Resilience Universe with Sanjay Poonen | Chirantan “CJ” Desai

Great to see you. I've known this wonderful man for many years from our years at Symantec, and I've followed his career. He's done incredibly well through a variety of companies, but most prominently ServiceNow and MongoDB. Congratulations. And you've been at MongoDB now how long, CJ?

Six months.

I joined MongoDB six months ago. After ServiceNow I was at Cloudflare, and after Cloudflare I joined MongoDB to be first-time CEO, like you are. We're doing it together. MongoDB was created in 2007, and the fundamentals were very simple: relational databases are very rigid. The Oracles of the world and SQL Servers of the world are a very rigid way to represent data. You should represent data in a natural way, as in a document model. And cloud was just emerging, as in AWS public cloud. So can you leverage data and scale data in a scale-out fashion rather than scale-up? Oracle and all other architectures are scale-up architectures. You can run on commodity hardware and be cloud agnostic. I mean literally that was the thing.

Very similar to your story. Unstructured data, as you know Sanjay, you guys do this every day, continues to grow at a much higher rate than structured, and AI continues to have a lot of unstructured data. I want to create an image, I want to create a video, whatever it is. All of those things — we are the default data platform for many companies, and we want to be the default data platform in the agentic world as well as how we have been in the application world.

Yeah, we go back and forth a lot internally, and I'm sure you do too — how do we segment customers in this AI transformation. So we have 65,200 customers. We have a product-led growth motion which helps us with acquisition of those customers in a very effective way. You have frontier model companies — very few, you can count them on fingers. Then you have AI-native companies, so these are the companies truly built after 2022, ChatGPT era, where on top of LLMs, whichever LLM you use, you build something. Those are AI-native companies. Then you had the digital natives here in the Bay Area, many companies that got created in early 2000s, post-mobile era and so on. And then the enterprises. Enterprises is definitely where MongoDB is used for mission-critical workloads — whether it's payments, commercial banking, healthcare, insurance policies, public sector. Within the enterprises, I would say banking or financial services, healthcare, tech, and then the fourth one I would put as public sector.

The way I see it is — and I understand why they are using us and for what specific use case — there are two ways. Even if you're training a frontier model, there's so much unstructured data that you feed into the LLM so that LLM can provide better answers. So some frontier model companies use us for research, because they put lots and lots of training data and then the new model comes out. Versus there is a frontier model company that uses us for inference. So you ask a question, create an image of Sanjay and CJ standing together, it creates an image and then you go back and forth and it keeps that long-term memory. So we have research as well as inference, depending on who it is.

First, I do want to say thanks to your teams. Even before the partnership, for our joint customers, which really matters — Helios control plane is built on top of MongoDB. And that is the brain behind all the different backup silos as you outlined. That really means a lot to us — that we are part of the control plane on Helios, that just tells us that we are the most mission-critical even for you on how you serve. You're a great customer and we embed that everywhere we go. Number two, on the customers Sanjay, it's very simple. Initially we started out around 2007-2008 when MongoDB was created, a lot of focus — we were boxed as a NoSQL company, unstructured data company, and hey, if it's unstructured data, this is a great database for it because of document model, scale-out, etc. But over time we have become mission-critical at some of the firms, our joint customers, where they are running — like I said — payment processing workloads or insurance policy claims. All of those mission-critical workloads run on MongoDB. And so then the request was, hey, this is our horizontal backup strategy, they are always trying to improve RTO, and with ransomware and all that you guys have been focused on — all these regulated industries, both from an operational resilience perspective but just cyber protection — the ask is always the simple: how do you work with leaders such as Cohesity and what can you do? So that partnership, from my standpoint, just gives them peace of mind. And if it gives our customers peace of mind, it gives me peace of mind. The ransomware stats — they're insane right now — all the way from core data to identity. Immutable is such an important point. In this regulated industry where they have to show to regulators all the RTO objectives and so on, that is where the partnership is really meaningful. For me it's just a simple statement to that CIO or CTO at a bank — we can articulate our joint partnership together so that they know, hey, if I made MongoDB as my standard for retail banking, and our horizontal standard for backup, restore, cyber resilience is Cohesity — how do you guys work together? And I should be able to answer that very simply.

I know, and Sanjay, here is the interesting piece that I'm seeing. If you look at frontier model companies, even last week's announcement by somebody like Anthropic — they are now moving their workloads on-prem. And this is very interesting — you and I have worked together at many other companies in the past, and we thought like ten years ago everybody is moving to public cloud. And now when I'm speaking to customers, the joint customers we have — there is this fascination: hey, we may not move this application to public cloud, and we are actually expanding more. One of the large banks on the east coast, they're expanding their on-prem data center footprint even more now. And I know that's the one big advantage NetBackup had, Cohesity had, and that's the advantage MongoDB has. So I go and tell these customers, that's fine if you don't want to move to Atlas, our public consumption offering, you can use MongoDB on-prem. And I know it's the same codebase that works for Cohesity across multi-cloud.

Hybrid, yeah.

Data sovereignty. I think there's going to be some yin and yang where there's going to be data — especially in the Middle East, European countries that might have sensitivity to US public clouds — having a sovereign cloud solution that works for them. Even banks, even Indian banks. I was talking to one of our sales teams recently and they said that the whole data sovereignty in parts of Asia is becoming even more important. So not only just Europe — of course France, you see a lot, UK public sector very meaningful — but then you also go to Scandinavia, parts of Scandinavia. And like you said, Middle East, it almost has to work in a software environment.

Yeah. The amount of data — when I was coming up to speed both on Cohesity and the Veritas data protection architecture that has evolved on top of the file system over time — your file system has a lot more data than we always will have. How do we make it easy to write that data, APIs and integration? And also from an RTO perspective, how fast you can restore a MongoDB cluster at a bank or a public sector organization or whatever that is — that's critical, because we are becoming mission-critical. And in the AI world, Sanjay, because of the examples I shared, now there are AI-native companies which are completely built on top of MongoDB or run on MongoDB. But even in banks they are experimenting with agents at production, where we could be the context layer or the memory layer. And that may actually be more data than the persistent data. And how Cohesity and us can partner — that would excite me for the next.

One hundred percent, and 150% credit to David Tachari and the team. They thought of this idea that when you want to do agents in production, it is never an LLM issue. It is always an issue of how effectively you do your RAG, and that should be cheap enough, because there is so much data everywhere. You needed to have real-time feed to give real-time answer — because if I'm making a decision on your mortgage via agents, of course I'm going to do RAG across the entire data states I have. How do we make that effective? So we call it the embedding model. That was the acquisition done of Voyage in 2025, February. And where it's going right now is that we will help you the most effective way to retrieve the right data, and that's what then you feed with your LLM data. So even your cost of tokens goes down. Phenomenal acquisition. And our vision — which already we have achieved now, because it's been 15 months since that acquisition — is core operational data layer with search, vector search, and embeddings all in one, so that you can build agent-based applications at scale. That's it, literally.

That's correct. GenAI app and you feed in text, and now you can ask a question rather than just doing text-based search and all that. And it's all in the operational layer — as in MongoDB operational layer of real-time data. And we are really excited about this team.

Yes.