🚀 MongoDB highlights clear advantages over Postgres in AI startup adoption, scalability, and integrated database solutions in its 2026 Q2 earnings transcript. Key customer migration stories reveal performance and cost benefits, while strategic education efforts aim to convert Postgres users. 📊
Generated by Dafinchi AI. Source-grounded AI analysis, not investment advice.
Postgres, Postgres adoption
MongoDB, Inc. addresses the topic of Postgres adoption multiple times throughout the earnings transcript, particularly in the context of AI startups, scalability challenges, and competitive positioning of MongoDB Atlas. The discussion reveals MongoDB’s strategic positioning against Postgres as a competitor in the database market and highlights key limitations of Postgres that drive customers toward MongoDB.
MongoDB explicitly contrasts its platform with Postgres, emphasizing that the comparison is often not apples-to-apples when considering modern applications, especially those requiring advanced capabilities such as search, vector search, embeddings, and streaming:
"Comparing MongoDB, Inc. to another database like Postgres is not an apples-to-apples comparison... The choice for this application, not between MongoDB, Inc. or Postgres, is between MongoDB, Inc. or Postgres plus other offerings like Pinecone, Elastic, and Cohere, for embeddings. MongoDB, Inc.'s complete solution allows developers to spend less time stitching together and maintaining a patchwork of disparate systems and more time building differentiated functionality that drives the business forward."
This framing positions MongoDB Atlas as a more integrated, scalable, and complete platform compared to Postgres, which often requires additional complementary tools to fill feature gaps.
Agibank case: A Brazilian neobank migrated from Postgres to MongoDB Atlas due to Postgres’ "inflexibility and task execution latency" issues and missing features such as sophisticated secondary indexes and full-text search. The result was a nearly 5x performance improvement and 90% cost reduction for Agibank.
Large automaker case: An automotive customer moved from Postgres due to “scalability and flexibility limits,” especially handling a “modern schema to handle both structured and unstructured data” at scale with millions of connected vehicles.
These examples highlight real-world challenges customers face with Postgres on performance, flexibility, and scaling, which MongoDB claims to address effectively.
In the Q&A section, when asked why Postgres is often heard about as the choice among AI startups despite limitations, CEO Dev Ittycheria offers important insights:
Many AI startup founders initially choose Postgres because it is familiar and well-known.
As these startups scale, they “run into real scaling challenges with Postgres,” especially related to JSONB handling, which incurs “enormous performance overheads” due to off-road storage for larger documents.
MongoDB offers a platform better suited to handling “structured, semi-structured, unstructured data,” with scalable performance critical for growing AI applications.
MongoDB is investing heavily in developer education and community engagement (e.g., Bay Area hackathons) to encourage startups to reconsider early database decisions and transition from Postgres where needed.
“What we're hearing clearly from the startup communities Postgres, in many cases, is not scaling for them. And they're now coming to us...”
MongoDB leverages Postgres’s perceived limitations to underscore its own advantages:
Architectural advantages: Better JSON support and integration of advanced capabilities like vector search—key for AI applications.
Simplification & integration: MongoDB’s platform reduces the need to combine multiple disparate tools that Postgres users often rely on (e.g., Pinecone, Elastic).
Enterprise readiness: MongoDB points out its strong adoption in demanding environments (e.g., large enterprises, telecoms, neobanks), which rely on its scalability and flexible schema.
Emphasizes “durable architectural advantage in terms of JSON support” and being a “key component of the AI infrastructure stack.”
MongoDB, Inc. uses the discussion around Postgres adoption primarily to highlight common pain points customers and startups encounter as they grow—namely, Postgres’s limited scalability, JSON handling inefficiency, and the need to integrate multiple systems to address modern data challenges. MongoDB positions Atlas as a more flexible, performant, and integrated platform better aligned with the needs of data-intensive, AI-driven applications.
The company is actively investing in educating AI startup founders who initially choose Postgres out of familiarity, aiming to convert them as their scaling needs expose Postgres’s limitations. Furthermore, MongoDB emphasizes actual customer migration stories as concrete examples of these challenges and the benefits of switching.
"Comparing MongoDB, Inc. to another database like Postgres is not an apples-to-apples comparison... MongoDB, Inc.'s complete solution allows developers to spend less time stitching together and maintaining a patchwork of disparate systems and more time building differentiated functionality that drives the business forward."
"Agibank was constantly updating the database and manually scaling infrastructure. Which is both time-consuming and error-prone... With Atlas, Agibank gained a resilient flexible system that handles rising demand and supports new services delivering nearly five times better performance and 90% lower cost, all with no outages."
"We consistently hear from customers that when teams try to scale from five prototypes built on relational back ends, to enterprise-grade deployments, these platforms quickly hit limits in flexibility, scalability, and performance."
“What we're hearing clearly from the startup communities Postgres, in many cases, is not scaling for them. And they're now coming to us.”
“...when you add a JSON when you use JSONB on Postgres, a two-kilobyte document or bigger starts really creating performance problems because Postgres has to do something called off-road storage, which creates enormous performance overheads.”
This discussion is particularly valuable for investors and market watchers as it illuminates how MongoDB views its competitive moat and go-to-market strategy in the rapidly evolving AI and cloud-native application space, where database scalability and feature integration are key.
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