2007
CJ Desai says MongoDB was created in 2007 and has been a disruptive force in the decades-old database market.
“MongoDB has been a truly disruptive force with being created in 2007, so it's been only eighteen-ish years.”
Topic
MongoDB
18 facts from 3 episodes, in date order. These are what speakers said on the shows, each linked to the moment it was said. They aren't independently verified.
2007
The first code commit to MongoDB was made in October 2007.
“in October of 2007 is when the first commit was made to MongoDB”
2020
In 2020 MongoDB noticed customers running their own Apache Lucene servers beside their clusters for keyword search.
“So in 2020, we noticed that a common use case for MongoDB was for people to stand up their own Apache Lucene servers co-located with wherever their their MongoDB clusters might be”
2025
MongoDB acquired Voyage AI, an embedding model company, in 2025.
“we purchased Voyage in 2025, which created this better together story”
2025
Host Nathan put the price of MongoDB's Voyage AI acquisition at about $220 million.
“the price, at even at 220 million, which is, I'm old enough to remember when that was serious money”
2026
Pete Johnson said MongoDB's revenue for fiscal 2026 was about $2.5 billion.
“we're about 2.5 billion is what we ended our revenue. That was our revenue number for our fiscal twenty six”
2026
MongoDB announced two aggregation pipeline stages, rank fusion and score fusion, that merge keyword and vector search results in a single query.
“we just announced a a pair of features, one's called Rake Fusion and one's called Score Fusion”
2026
Pete Johnson said Voyage released version three of contextualized chunking last summer and version four in the six weeks before the recording.
“we released version three of that last summer. We just released four of that in the last six weeks”
Undated
Brad Gerstner said it was famously hard for MongoDB's new CEO to complete the company's last private financing round at a billion-dollar valuation.
“when David Chariot took over, you know, Mongo, I mean, it was famously hard for him to get that last private round of financing done at a billion dollars.”
Undated
CJ Desai says the CTO of a bank in London told him the bank had built 300 applications on MongoDB.
“And finally, the CTO tells me in London, 300. I said, Wow, okay, that's great, 300 applications built on MongoDB.”
Undated
CJ Desai says the bank's total application count was 9,000, so its 300 MongoDB applications were a small share.
“What's the denominator? I understand the numerator is 300. He said, 9,000. I said, 9,000? That's a great opportunity for MongoDB.”
Undated
CJ Desai says MongoDB's previous CEO, Dave, told him that no challenger database had passed $1 billion in revenue before MongoDB passed $2 billion.
“Nobody has passed billion, and definitely nobody has passed 2 billion, and MongoDB did that, so there's something special there.”
Undated
CJ Desai says a European retailer he met at NRF in New York had failed ERP implementations and decided to build its own system on MongoDB.
“they tried a bunch of systems of record for e r p, expensive, failed implementations, lots, lots of issues on supply chain, all the way to financials”
Undated
CJ Desai says that when MongoDB published its Q3 results he was repeatedly asked whether growth came from AI and answered that it did not.
“MongoDB published our, uh, Q three results. I got asked this question over and over again, is this because of A I? And I said, absolutely not.”
Undated
CJ Desai says hundreds of AI-native companies build on MongoDB but that they are not what drove its core growth.
“Yes, we have A I native companies building on MongoDB, we have hundreds of them, but that's not this.”
Undated
MongoDB stores documents on disk in BSON, a binary form of JSON, which Pete Johnson says keeps one format from disk to client and makes it fast.
“we're storing it on disk in a binary form of JSON we call BSON, and that's part of why we're so fast”
Undated
MongoDB's auto embeddings feature automatically re-embeds a document's chosen field whenever it changes and updates the stored vector.
“Anytime an existing document with that attribute and it changes, we will automatically take that new, let's say it's text, put it through the embedding model, update the vector in the document”
Undated
Pete Johnson said that, depending on the use case, a re-ranker can improve retrieval quality by about five to ten percent.
“you can get like a five to 10% boost at retrieval quality just by using a re-ranker on top of whatever your embedding model of choice is”
Episodes