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So we never actually did like a kind of company wide rift, we did do some surgical risks, say like we had too many recruiters, given our growth was slowing down. And then we may have had some excess sales capacity in regions where we didn't see that the market was as big as we originally thought, but we've not done a company wide rift.
Now, when we went public, we were trailing 100 million in revenue and a negative 40% operating margins. And today, we're about a $1.5 billion revenue run rate business with 12% operating margin.
One of the hot things in our space is something called vector databases. And vectors, essentially a way for people to prevent hallucinations based on data coming from LLMs, and as also marry private data with public data, because not everyone wants to put all their data and say open AI or any other LLM out there. And so we just announced a vector capability, unlike other people who are using point solutions, one of the challenges in our industry is, there's almost like a single function database
What we've seen is almost 1500 customers since that announcement build AI applications on top of MongoDB. And we think AI is going to be a big driver for our business one because it'll increase developer productivity. You know, you could argue the statistics, say anywhere from 20 to 50% with code generation tools and automated testing tools.
one of the most attractive things about MongoDB is that the speed of development is so much faster, people can build new features, add new capabilities, because developers spend about 80% of the time working with data, if you think about any application, one of the biggest problems that developers can figure out is when to present the right data to the right audience at the right time, with the right security constraints, so on and so forth. And so when you can make it very easy to work with dat
we are the only ISV that's on the management consoles of AWS, Azure, and GCP today. And we have incentives for customers to apply their credits towards an MongoDB, or what we call Atlas or cloud service. And then their salespeople also get compensated for selling Atlas.
we had a much more restrictive license. So the hyperscalers couldn't take a free version of our technology with us. And so when they saw the popularity of MongoDB, they came out with their own document databases, but they're built on a very different architecture, in fact, built on a relational architecture. So there's severe feature and performance trade offs. And so our win rates against what we call the clones are very high.
we had amazing product market fit. And, and people just genuinely love the way MongoDB was designed and, and essentially, it was incredibly popular.
we decided to build a cloud service. And we were actually, probably the first, you know, you know, company to build a well known cloud service that ran across all the hyperscalers.
we first had to prove that we were more than a toy, because there was a lot of excitement of MongoDB, but could would people really trust us for mission critical workloads? We over time, we built a tons of features, including the highest form of data guarantees, like asset transaction support, and gave people confidence to do that.
the key insight there was to organize data and documents versus tables. And it's much more easy to organize data that way. It's much more aligned to a developers thinking code, it's very easy to add and change features and entries into the data model, and also is designed to be highly distributed. So you can really serve the most demanding and most sophisticated requirements.
we had today have over 40,000 customers. We're about a $1.5 billion revenue company. And our customers range from almost every bank on Wall Street, every large telecom company, to startups you never heard of. In fact, we have tons of startups, including many AI startups who are building their applications on top of MongoDB.