Morgan Stanley Sees MongoDB’s AI Catalyst Nearing, Lifts Price Target After Earnings
There’s a certain kind of earnings report that leaves investors scratching their heads—one that clears every near-term bar but still fails to answer the biggest question. MongoDB’s (MDB) fiscal first-quarter results, released May 28, checked all the boxes: revenue above consensus, a full-year guidance hike, and expanding margins. And yet the market’s nagging doubt remained: when will the AI engine actually kick in?
Morgan Stanley’s response, shared with TheStreet, captures the tension. The bank raised its price target on MongoDB to $380 from $335, maintaining an Overweight rating. The message in the note’s title was blunt: the stock is being priced for a company whose AI moment hasn’t arrived—but the timeline is tightening.
Year to date, MongoDB shares have fallen 24.65%, sharply underperforming the S&P 500’s 10.78% gain. That disconnect, Morgan Stanley argues, creates an opportunity for investors willing to wait out the final stretch before AI applications reach the maturity needed to drive a sustained inflection.
The Quarter at a Glance
MongoDB’s first-quarter scorecard showed solid execution. Revenue beat expectations, and the company lifted its full-year fiscal 2027 outlook by $60 million—more than the combination of the first-quarter beat and second-quarter raise. Management’s confidence, Morgan Stanley notes, suggests visibility into stronger second-half performance that isn’t fully reflected in near-term numbers alone.
The one soft spot: Atlas revenue grew 29%, slightly below the 30%-31% that some investors had hoped for, especially after strong recent prints from peers Datadog and Snowflake. But management characterized Atlas as becoming more predictable and less dependent on individual customer movements, describing the current trajectory as sustained growth rather than imminent acceleration.
“We delivered better-than-expected first quarter results, as our go-to-market teams continue to execute well,” CEO CJ Desai said in the earnings release.
Why Morgan Stanley Thinks the AI Wait Is Worth It
The $380 price target implies a 37x multiple on Morgan Stanley’s calendar 2029 base-case free cash flow estimate of roughly $1.27 billion, discounted back to calendar 2027 at a 10.3% weighted average cost of capital. That multiple expansion from approximately 33x reflects the firm’s view that the AI monetization timeline is compressing.
Morgan Stanley’s core argument is structural: for MongoDB to experience an AI-fueled inflection similar to Datadog and Snowflake, AI applications must cross a maturity threshold—becoming mission-critical to end customers, achieving significant product-market fit, and reaching material scale relative to the millions of existing MongoDB applications. That takes time. But Desai’s commentary on the first-quarter call was notably more constructive than in prior quarters.
He described early AI deployments with enterprise customers and growing momentum among AI-native companies, with MongoDB being used for mission-critical use cases and expanding within those accounts over time. The shift in tone, according to Morgan Stanley, is a meaningful signal: when a CEO who has been consistently cautious about AI timing starts sounding confident, it often precedes guidance revisions that move stocks.
Building the Infrastructure for AI
MongoDB isn’t waiting passively for the AI tailwind. The quarter included several strategic moves aimed at capturing the opportunity. A partnership with LangChain positions MongoDB Atlas as a unified backend for production-ready AI agents, integrating vector search, persistent memory, and natural-language querying into a single enterprise-grade platform. At MongoDB Local London, the company announced seven new platform capabilities designed to bridge the gap between AI experimentation and high-performance production.
The acquisition of Clarity Business Solutions strengthens MongoDB’s U.S. Federal vertical—a segment with mission-critical requirements and significant AI investment budgets. New leadership hires, including a Chief Revenue Officer, a Chief Product Officer for AI and Emerging Products, and a formalized Chief Information Security Officer, suggest a more aggressive posture.
Related: Bank of America resets MongoDB stock price target ahead of earnings
Read more: MongoDB just got a reality check from Wall Street
For investors, the picture is a company with a consistently growing core business, expanding margins, and an increasingly tangible AI roadmap. The stock, however, remains priced for a future that hasn’t fully materialized. Morgan Stanley believes that future begins to arrive toward the end of fiscal 2027—and that the wait, at $380, is worth taking.
More Tech Stocks:Morgan Stanley resets Walmart forecast on high inflation
This story was originally published by TheStreet on May 29, 2026, where it first appeared in the Investing section. Add TheStreet as a Preferred Source by clicking here.