play positioned around AI retrieval. The risk is intense competition from much larger cloud and data platforms.
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Oracle (ORCL)
What it does: Oracle provides databases, enterprise applications, and rapidly expanding cloud infrastructure.
Why it fits: It owns one of the world's largest installed bases of corporate databases and can increasingly connect that data with cloud and AI workloads.
What stands out: Fiscal Q4 cloud revenue jumped 47% to $9.9 billion, while cloud infrastructure revenue surged 93% to $5.8 billion. RPO reached $638 billion.
What to watch: Cloud infrastructure growth, database migrations, RPO conversion, capital spending, and margins.
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The Models Need Something to Work With
AI models get most of the attention.
Enterprise data may determine how useful those models actually become.
MongoDB stores the live information behind applications. Snowflake brings enormous datasets together. Datadog watches the systems using them.
Elastic helps find the information that matters. Oracle sits on decades of corporate data while building the infrastructure to process more of it.
For you, the next AI question may be less about who has the smartest model:
Who controls the data that makes the model useful?
Best Regards,
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Elite Trade Club
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