MongoDB acquires Voyage AI

This blog post outlines the new roadmap: https://www.mongodb.com/blog/post/redefining-database-ai-why...

We use it a lot for a specific use-case and it works great. Mongo has come a long long way since the release over a decade ago, and if you keep it in Majority Read and Write, it's very reliable.

Also, on some things, it allows us to pivot much faster. And now with the help of LLMs, writing "Aggregation Pipelines" are very fast.

Pretty sure they achieved fiscal nirvana by exploiting enterprise brain rot. You hook em, they accumulate tech debt for years, all their devs leave, now they can't move away & you can start increasing prices. Eventually the empty husk will topple over but that's still years away.

that’s what I thought, but every single candidate I interviewed mentioned MongoDB as their recent reference document database, I asked the last candidate if they were self-hosting, the answer is no, they used MongoDB cloud.

Everyone you know put a dollar in donation basket while moving off. Mongo collected all and brought Voyage AI

Because they are web-scale obviously.

$2.3B in cash as of last quarter

Well, it's referred to as a cash-and-stock deal but I can't find any more detail about how much is stock:

https://seekingalpha.com/news/4412466-mongodb-acquires-voyag...

I will copy and paste a comment I wrote here previously:

"MongoDB ships with horizontal sharding out-of-the-box, has idiomatic and well-maintained drivers for pretty much every language you could want (no C library re-use), is reasonably vendor-neutral and can be run locally, and the data modeling it encourages is both preferential for some people as well as pushes users to avoid patterns that don't scale very well with other models. Whether these things are important to you is a different question, but there is a lot to like that alternatives may not have answers for. If you currently or plan on spending > 10K per month on your database, I think MongoDB is one of the strongest choices out there."

I have also run Postgres at very large scale. Postgres' JSONB has some serious performance drawbacks that don't matter if you don't plan on spending a lot of money to run your database, but MongoDB does solve those problems. This new documentdb extension from Microsoft may solve some of the pain, but this is some very rough code if you browse around, and Postgres extensions are quite painful to use over the long term.

The reality is that it is not possible to run vanilla Postgres at scale. It's possible to fix its issues with third party solutions or cobbling together your own setup, but it takes a lot of effort and knowledge to ensure you've done things correctly. It's true that many people never reach that scale, but if you do, you're willing to spend a lot of money on something that works well.

MongoDB is not the same as Postgres and jsonb.

also, I'd challenge your thinking - ultimately the goal is to solve problems. you don't necessarily need SQL, or relations for that matter. that being said, naively modeling your stuff in mongodb (or other things like dynamodb) will cause you severe pain...

what's also true, which people forget, is naively modeling your stuff with a relational database will also cause you pain. as they sometimes say, normalize until it hurts, and then denormalize to scale and make it work

the amount of places I've seen that skip the second part and have extremely normalized databases makes me cringe. it's like people think joins are free...

I have seen a few rather large, production mongodb deployments. I don't understand how so many people chose it as their basis of their applications. There are a not-negligible amount of mongodb deployments I have seen that basically treat mongodb as a memory dump, where they then scan from some key and hope for the best. I have never seen a mongodb solution where I thought that it was better than if they just chose any sql server.

SQL or rather just some schema based database has a ton of advantages. Besides speed, there is a huge benefit for developers to be able to look at a schema and see how the relationships in the data work. Mongodb usually involves looking at a de facto schema, but with fewer guarantees on types relations or existence, then trawling code for how its used.

We use their atlas offering. It’s a bit pricey but we are very happy with it. It’s got a bunch of stuff integrated - vectors, json (obviously), search and charting along with excellent support for drivers and very nice out of the box monitoring.

Now I could possible spend a bunch of time and do the same thing with open source dbs - but why? I have a small team and stuff to deliver. Atlas allows me to do it fast.

If you can learn Mongo you can learn SQL and 'back end architecture' let's be honest the basics are hardly difficult no matter what tool you're using.

Just because Postgres is good doesn't mean other things can't also be good (and better for some use cases).

Enterprise sales

Mongo is Firestore for entrprise

Um because it must be worth 2 billion if this acquisition is worth $220 million. I know there’s rules about discussion quality on this site, so I guess we can’t question that.

Can you share more details about the conditions under which it is slow in recent versions? We moved from 3.x to 7 for our main database and after adding a few indexes we were missing we have seen at least an order of magnitude speed up.

I think 8 was a release purely focused on performance, with some big improvements. Comparing 3.4 is kinda unfair.. You were fast with the tradeoff of half your data missing half the time

mongodb had consistency issues before v5 if I recall, so take that for what it's worth.

You don't hear the big AI providers talk about embeddings much, but I have to believe in the long run that companies building SOTA foundational LLMs are going to ultimately have the best embedding models.

Unless you can get to a point where you can make these models small enough that basically sit in the DB layer of an application...

>their embedding model is not SOTA, does not even outperform the open ones out there, and reranking is a dead end in 2025.

Are you referring to the MTEB leaderboard? It's widely believed many of those test datasets are considered during the training of most open-source text embedding models, hence why you see novel + private benchmarks discussed in many launch blogs that don't exclusively refer to MTEB. There are problems there, and it would be great to see more folks in the search benchmark dataset production space like what Marqo AI has done in recent months.

Also what makes you say reranking is dead? Mongo doesn't provide it out of the box but many other search providers like ES, Pinecone, Opensearch do so it must provide some value to their customers? Maybe you're saying it's overrated in terms of how many apps actually need it?

disclosure: I work on vector search at Mongo

Interesting take. Have you benchmarked models on your own data? Cause at this point everything is contaminated so I find it impossible to tell what proper sota is. Also - most folks still just use openai. Last time I checked, reranking always performs better than pure vector search. And to my knowledge it's still the superior fusion method for keyword and vector results.

If I had to guess they might see embedding models become small and optimised enough to the point that they can pull them into the DB layer as a feature instead of being something devs need to actively think about and build into their app.

Or it could just be an expansion to their cloud offering. In a lot of cases embedding models just need to be 'good enough' and cheap and/or convenient is a winning GTM approach.

and the mongo blog post for how it will be used: https://www.mongodb.com/blog/post/redefining-database-ai-why...