MongoDB raises FY27 adj EPS guidance to $6.39-$6.58, boosts GAAP outlook
- MongoDB raises FY27 adjusted EPS guidance to $6.39-$6.58, beating the $6.11 consensus estimate
- FY27 sales guidance upgraded to $2.990 billion-$3.030 billion, surpassing the $2.953 billion estimate
- GAAP EPS guidance for FY27 is lifted from $0.15-$0.39 to $0.53-$0.77
- The revisions reflect strong operational momentum and expected margin expansion

*this image is generated using AI for illustrative purposes only.
MongoDB (NASDAQ: MDB) raised its full-year financial guidance for fiscal year 2027, lifting both adjusted and GAAP earnings per share outlooks above analyst expectations.
The database technology company increased its FY27 adjusted EPS guidance range from $5.95-$6.14 to $6.39-$6.58. This new midpoint exceeds the consensus analyst estimate of $6.11.
Simultaneously, MongoDB upgraded its FY27 sales guidance from $2.920 billion-$2.960 billion to $2.990 billion-$3.030 billion, surpassing the estimated $2.953 billion.
In a separate update, the company also raised its FY27 GAAP EPS guidance from $0.15-$0.39 to $0.53-$0.77.
What the Numbers Show
The upward revision in both top-line revenue and bottom-line profitability indicates strong operational momentum heading into the next fiscal period. The adjusted EPS guidance now sits comfortably above the street estimate, suggesting management sees sustained margin expansion or cost efficiencies alongside the revenue growth.
The significant lift in GAAP EPS guidance, which more than doubles the previous midpoint, aligns with the improved adjusted metrics, reinforcing confidence in the company's underlying earnings power excluding non-GAAP adjustments.
How will MongoDB's upgraded guidance influence its valuation multiples relative to other cloud database competitors like Snowflake and Databricks?
What specific operational efficiencies or margin expansion strategies are driving the significant increase in GAAP EPS, and are these sustainable long-term?
Could the strong FY27 outlook signal an acceleration in enterprise adoption of AI-native database solutions, and how might this impact future R&D spending?





























