MongoDB po výsledcích sice překonala odhady a zvýšila výhled hospodaření, ale akcie v jedné obchodní seanci spadly o 13,6 %. Trhu vadil rozdíl mezi GAAP ziskem a non-GAAP ziskem kvůli 153 milionům USD na kompenzace v akciích.
MongoDB crushed estimates and lifted guidance, then watched investors punish the stock anyway. The reason comes down to a single line buried in the earnings report that separates what the company reported from what it actually earned.
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Beat and Raise, Then a 13.6% Gut Punch MongoDB (NASDAQ: MDB | MDB Price Prediction) delivered a textbook beat-and-raise after the close on September 1, 2026, then watched shares fall 9.2% by late morning on September 2, extending losses that the article title pegs at 13.6% for the session. Revenue grew 30.5% year over year, guidance moved higher, and none of it mattered. Investors made clear they cared more about how the profit was built than the profit itself.
Growth Reaccelerates, Atlas Still Leads The top line was genuinely strong. Revenue hit $771.8 million versus $734.4 million expected, the highest quarterly growth since fiscal 2024. Atlas revenue reached $565.9 million, up roughly 29%, and Enterprise Advanced added $181.2 million, up about 36%. Remaining performance obligations jumped 91% to $1.52 billion, a real forward-visibility win. I liked the RPO number. It is the cleanest evidence that enterprise commitments are hardening.
Accounting Story That Broke the Rally Here is why the stock cracked. On CNBC’s opening bell coverage, the panel walked through how $40.9 million of GAAP net income became $162.6 million of non-GAAP net income after adding back $153 million of stock-based compensation. That add-back equals 94% of the reported non-GAAP net income. On a per-share basis, the stock-comp add-back was $1.91, larger than the entire $1.90 adjusted EPS shareholders were shown. Management’s own outlook makes the gap official: full-year GAAP EPS of $0.53 to $0.77 against non-GAAP EPS of $6.39 to $6.58.
Numbers Tell the Story Revenue: $771.8M vs $734.4M expected; up 30.5% YoY Non-GAAP EPS: $1.90 vs $1.61 expected (beat by 18.09%) GAAP Net Income: $40.9M vs Non-GAAP Net Income of $162.6M Stock-Based Comp Add-Back: $153M ($1.91/share) Free Cash Flow: $137.6M, up 92.29% YoY FY27 Revenue Guide: $2.99B to $3.03B (raised) You should look at the gap between GAAP and non-GAAP EPS. That single spread is the entire bear case in one line.
CEO Leans Into the AI Narrative CEO CJ Desai said MongoDB is “emerging as the intelligent data platform for the AI era” and pointed to “strength driven by core enterprise workloads and early momentum with AI use cases” as the basis for raising the outlook. It was a confident tone. Investors wanted that confidence expressed in cash earnings.
Context and What to Watch Into Investor Day Zoom out and the pain is sharper. MongoDB has returned just 8.11% over five years, even after a 28.66% run in the prior month. Compare that with today’s other AI-adjacent movers, where Dell (NYSE:DELL) popped on its own AI-server beat Wednesday morning. I would keep an eye on the Investor Day on September 29 in New York. If management addresses the dilution math head on, sentiment can reset. If not, the accounting question will keep following this stock.
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MongoDB klesá před otevřením trhu navzdory silnému druhému čtvrtletí, protože investory znepokojuje zpomalení růstu Atlasu na očekávaných 26 % ve třetím čtvrtletí zhruba z 29 %.
MongoDB MDB is experiencing a significant drop in pre-market trading, even after reporting a strong Q2 performance that exceeded expectations. Investors are concerned about the slowdown in Atlas growth, which decreased from approximately 29% to a projected 26% for Q3, along with an anticipated further moderation in Q4. Although management raised its FY27 Atlas growth forecast by 300 basis points to around 27%, the overall guidance for Q3 includes an EPS estimate of $1.57-$1.61 and revenue expectations of $756-$761 million. Additionally, FY27 guidance has been lifted to an EPS range of $6.39-$6.58 and revenue of $2.99-$3.03 billion. However, these positive updates did not meet the high expectations reflected in the stock's near 52-week high trading levels.
Atlas achieved a record revenue increase of $127 million year-over-year, marking the sixth consecutive quarter of growth. The company-wide net Annual Recurring Revenue (ARR) expansion rate rose to 122% from 121% sequentially, with contributions from both Atlas and Enterprise Advanced (EA). Remaining Performance Obligations (RPO) surged 91% to $1.52 billion, while current RPO grew by 73%. However, this growth is influenced by multiyear EA agreements and does not directly indicate Atlas consumption. EA and other revenue increased by approximately 36%, with FY27 growth guidance revised up to about 11%, compared to a previous mid-single-digit forecast. Nonetheless, management expects EA growth to remain in the mid-single digits for Q3 and flat for the latter half of the year due to unpredictable deal timing. MDB welcomed a record 2,900 new customers, bringing the total to 70,600. Customers generating at least $100,000 in ARR increased by 17% to nearly 3,000, and Voyage customers doubled sequentially for the second consecutive quarter. Non-GAAP operating margin improved to 24% from 15%, while gross margin rose by 210 basis points to 75.9%, partly due to a higher proportion of profitable EA revenue. MDB anticipates around 250 basis points of operating margin expansion for FY27, but Q3 margin guidance suggests that Q2 should not be viewed as the new run rate.The key issue lies in the market's high expectations for MDB's Atlas growth trajectory, despite the company improving its annual growth and profitability outlook. Management indicated that consumption trends remain steady, with Q3 presenting the toughest year-over-year comparison. Recent quarterly Atlas guidance has exceeded expectations by 200-300 basis points, suggesting that the projected Q4 slowdown may be conservative. However, visibility on consumption diminishes beyond one quarter, and holiday activities could impact Q4 usage. Additionally, EA's multiyear contracts contribute to revenue variability. While AI adoption bolsters the long-term platform strategy, investors are seeking assurance that Voyage, Vector Search, and production agents can generate significant revenue. Upcoming evaluations will focus on Atlas consumption from September to October, EA deal conversions, multi-product adoption, and MDB's execution against its targeted Rule-of-44 profile.
This stock alert was generated using automated technology and GuruFocus financial data to provide readers with timely and accurate market reporting. This content was reviewed by GuruFocus editorial team prior to publication. Please send any questions or comments about this story to [email protected].
Disclosures I/We may personally own shares in some of the companies mentioned above. However, those positions are not material to either the company or to my/our portfolios.
MongoDB, Inc. (MDB) Q2 2027 Earnings Call September 1, 2026 5:00 PM EDT
Company Participants
Jess Lubert - Vice President of Investor Relations
Chirantan Desai - President, CEO & Director
Michael Berry - CFO & Principal Financial Officer
Conference Call Participants
Raimo Lenschow - Barclays Bank PLC, Research Division
Aleksandr Zukin - Wolfe Research, LLC
Matthew Martino - Goldman Sachs Group, Inc., Research Division
Karl Keirstead - UBS Investment Bank, Research Division
Sanjit Singh - Morgan Stanley, Research Division
Ryan MacWilliams - Wells Fargo Securities, LLC, Research Division
S. Kirk Materne - Evercore ISI Institutional Equities, Research Division
Tyler Radke - Citigroup Inc., Research Division
Koji Ikeda - BofA Securities, Research Division
Presentation
Operator
Hello, and welcome to MongoDB's Second Quarter Fiscal '27 Earnings Call. [Operator Instructions]
I would now like to hand the conference over to Jess Lubert, Vice President of Investor Relations. You may begin.
Jess Lubert
Vice President of Investor Relations
Thank you, operator. Good afternoon, and thank you for joining us today to review MongoDB's Second Quarter Fiscal 2027 Financial Results, which we announced in our press release issued after the close of market today. Joining me on the call today are CJ Desai, President and CEO of MongoDB; and Mike Berry, CFO of MongoDB.
During this call, we will make forward-looking statements, including statements related to our market and future growth opportunities, our opportunity to win new business, our expectations regarding Atlas assumption growth, the impact of EA and other business and multiyear license revenue and the long-term opportunity of AI, our financial guidance and underlying assumptions, including expectations regarding profitability and operating margin and our investments in growth opportunities in AI.
These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions that could cause actual results to differ materially from our expectations. For a discussion
Live 6 updates · Last at 4:51pm ET Updates appear automatically.
By Thomas Richmond · Updated Sep 1, 4:51pm ET · Published Sep 1, 3:04pm ET
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Live UpdatesNewest first
That wraps up our initial coverage of MongoDB’s results. Thank you for stopping by!
MongoDB just reported earnings, with shares initially down 13% following the report. Here are the key numbers:
Revenue: $772 million vs. $734 million expected Adjusted EPS: $1.90 vs. $1.61 expected RPO: $1.52 billion, up 91% year over year Atlas Revenue: $566 million, up 29% year over year Guidance:
FY27 Revenue: $3.01 billion vs. $2.96 billion expected FY27 EPS: $6.49 vs. $6.13 expected Quick Read:
A strong beat-and-raise isn’t enough: MongoDB topped revenue and EPS expectations and lifted its full-year outlook above consensus, yet shares initially plunged 13%. Atlas growth remains strong at 29%: With RPO surging 91%, the selloff suggests expectations and valuation were simply extremely high after the stock’s sharp run into earnings, rather than an obvious deterioration in headline fundamentals.
Wall Street models Q2 revenue near and EPS of , sitting right at the top of MongoDB‘s (NASDAQ:MDB) own guide of and . CFO Mike Berry said guidance while staying a framework that produced .
Bullish: FY27 revenue lifted above , Atlas sustained near , and full-year EPS raised above .
Bearish: An unchanged FY27 range, Atlas decelerating below 27%, or a Q3 view soft versus the consensus.
CFO Mike Berry noted Atlas has as it scales. RPO of , up year over year, gives management cover to raise. Anything less than a confident raise risks a repricing.
Ahead of tonight’s MongoDB (NASDAQ:MDB) earnings release, here’s the analyst playbook for the call.
Top 5 Analyst Questions Can Atlas sustain as it laps tougher comps? What is real revenue contribution from Voyage AI, Vector Search, and agent memory? How durable is as forward visibility? Federal traction after the Clarity Business Solutions acquisition? Path to consistent GAAP profitability given stock-based comp? Key Topics Management Must Address Magnitude of the FY27 raise above Enterprise Advanced decline trajectory Progress under CEO CJ Desai and new CPOs Buzzwords to Listen For Rule of 40, agentic AI, unified data platform, consumption trends, mission-critical workloads Red Flags Atlas below 29%, softer Q3 guide, slowing customer adds, or macro caution commentary
Bull Case: Why MongoDB Could Beat and Rally Atlas grew in Q1 with RPO up to , signaling durable forward visibility. Polymarket assigns a probability of a beat, backed by upward EPS revisions and zero cuts in 30 days. Agentic AI wins with Adobe (NASDAQ:ADBE), , and validate Atlas as a scaled AI backend for enterprise workloads. Bear Case: Why the Setup Looks Stretched Shares are up in a month, leaving little margin for error. Q4 FY26 topped estimates by , yet shares plunged on guidance concerns. Q3 FY27 consensus saw downward EPS revisions, hinting at a softer second-half setup. Leadership turnover adds execution risk against elevated expectations.
MongoDB is expected to report Q2 FY27 earnings at 4:05 PM ET, with management guiding for $729-$734 million in revenue. The bigger question is whether Atlas can sustain the roughly 26% growth management has targeted for the quarter.
Expectations have risen sharply. MongoDB shares are up 34.34% over the past month, while Polymarket traders are assigning a 97.1% probability that the company beats expectations.
That makes guidance especially important. A strong quarter accompanied by a raise that pushes FY27 revenue above $2.96 billion would strengthen the case that MongoDB is becoming a major beneficiary of agentic AI and growing database demand.
In Q4 of FY26, MongoDB topped expectations by 12.08%, yet shares still plunged 22.24%. With the stock rallying into tonight’s print, investors will likely demand both strong results and an improving outlook.
This article is updated throughout the trading day. Check back for more.
Full CoverageThe story so far
MongoDB (NASDAQ:MDB | MDB Price Prediction) reports Q2 FY27 results today at 4:05 PM ET. The database platform enters the report with a $35.9 billion market cap, with the stock down 3.5% today.
Momentum Meets a Higher Bar Last quarter, MongoDB posted $687.6 million in revenue, up 25.25% year over year, and non-GAAP EPS of $1.32 versus a $1.1835 consensus.
Atlas, now roughly 75% of revenue, grew 29.4% and crossed a $2 billion run rate. Free cash flow nearly doubled to $197.5 million, and RPO jumped 88% to $1.46 billion. CEO CJ Desai raised full-year guidance on that strength. Shares have followed through, with MDB rising 43.65% over the past year and trading at $437.47 into the report.
Consensus Estimates Metric Q2 FY27 Estimate YoY Change FY27 Estimate FY28 Estimate Revenue $734.4M +24% $2.96B $3.48B EPS (Normalized) $1.609 +145% $6.13 $7.34 The Q2 EPS bar has climbed from $1.28 ninety days ago to $1.609, with 34 upward revisions in the past 30 days and zero cuts. Consensus now sits above the guidance midpoint, meaning MongoDB effectively needs to clear its own top end to keep the beat streak intact.
What I’m Watching Tonight: Atlas Consumption, AI Workloads, and Margin Leverage Tonight, I’ll be watching Atlas consumption first. Management guided Q2 Atlas growth to approximately 26%, a deceleration from 29.4%, and CFO Mike Berry described the segment as “more predictable and less sensitive” at scale. Any reacceleration reframes the multiple.
Second, analysts are of course going to be watching AI traction. Desai said “AI adoption of MongoDB technologies across our customer base continues to accelerate,” with vector search “far outpacing overall company growth.” New Adobe, Zomato, and 11 Labs deployments should give management fresh proof points.
Third, margins. Non-GAAP operating margin expanded to 18% from 16%, and the company targeted approximately 21% at the Q2 high end. Rule of 40 status is on the line.
Fourth, EA. Enterprise Advanced grew 13% last quarter, but management guided to approximately flat EA growth in the second half. Deal timing commentary matters.
Finally, the Clarity acquisition and federal push. Investors want scope on the $10 million annual services contribution and pipeline build.
Earnings History Quarter EPS Surprise Day-Of Move 1-Day Move 1-Week Move Q1 FY27 +11.53% +3.03% +20.36% +4.53% Q4 FY26 +12.08% -22.24% -1.87% +7.05% Q3 FY26 +66.23% +22.23% +0.98% +3.00% Q2 FY26 +52.39% +37.96% +7.58% +8.91% On average, shares moved 3.39% seven days after earnings over the past year.
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Thomas Richmond
Thomas Richmond is a financial writer and content strategist with 5+ years of experience covering stocks and financial markets. He has published over 250 articles focused on individual stock analysis, helping investors better understand business fundamentals, stock valuations, and long-term opportunities.
Thomas previously served as a Content Lead at TIKR, a stock research platform, where he helped scale the company’s blog to hundreds of articles per month and contributed to a weekly newsletter reaching more than 100,000 investors.
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MongoDB míří na výsledky za druhé čtvrtletí, zatímco investoři sledují, zda se poptávka od zákazníků z oblasti AI projeví v číslech. UBS čeká, že Atlas poroste o 29–30 % už páté čtvrtletí v řadě, ale zatím nevidí jasný důkaz výrazné poptávky po AI.
MongoDB Inc (NASDAQ:MDB) heads into its second-quarter earnings report Tuesday with investors watching for signs that red-hot demand from AI-native customers is starting to show up in the numbers.
The compay’s cloud-hosted Atlas segment is on track for a fifth straight quarter of 29-30% growth, analysts at UBS noted.
UBS said MongoDB guided to 26% Atlas growth for the quarter, implying $41 million in sequential dollar adds compared with $43 million a year earlier, a guide the firm called conservative.
Based on beats of roughly 1.5 and 2.5 percentage points in the prior two quarters, UBS said many investors expect a similar beat, landing Atlas growth in the 29-30% range. The firm is modeling total revenue growth of 28%, a 3-point beat, with non-Atlas revenue guided to 20% growth on the timing of multi-year deal renewals.
Looking ahead to the following quarter, UBS said Atlas would need $40-45 million in sequential dollar adds to sustain a sixth straight quarter of 29-30% growth, up from $31 million a year earlier. The firm expects MongoDB to guide conservatively at around 25% before a typical beat lands growth back near 29%. UBS models full-year revenue growth of 24%, above MongoDB's own 20% guidance and ahead of what it sees as consensus expectations near 22%.
MongoDB raised its full-year operating income guidance by $26 million, above its first-quarter beat, implying an operating margin of 20%, up 150 basis points year over year. UBS said further margin upside looks less likely this year as the company continues investing in AI product capabilities, go-to-market headcount, and expansion in Japan and the US federal vertical.
UBS also pointed to MongoDB's growing ties with AI-native companies, including references to Anthropic and signals suggesting OpenAI as a customer, alongside smaller AI-native names such as Factory, Fireworks, ElevenLabs and Mercor. Still, the firm said it remains unclear how large these relationships are, noting customer checks showed stable spending growth but no clear evidence yet of material AI-driven demand for MongoDB specifically.
MongoDB má po uzavření trhu zveřejnit výsledky za 2. čtvrtletí; analytici čekají EPS 1,61 USD a tržby 734,4 mil. USD, po loňských 1,00 USD a 591,4 mil. USD.
MongoDB, Inc. (NASDAQ:MDB) will release its second earnings report after the closing bell on Tuesday, Sept. 1.
Analysts expect the New York-based company to report quarterly earnings of $1.61 per share, up from $1.00 per share in the year-ago period. The consensus estimate for MongoDB’s quarterly revenue is $734.4 million. It reported $591.4 million last year, according to Benzinga Pro.
On May 28, MongoDB reported better-than-expected first-quarter financial results and issued second-quarter guidance above estimates.
MongoDB shares gained 1.4% to close at $446.62 on Friday.
Benzinga readers can access the latest analyst ratings on the Analyst Stock Ratings page. Readers can sort by stock ticker, company name, analyst firm, rating change or other variables.
Let’s have a look at how Benzinga’s most-accurate analysts have rated the company in the recent period.
DA Davidson analyst Rudy Kessinger maintained a Buy rating and raised the price target from $375 to $465 on Aug. 26, 2026. This analyst has an accuracy rate of 75%. Barclays analyst Raimo Lenschow maintained an Overweight rating and boosted the price target from $387 to $460 on Aug. 26, 2026. This analyst has an accuracy rate of 71%. Wells Fargo analyst Ryan Macwilliams maintained an Overweight rating and raised the price target from $375 to $475 on Aug. 25, 2026. This analyst has an accuracy rate of 67%. Baird analyst William Power maintained a Neutral rating and boosted the price target from $335 to $400 on Aug. 25, 2026. This analyst has an accuracy rate of 84%. UBS analyst Karl Keirstead maintained a Neutral rating and increased the price target from $350 to $460 on Aug. 24, 2026. This analyst has an accuracy rate of 75%. Trending
Considering buying MDB stock? Here’s what analysts think:
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MongoDB uvedla, že firmy potřebují sjednocenou datovou architekturu pro nasazení AI agentů do provozu. Oddělené databáze podle ní zvyšují zpoždění, synchronizační problémy a rizika v bezpečnosti.
5 Stocks to Buy in September Before Wall Street Catches OnMongoDB NASDAQ: MDB said enterprises moving artificial intelligence applications and autonomous agents into production need unified data architectures that combine operational information, historical records, business rules and security controls.
Speaking during The 2026 Six Five AI Summit, Ashish Kumar, MongoDB’s senior vice president and technical fellow, said the focus of enterprise AI development has shifted from selecting foundation models to providing those models with trusted, current context.
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MongoDB Is Surging—And the Next Catalyst Is Almost Here“We’re moving from this static, deterministic code to these autonomous AI agents that are perceiving, reasoning, and acting on the fly,” Kumar said. He argued that organizations initially added standalone vector databases to legacy technology stacks as generative AI adoption accelerated, but that approach created data synchronization problems and latency.
According to Kumar, enterprises cannot effectively operate AI in production when vector data, operational data and security rules are maintained in separate systems. Instead, he said companies need a unified platform that can provide AI applications with data and governance in the same environment.
Context, memory and operational data MarketBeat Week in Review – 06/01 - 06/05Kumar described foundation models as sophisticated reasoning engines that lack knowledge of an individual company’s operations without relevant context. He defined useful context as the combination of real-time operational signals, historical records and explicit business rules that an AI system can consume and trust.
Without that context, he said, models may make educated guesses based on raw text rather than decisions grounded in current business conditions. Kumar cited AT&T as an example, saying the telecommunications company combines real-time network signals and historical outage data to help AI determine where repair crews should be sent.
Kumar said that effort avoided 3.1 million unnecessary dispatches and saved $12 million in downtime. He emphasized that poor context can create material consequences when AI agents are used in physical operations or interact with billing systems.
The MongoDB executive also highlighted the importance of statefulness and memory for agentic applications. An agent that cannot retain information from earlier steps, understand user objectives or remember preferences over time is “really just an expensive chatbot,” he said.
For complex processes involving multiple steps and approvals, agents need to preserve their state over extended periods, Kumar said. He characterized that need as a core data challenge, particularly at large scale.
Kumar said an unnamed frontier AI laboratory moved more than 50 billion conversations from Postgres to MongoDB Atlas in four weeks to address conversational-state requirements. The system involved hundreds of petabytes of conversational state, with sub-millisecond reads and no reported downtime, according to Kumar.
Flexibility and performance MongoDB customers are increasingly moving beyond experimental chatbot projects and integrating agentic workflows into mission-critical business processes, Kumar said. Based on his discussions with roughly 100 MongoDB customers over the prior nine months, he identified real-time performance and architectural flexibility as two key requirements.
AI agents need access to conversational history, enterprise context, semantic search and full-text search capabilities on current operational data, he said. Kumar added that an operational data layer must support spikes in reads and writes while maintaining low latency.
He cited Emergent Labs, a MongoDB customer, as an example of the value of flexible data modeling. Kumar said the company evaluated Postgres before selecting MongoDB Atlas because agents building applications require data models that can change frequently. He said Emergent Labs has powered about 2 million agentic applications on MongoDB.
Kumar also pointed to Macquarie, which built a retail business payments platform requiring continuous availability. He said MongoDB Atlas gave Macquarie portability across environments as it scaled to support millions of transactions, rather than tying the operation to a single cloud provider.
That flexibility is increasingly important because companies may need agents to operate near data held in other cloud environments or in on-premises, air-gapped networks, Kumar said. Organizations should be able to run the same agent functionality across those settings without rewriting it, he added.
Advice for enterprise AI deployments Kumar advised businesses to avoid adding more specialized point products simply to launch AI pilots quickly. He said each additional database or single-purpose tool can add synchronization delays, security risks and technical debt.
Rather than replacing legacy databases, Kumar recommended building an operational data layer in front of existing systems. Such a platform can consolidate and enrich data with metadata, create vector embeddings where data resides, and provide AI agents with controlled access to context, security rules and guardrails, he said.
He added that companies can connect a unified data layer to AI models and enterprise data through MCP and other available standards. The objective, Kumar said, is to create a practical foundation for AI agents that require resiliency, high throughput, low latency and trusted information.
About MongoDB (NASDAQ:MDB)MongoDB, Inc is a software company best known for developing MongoDB, a general-purpose, document-oriented database designed for modern application development. The company's platform is built to support high-performance, scalable data storage and retrieval for use cases such as cloud-native applications, mobile backends, real-time analytics, and content management. MongoDB offers a mix of open-source software, commercial server distributions, and subscription-based services that include technical support, training and professional services.
The company traces its origins to 2007 when it was founded as 10gen by Dwight Merriman and Eliot Horowitz; it later adopted the MongoDB name and completed a public listing in 2017.
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In its upcoming report, MongoDB (MDB - Free Report) is predicted by Wall Street analysts to post quarterly earnings of $1.60 per share, reflecting an increase of 60% compared to the same period last year. Revenues are forecasted to be $733.61 million, representing a year-over-year increase of 24%.
The current level reflects no revision in the consensus EPS estimate for the quarter over the past 30 days. This demonstrates how the analysts covering the stock have collectively reappraised their initial projections over this period.
Prior to a company's earnings release, it is of utmost importance to factor in any revisions made to the earnings projections. These revisions serve as a critical gauge for predicting potential investor behaviors with respect to the stock. Empirical studies consistently reveal a strong link between trends in earnings estimate revisions and the short-term price performance of a stock.
While investors typically rely on consensus earnings and revenue estimates to gauge how the business may have fared during the quarter, examining analysts' projections for some of the company's key metrics often helps gain a deeper insight.
In light of this perspective, let's dive into the average estimates of certain MongoDB metrics that are commonly tracked and forecasted by Wall Street analysts.
Analysts' assessment points toward 'Revenue- Subscription' reaching $710.67 million. The estimate suggests a change of +24.2% year over year.
The consensus among analysts is that 'Revenue- Services' will reach $21.90 million. The estimate indicates a year-over-year change of +15%.
The average prediction of analysts places 'Revenue- Subscription - Atlas-related' at $552.44 million. The estimate suggests a change of +25.9% year over year.
Analysts expect 'Revenue- Subscription - MongoDB Enterprise Advanced and other' to come in at $159.26 million. The estimate points to a change of +19.4% from the year-ago quarter.
The collective assessment of analysts points to an estimated 'MongoDB Atlas customers' of 68,939 . Compared to the present estimate, the company reported 58,300 in the same quarter last year.
View all Key Company Metrics for MongoDB here>>>
Over the past month, MongoDB shares have recorded returns of +26.5% versus the Zacks S&P 500 composite's +3.7% change. Based on its Zacks Rank #3 (Hold), MDB will likely exhibit a performance that aligns with the overall market in the upcoming period. You can see the complete list of today's Zacks Rank #1 (Strong Buy) stocks here >>>> .
In the latest close session, MongoDB (MDB - Free Report) was up +1.37% at $446.62. The stock's performance was ahead of the S&P 500's daily loss of 0.25%. On the other hand, the Dow registered a loss of 0.02%, and the technology-centric Nasdaq decreased by 0.52%.
Shares of the database platform have appreciated by 35.2% over the course of the past month, outperforming the Computer and Technology sector's gain of 7.57%, and the S&P 500's gain of 4.34%.
The investment community will be closely monitoring the performance of MongoDB in its forthcoming earnings report. The company is scheduled to release its earnings on September 1, 2026. The company's upcoming EPS is projected at $1.6, signifying a 60.00% increase compared to the same quarter of the previous year. At the same time, our most recent consensus estimate is projecting a revenue of $733.61 million, reflecting a 24.05% rise from the equivalent quarter last year.
In terms of the entire fiscal year, the Zacks Consensus Estimates predict earnings of $6.07 per share and a revenue of $2.95 billion, indicating changes of +22.13% and +19.58%, respectively, from the former year.
Investors might also notice recent changes to analyst estimates for MongoDB. These revisions help to show the ever-changing nature of near-term business trends. As a result, we can interpret positive estimate revisions as a good sign for the business outlook.
Our research shows that these estimate changes are directly correlated with near-term stock prices. We developed the Zacks Rank to capitalize on this phenomenon. Our system takes these estimate changes into account and delivers a clear, actionable rating model.
The Zacks Rank system, which ranges from #1 (Strong Buy) to #5 (Strong Sell), has an impressive outside-audited track record of outperformance, with #1 stocks generating an average annual return of +25% since 1988. Over the past month, there's been a 42.31% fall in the Zacks Consensus EPS estimate. MongoDB is holding a Zacks Rank of #3 (Hold) right now.
Investors should also note MongoDB's current valuation metrics, including its Forward P/E ratio of 72.64. This expresses a premium compared to the average Forward P/E of 21.62 of its industry.
It's also important to note that MDB currently trades at a PEG ratio of 5.95. Comparable to the widely accepted P/E ratio, the PEG ratio also accounts for the company's projected earnings growth. As of the close of trade yesterday, the Internet - Software industry held an average PEG ratio of 1.06.
The Internet - Software industry is part of the Computer and Technology sector. This industry, currently bearing a Zacks Industry Rank of 77, finds itself in the top 32% echelons of all 250+ industries.
The Zacks Industry Rank gauges the strength of our individual industry groups by measuring the average Zacks Rank of the individual stocks within the groups. Our research shows that the top 50% rated industries outperform the bottom half by a factor of 2 to 1.
You can find more information on all of these metrics, and much more, on Zacks.com.
MongoDB spustila spravovaný MCP Server pro Atlas, který propojuje AI nástroje jako Claude Code, Codex, Grok Build a Devin s živými provozními daty. Nově přidala také schopnosti Voyage AI včetně Automated Embeddings v Atlasu, Atlas Embedding and Reranking API a voyage-code-4.
A new Managed MCP Server connects Claude Code, Codex, Grok Build, and Devin to MongoDB Atlas, giving coding agents direct access to live operational data
, /PRNewswire/ -- MongoDB, Inc. (NASDAQ: MDB) today announced at MongoDB.local Build Fest that MongoDB's intelligent data platform is now available natively inside the AI tools developers use to build applications. Available today, MongoDB Atlas Managed MCP Server is a fully hosted way to connect agents to Atlas without running additional infrastructure. Builders can now easily add MongoDB Atlas to Claude Code, Codex, Grok Build and Devin. Everything announced today is available now, and teams can get started with Atlas for free at mongodb.com/atlas.
"The AI tools teams reach for keep changing, so our approach is to make sure MongoDB is present in all of them, whether a team is working in Claude or Codex, or running an agent in production. More of that building is now done by agents, and neither the agent nor the developer has to stop and set up a connection, so applications come together faster," said Pablo Stern-Plaza, Chief Product Officer, AI and Emerging Products, MongoDB.
With the new connectors, MongoDB is available natively across the tools where software is built. Ask questions of data in plain language in ChatGPT, Claude, and Grok. Query, inspect, and update data in MongoDB as work happens, with coding agents like Claude Code, Codex, Grok Build and Devin. And builders can also see live MongoDB data while generating an app in an IDE like Cursor.
Getting connected with these tools takes only a few clicks. Find the MongoDB connector in the tool's marketplace and authorize it, with no connection string to paste and no infrastructure to configure. Once connected, the tool can list collections and indexes, query and aggregate data, and inspect schemas—and with the right permissions—can also create collections or manage indexes.
Introducing the MongoDB Atlas Managed MCP Server
Running an agent in production means connecting it to real operational data and agent memory, and until now, teams had to build and maintain that connection themselves. MongoDB's MCP server already sees more than 30,000 installs a week. Starting today, the MongoDB Atlas Managed MCP Server is remote and fully hosted, running as a service inside Atlas, so there is nothing for a team to install, operate, or upgrade. Teams connect using the same credentials and access controls they already use with Atlas, so administrators can govern how agents access operational data from one place.
"Developers want their AI tools to connect with the context and systems they already rely on," said Vibhor Chhabra, Product Lead for ChatGPT Ecosystem at OpenAI. "MongoDB's plugin in ChatGPT makes it easier to access and work with live application data, helping developers move faster while staying grounded in the context of their applications."
"We're in the golden age of software engineering. The scope of what one engineer can build has exploded, and the unlock is agents working with real context," said Russell Kaplan, President at Cognition, the company behind Devin. "By connecting Devin to MongoDB Atlas, engineers can hand off well-scoped tasks knowing Devin is working from live application data, not stale assumptions, and spend their own time on the harder problems."
MongoDB also announced at Build Fest new capabilities that bring its benchmark-leading Voyage AI retrieval models into the operational database, including Automated Embeddings in MongoDB Atlas powered by Voyage AI, the Atlas Embedding and Reranking API, and voyage-code-4.
Get started with these new capabilities today for free at mongodb.com/atlas.
About MongoDB
Headquartered in New York, MongoDB's mission is to empower innovators to create, transform, and disrupt industries with software. MongoDB's unified database platform was built to power the next generation of applications, and MongoDB is the most widely available, globally distributed database on the market. With integrated capabilities for operational data, search, real-time analytics, and AI-powered data retrieval, MongoDB helps organizations everywhere move faster, innovate more efficiently, and simplify complex architectures. Millions of developers and more than 67,000 customers across industries—including ~75% of the Fortune 100—rely on MongoDB for their most important applications. To learn more, visit mongodb.com.
Forward-Looking Statements
This press release includes certain "forward-looking statements" within the meaning of Section 27A of the Securities Act of 1933, as amended, or the Securities Act, and Section 21E of the Securities Exchange Act of 1934, as amended, including new capabilities announced at MongoDB.local Build Fest 2026. These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies and prospects, which are based on the information currently available to us and on assumptions we have made. Actual results may differ materially from those described in the forward-looking statements and are subject to a variety of assumptions, uncertainties, risks and factors that are beyond our control including those risks detailed under the caption "Risk Factors" and elsewhere in our Securities and Exchange Commission filings and reports. Except as required by law, we undertake no duty or obligation to update any forward-looking statements contained in this release as a result of new information, future events, changes in expectations or otherwise.
MongoDB v poslední obchodní seanci klesl o 4,95 % na 312,33 USD a za měsíc odepsal 1,25 %. Investoři čekají na výsledky, kde trh počítá s EPS 1,6 USD a tržbami 733,61 mil. USD.
MongoDB (MDB - Free Report) closed the most recent trading day at $312.33, moving -4.95% from the previous trading session. The stock's performance was behind the S&P 500's daily loss of 1.01%. Meanwhile, the Dow lost 0.77%, and the Nasdaq, a tech-heavy index, lost 1.4%.
The database platform's stock has dropped by 1.25% in the past month, exceeding the Computer and Technology sector's loss of 3.73% and lagging the S&P 500's gain of 0.32%.
Investors will be eagerly watching for the performance of MongoDB in its upcoming earnings disclosure. The company is expected to report EPS of $1.6, up 60% from the prior-year quarter. Simultaneously, our latest consensus estimate expects the revenue to be $733.61 million, showing a 24.05% escalation compared to the year-ago quarter.
For the full year, the Zacks Consensus Estimates are projecting earnings of $6.07 per share and revenue of $2.94 billion, which would represent changes of +22.13% and +19.5%, respectively, from the prior year.
Investors should also pay attention to any latest changes in analyst estimates for MongoDB. Recent revisions tend to reflect the latest near-term business trends. As a result, we can interpret positive estimate revisions as a good sign for the business outlook.
Our research shows that these estimate changes are directly correlated with near-term stock prices. To utilize this, we have created the Zacks Rank, a proprietary model that integrates these estimate changes and provides a functional rating system.
The Zacks Rank system, running from #1 (Strong Buy) to #5 (Strong Sell), holds an admirable track record of superior performance, independently audited, with #1 stocks contributing an average annual return of +25% since 1988. Within the past 30 days, our consensus EPS projection remained stagnant. Currently, MongoDB is carrying a Zacks Rank of #3 (Hold).
In terms of valuation, MongoDB is currently trading at a Forward P/E ratio of 54.11. This expresses a premium compared to the average Forward P/E of 20.37 of its industry.
Investors should also note that MDB has a PEG ratio of 4.44 right now. Comparable to the widely accepted P/E ratio, the PEG ratio also accounts for the company's projected earnings growth. By the end of yesterday's trading, the Internet - Software industry had an average PEG ratio of 1.11.
The Internet - Software industry is part of the Computer and Technology sector. This industry currently has a Zacks Industry Rank of 86, which puts it in the top 35% of all 250+ industries.
The Zacks Industry Rank gauges the strength of our industry groups by measuring the average Zacks Rank of the individual stocks within the groups. Our research shows that the top 50% rated industries outperform the bottom half by a factor of 2 to 1.
Make sure to utilize Zacks.com to follow all of these stock-moving metrics, and more, in the coming trading sessions.
MongoDB uvedla nové AI nástroje pro přesnější vyhledávání dat a zpřístupnila Search a Vector Search pro MongoDB Enterprise Advanced, Community Edition, private cloud i lokální nasazení. Native Reranking má zvýšit kvalitu vyhledávání až o 30 %.
New Voyage AI capabilities and Search for on-premises and private cloud let enterprises build accurate, compliant AI applications to run anywhere without rewriting their applications and relying on bolt-on tools
, /PRNewswire/ -- MongoDB, Inc. (NASDAQ: MDB) today announced new capabilities at MongoDB.local Bengaluru that address the two reasons enterprise AI projects routinely stall before production: retrieval that isn't accurate enough to trust and infrastructure that can't meet compliance requirements. voyage-context-4, Hybrid Search, and Native Reranking work together to improve retrieval accuracy, with Native Reranking alone improving retrieval quality by up to 30%*. The capabilities are powered by Voyage AI models that outperform Google and Cohere on the public Retrieval Embedding Benchmark leaderboard. Search and Vector Search are now generally available for MongoDB Enterprise Advanced and Community Edition, bringing the same retrieval capabilities Atlas customers rely on to on-premises, private cloud, and local environments where regulated enterprises and startups operate. Together, these capabilities give enterprises and builders a production-ready retrieval stack that is accurate, compliant, and deployable wherever their data lives.
"The biggest barrier to enterprise AI in production and at scale isn't the LLM. It's memory, retrieval, accuracy, and compliance. Most enterprises aren't blocked by ambition. They're held back by infrastructure that wasn't designed to provide AI with trusted access to enterprise data. Bolting on more systems to solve those problems only creates more vendors, more latency, and more points of failure," said Ben Cefalo, Chief Product Officer, Core Products, MongoDB. "Whether you're running in the cloud, private cloud, or behind a firewall, MongoDB gives you the same production-grade retrieval capabilities wherever your data lives."
Voyage AI: Accuracy begins with top-ranked embedding models
Accuracy is the first bar AI has to clear for production. The second is ensuring AI works from current data, not outdated data sitting in a separate search system. Today, MongoDB launched three new capabilities, built into the database, that deliver more accurate retrieval and keep applications working from current data.
Native Reranking in MongoDB Atlas, now in public preview, is powered by Voyage AI and delivers up to a 30% boost in retrieval quality directly inside the database, eliminating a leading cause of AI project failure. It works on top of existing search results, with no external APIs, keys, or round-trips to manage. Voyage Context 4, now generally available, is a new embedding model built for long documents. It processes long documents in full context rather than isolated chunks, preserving meaning across complex enterprise content for better retrieval accuracy. It drops into existing RAG pipelines without re-architecting. Hybrid Search in MongoDB, now generally available, combines full-text and vector search in a single query inside the operational database, delivering precision retrieval without separate systems or complex query logic. Because embeddings stay up to date automatically, agents retrieve from the current state of the data rather than a stale copy. Emergent Labs is an AI-native app development platform and one of the fastest growing startups in the world. The company first tested its platform on PostgreSQL, where agents repeatedly got stuck in schema migration loops every time users refined their ideas. On MongoDB Atlas, agents create and modify data structures freely as applications evolve, and because search and embeddings live in the same database as that constantly changing data, retrieval keeps up with it.
"Our agents write code, modify data structures, and act on what they read back millions of times a day. If retrieval returns something stale or wrong, the agent builds on it, and the error compounds. MongoDB gives us the retrieval accuracy to keep agents working from the current state of the data, and that's what lets us run two million applications at scale," said Mukund Jha, CEO of Emergent Labs.
Run AI anywhere without compromising on accuracy or increasing risk
Retrieval accuracy is only half the problem enterprises face. The other half is whether they're allowed to run it where their data must reside, and for enterprises in regulated industries, the answer is rarely the public cloud. Data residency mandates, sovereignty rules, and compliance frameworks don't bend for innovation timelines, yet the most capable AI tooling has been built cloud-first, leaving regulated enterprises to choose between compliance and capability.
Today, MongoDB Search and Vector Search are now generally available as an add-on for MongoDB Enterprise Advanced, bringing the same retrieval capabilities MongoDB Atlas customers have been building in on-premises, private cloud, and hybrid environments, with the same platform, API, and technical skills regardless of where the workload runs. Ahead of this release, more than 20 of the world's largest banks and financial institutions have been evaluating Search for Enterprise Advanced, drawn by the same thing: AI-ready retrieval that runs inside the infrastructure they control.
Search and Vector Search are now generally available for MongoDB Community Edition, enabling builders to implement AI retrieval locally at no cost. A startup can prototype on a laptop with full-text search, vector search, and hybrid search in one single system, then move to Atlas or Enterprise Advanced when it's ready to scale, without re-architecting or switching databases.
Investing in India for the long term
As part of MongoDB.local Bengaluru, the company also announced plans to upskill two million Indian builders by 2030. MongoDB is expanding its MongoDB for Academia program through partnerships with the All India Council for Technical Education, HCL GUVI, and the ICT Academy of Kerala. Since 2023, the program has reached more than 650,000 students.
MongoDB also launched Bengaluru to the Bay, a startup challenge that gives early-stage AI founders a path from India's builder ecosystem to San Francisco's AI community during SF Tech Week experience. $50,000 in MongoDB Atlas credits, travel, and go-to-market opportunities included.
What's new at MongoDB.local Bengaluru 2026
voyage-context-4 (Generally available): Next-generation contextualized embeddings with document-level context and auto-chunking; a drop-in upgrade for existing retrieval-augmented generation (RAG) pipelines. Native Reranking in MongoDB Atlas (Public Preview): Reranking runs inside the aggregation pipeline - no external APIs, no round-trips - and delivers up to a 30% boost in retrieval quality directly inside the database. Hybrid Search (Generally available): More accurate retrieval by combining full-text precision and vector-based semantic understanding in a single query on live operational data. Search and Vector Search for MongoDB Enterprise Advanced (Generally available): Production AI behind your firewall, under your compliance framework, with full parity to MongoDB Atlas capabilities. Search and Vector Search in MongoDB Community Edition (Generally available): Full-text, vector, and hybrid retrieval in self-managed environments, at zero cost to start. MongoDB Atlas Stream Processing: Apache Iceberg Support (Generally available): MongoDB Atlas now supports Apache Iceberg via the new $iceberg aggregation stage in Atlas Stream Processing, enabling any Atlas collection to be continuously synchronized to Iceberg tables on AWS object storage. Gen2 MongoDB Atlas M30+ Dedicated Clusters on AWS (Generally available): Next-generation infrastructure for high-scale production workloads. Asymmetric Search Node deployment for multi-region Atlas clusters: (Generally available): Set Search Node capacity to match each region's actual search traffic and lower total Search Node cost on multi-region clusters by 25–40%+ MongoDB for Academia Expansion: Targeting 2 million builders trained by 2030 through HCL GUVI, ICT Academy of Kerala, and AICTE partnerships. Bengaluru Meets the Bay—startup contest: $50K in MongoDB credits plus travel and VIP access to MongoDB.local San Francisco for winning founders. *Based on Voyage instruction-following rerankers on the MAIR benchmark; improvement measured over first-stage retrieval.
About MongoDB
Headquartered in New York, MongoDB's mission is to empower innovators to create, transform, and disrupt industries with software. MongoDB's unified database platform was built to power the next generation of applications, and MongoDB is the most widely available, globally distributed database on the market. With integrated capabilities for operational data, search, real-time analytics, and AI-powered data retrieval, MongoDB helps organizations everywhere move faster, innovate more efficiently, and simplify complex architectures. Millions of developers and more than 65,200+ customers across industries—including ~75% of the Fortune 100—rely on MongoDB for their most important applications. To learn more, visit mongodb.com.
Forward-Looking Statements
This press release includes certain "forward-looking statements" within the meaning of Section 27A of the Securities Act of 1933, as amended, or the Securities Act, and Section 21E of the Securities Exchange Act of 1934, as amended, including new capabilities announced at MongoDB .local Bengaluru 2026. These forward-looking statements include, but are not limited to, plans, objectives, expectations and intentions and other statements contained in this press release that are not historical facts and statements identified by words such as "anticipate," "believe," "continue," "could," "estimate," "expect," "intend," "may," "plan," "project," "will," "would" or the negative or plural of these words or similar expressions or variations. These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies and prospects, which are based on the information currently available to us and on assumptions we have made. Although we believe that our plans, intentions, expectations, strategies and prospects as reflected in or suggested by those forward-looking statements are reasonable, we can give no assurance that the plans, intentions, expectations or strategies will be attained or achieved. Furthermore, actual results may differ materially from those described in the forward-looking statements and are subject to a variety of assumptions, uncertainties, risks and factors that are beyond our control including, without limitation: our customers renewing their subscriptions with us and expanding their usage of software and related services; global political changes; the effects of the ongoing military conflicts between Russia and Ukraine and Israel and Hamas and recent events in Venezuela on our business and future operating results; economic downturns and/or the effects of rising interest rates, inflation and volatility in the global economy and financial markets on our business and future operating results; our potential failure to meet publicly announced guidance or other expectations about our business and future operating results; reputational harm or other adverse consequences resulting from use of AI and ML in our product offerings and internal operations if they don't produce the desired benefits; our limited operating history; our history of losses; our potential failure to repurchase shares of our common stock at favorable prices, if at all; failure of our platform to satisfy customer demands; the effects of increased competition; our investments in new products and our ability to introduce new features, services or enhancements, including AI and ML; social, ethical and security issues relating to the use of new and evolving technologies, such as artificial intelligence, in our offerings or partnerships; our ability to effectively expand our sales and marketing organization; our ability to continue to build and maintain credibility with the developer community; our ability to add new customers or increase sales to our existing customers; our ability to maintain, protect, enforce and enhance our intellectual property; our ability to continue to increase revenue from our Atlas platform; the effects of social, ethical and regulatory issues relating to the use of new and evolving technologies, such as AI and ML, in our offerings or partnerships; the growth and expansion of the market for database products and our ability to penetrate that market; our ability to maintain the security of our software and adequately address privacy concerns; our ability to manage our growth effectively and successfully recruit and retain additional highly-qualified personnel; our ability to integrate acquisitions and work with our strategic partners effectively; and the price volatility of our common stock. These and other risks and uncertainties are more fully described in our filings with the Securities and Exchange Commission ("SEC"), including under the caption "Risk Factors" in our Annual Report on Form 10-Q for the quarter ended April 30, 2026, filed with the SEC on May 29, 2026. Additional information will be made available in other filings and reports that we may file from time to time with the SEC. Except as required by law, we undertake no duty or obligation to update any forward-looking statements contained in this release as a result of new information, future events, changes in expectations or otherwise.
