MongoDB, Inc. Q2 2027 Earnings Call Summary

MongoDB, Inc. Q2 2027 Earnings Call Summary Moby Intelligence Wed, September 2, 2026 at 2:30 PM GMT+2 3 min read NVDA MDB MongoDB, Inc. Q2 2027 Earnings Call Summary - Moby Strategic Performance Drivers Our analysts just identified a stock with the potential to be the next Nvidia.

Tell us how you invest and we'll show you why it's our #1 pick. Tap here. Total revenue growth accelerated to 30%, the highest rate since fiscal 2024, driven by broad-based strength across both Atlas and Enterprise Advanced (EA).

Management attributes the 36% growth in EA to the strategic launch of search and vector search capabilities, allowing regulated industries to build AI agents within governed, self-managed environments. The 'run-anywhere' strategy is serving as a key differentiator, with customers increasingly adopting hybrid deployments across multiple clouds and on-premises to satisfy regulatory and operational resilience requirements. Atlas momentum is being fueled by large enterprise customers and 'AI natives' who migrate to MongoDB after hitting scaling limits with legacy relational databases like Postgres.

The acquisition of Voyage is serving as a significant top-of-funnel driver, with a large majority of new Voyage customers having no prior relationship with MongoDB before adopting its embedding models. Management emphasizes that EA growth is not cannibalizing Atlas; instead, the two products often coexist within the same customer estate to provide a 'speed layer' for real-time digital channels. Outlook and Strategic Assumptions Full-year Atlas growth guidance was raised to approximately 27%, reflecting a 300 basis point increase based on consistent consumption trends and upmarket momentum.

EA and other revenue expectations were upgraded to 11% growth for the full year, marking the first projected double-digit growth for this segment in three years. The Q3 and Q4 outlook assumes a prudent stance on consumption dynamics, accounting for potential holiday-related impacts and the inherent difficulty in predicting multi-year deal timing. Management expects to expand operating margins by approximately 250 basis points for the full year while simultaneously increasing investments in quota-carrying headcount and AI R&D.

The company is targeting 'Rule of 44' performance at the high end of its fiscal 2027 guidance, balancing 23% revenue growth with a 21% operating margin. Operational Highlights and Risk Factors Added a record 2. 9 thousand net new customers in the quarter, bringing the total customer count to 70.

6 thousand. Net ARR expansion rate increased to 122%, up from 119% in the year-ago period, driven by multi-product penetration among $100 thousand plus ARR customers. Voyage customer count nearly doubled quarter-over-quarter for the second consecutive time, signaling strong early adoption of AI embedding capabilities.

Management noted that while AI signals are increasing, the direct revenue contribution from AI workloads currently remains a small but growing portion of the overall business. Story Continues Q&A Insights Factors driving the 300 basis point raise in Atlas guidance One stock. Nvidia-level potential.

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Management cited five straight quarters of approximately 29% growth and a record $127 million in net new Atlas dollars as the primary confidence drivers. Strength is particularly concentrated in North American large enterprises and increasing multi-product adoption. Potential for Enterprise Advanced (EA) to cannibalize Atlas revenue CEO CJ Desai clarified that EA growth is 'not coming at the expense of Atlas' but is meeting specific customer needs for data sovereignty and operational resilience.

EA often serves as a gateway for new Atlas conversations within the same large organizations, particularly in public sector and banking. Conversion of Voyage-only customers to the broader Atlas platform Many new Voyage customers are AI natives with no prior MongoDB relationship, often referred by coding agents such as Claude and Codex. Management views this as a 'massive opportunity' for future cross-selling, though they admit it is still early in the integration lifecycle.

Specific AI use cases driving production workload volume Traction is highest in customer-facing agents where scale and performance are critical, such as wealth management chatbots and real-time semantic search for media. Frontier Labs are increasingly using Atlas as a memory layer for inference workloads, citing 10x faster reads compared to Postgres.