BREAKING: Inside AI's Biggest Downstream Winner.. the Surge in AI Database Demand
MongoDB CEO CJ Desai
AI’s Biggest Downstream Winner
CJ Desai, CEO of MongoDB, joins Sourcery at the RAISE Summit in Paris. We cover why data is the downstream winner of the AI cycle, how hyperscaler capacity limits are pushing workloads back on-prem, the return of data sovereignty, & what a bank's real agentic architecture actually looks like.
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"Data is the unsung hero and data is back. You cannot create an AI application without a great data layer, and your AI application is as good as your data."
"You see these hyperscalers, some of them are running out of capacity… the hyperscaler said, 'Sorry, we don't have a capacity.' And they are one of the top 50 customers for that hyperscaler."
CJ took over MongoDB (NASDAQ: MDB) in November 2025 after Dev Ittycheria's 11-year run, with a mandate to reposition the company as the default modern database for AI applications. MongoDB carries a market cap of roughly ~$25 billion. CJ runs on a cadence of speaking to 10 to 12 customers a week, and in this conversation he brings that view straight from frontier labs, AI-native startups, and Global 2000 enterprises.
Plus prediction markets under World Cup load, the open vs closed source model debate, data centers in space, and the mentors who shaped him.
We cover:
› The 3 classes of AI customers MongoDB serves
› Why hyperscalers are telling top-50 accounts "no capacity"
› On-prem, sovereign AI, and the data-center comeback
› ElevenLabs running 50M+ agents on MongoDB
› MongoDB (OLTP) vs Snowflake and Databricks (OLAP)
› Why there is no standardization in enterprise AI models
› Auto-scaling and the death of the expensive DBA
Special thank you to Brex, MongoDB, & AssemblyAI for helping make this RAISE AI Summit mini-series in Paris, France happen.
𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒
(00:00) CJ Desai, CEO of MongoDB
(01:12) Top CEOs at the Raise AI Summit
(03:24) The 3 types of companies powering the AI boom
(07:25) Why on-prem is making a shocking comeback
(09:01) Hyperscalers are quietly running out of capacity
(11:06) What actually separates MongoDB from Snowflake and Databricks
(13:18) Why Frontier Labs treat MongoDB as their memory layer
(15:23) The truth about data labeling companies
(16:22) From Oracle intern to first-time CEO
(19:19) Becoming CEO during the AI chaos
(23:53) The World Cup crisis that tested MongoDB's scale
(28:19) The real complexity behind simple AI agents
(31:22) Open source vs. closed models: what customers actually pick
(34:36) CJ's honest take on data centers in space
(36:50) The mentors who shaped a first-time CEO
(40:32) MongoDB's next big bets
(41:36) Data is back?
Brought to you by:
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MongoDB–Millions of developers and more than 65,200+ customers across industries – including ~75% of the Fortune 100 – rely on MongoDB for their most important applications. With integrated capabilities for operational data, search, real-time analytics, & AI-powered data retrieval, MongoDB helps organizations everywhere move faster, innovate more efficiently, & simplify complex architectures. visit → mongodb.com/ai
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CJ Desai on the Surge in AI Database Demand
Desai got his start in databases at Oracle, went on to help scale ServiceNow from $1.5 billion in revenue past $10 billion as its COO, then ran product & engineering at Cloudflare. In November 2025 he took over as CEO of MongoDB.
We spoke with CJ at the RAISE Summit in Paris about 8 months in.
Hyperscalers are running out of capacity
Desai’s customers are telling him the big cloud providers are running low on capacity. “You see these hyperscalers, some of them are running out of capacity.” One case he brings up is a Fortune 100 company in Texas that went to move more workloads, AI included, into public cloud and got turned down. The provider said no. “Sorry, we don’t have a capacity.” The company is one of that provider’s top 50 customers.
So it kept a data center it had been planning to shut down and now runs those workloads itself. Another customer, a US telecom, was refused more regional capacity by one provider and signed with a second, and now juggles two clouds. Different models tend to live in different clouds, so a single app can end up with one sub-agent on GCP and another on Azure.
To Desai, this flips the usual assumption. “Most people get that wrong because everybody thought all the workloads are just gonna move to these hyperscalers.” Between the capacity crunch and the pull to keep proprietary data in-house, he sees on-prem and private data centers coming back, and not only at regulated companies.
Sovereignty adds to it. A large customer in Paris tells him it is keeping everything off public cloud. “Data sovereignty is highest priority. French regulations forces us to have that as the highest priority.” Working across clouds is part of why some customers choose MongoDB to begin with. “They chose MongoDB because we work in multiple clouds. We are not just a database that works in only one cloud, and they really like that for resiliency purposes.”
Databases as downstream winners
A lot of the AI attention goes to the models, but Desai sees MongoDB winning off the data underneath them. Every model and every agent throws off data that has to be stored and served, whatever model a customer picks. “The data layer is typically the unsung hero.” He does not separate the two. “We feel that models and data, both are needed to create a great agentic application.”
MongoDB handles that data at scale. “We do unstructured data really well. We are a scale out architecture.” Mongo does stands for Humongous.
The rest of the category is moving too. Databricks, still private, signed a term sheet in July 2026 for a Coatue-led round at a $188 billion valuation, about $3 billion per the Wall Street Journal, closing later this summer, up from $134 billion in February. Its revenue was running at about $5.4 billion a year in early 2026, growing more than 65%, with positive free cash flow. Snowflake (NYSE, SNOW), which is public, is worth around $93 billion, with roughly $5 billion in trailing revenue and guidance of $5.84 billion in product revenue for fiscal 2027, about 31% growth.
MongoDB is a different kind of database from those two. It is built for live, operational work rather than analytics, though the line is blurring as Databricks pushes Lakebase, a Postgres database aimed at AI agents, into operational territory Oracle has long held. In July 2026, Citi named MongoDB, Snowflake, and Palantir its top three software picks for the year.
Inside enterprise agent architectures
The agent systems Desai sees inside big companies are getting more complicated, not less. He asked one large New York bank to walk him through its whole setup, from the cloud provider down through databases, agent runtime, and models. “I wanna say 55 different boxes for the agentic architecture.” Half of them, he says, did not exist a year earlier, things like observability, security, evaluations, guardrails, and getting agents into production. The bank expects to add more.
Customers are not settling on one model either, and Desai admits he expected them to. “I had a very simple viewpoint of the world from a customer perspective. Oh, are you standardizing on this particular model.” Instead it is a mix. “There is no standardization even when it comes to these models. It goes anywhere from open source to closed source, small to large, horizontal to domain specific based on use cases.” For coding, customers reach for proprietary tools like Claude Code, Codex, Cognition, & Cursor. Some pull domain-specific open-source models off Hugging Face, but still.. “I have not seen a complete pivot towards open source or a complete pivot towards proprietary.”
Where the money is going in the AI stack
Menlo Ventures’ enterprise survey puts AI spending at $37 billion in 2025, up from $1.7 billion in 2023, about 6% of the global software market. In Menlo’s map, the data layer, the pipelines, databases, vector stores, and caches that feed models the right context, sits next to deployment and observability, not beneath the model. Other reads of the agent stack, like AImultiple’s, land in a similar place, that value spreads out more than it did in older software, where most of it sat at the app layer, and that pieces like reasoning, orchestration, and governance are the hard ones to copy.
Memory is becoming its own layer. Research from groups like mem0 sees dedicated memory, storage that carries context across sessions, turning into standard infrastructure in 2026 the way vector databases did in 2024. The recurring finding is that the top reason agents break in production is bad data, not the model or the framework. Gartner figures 40% of enterprise applications will use task-specific agents by the end of 2026, up from under 5%.
This is roughly where Desai puts MongoDB. The frontier labs are the ones he watches most. “Frontier labs, I would consider the holy grail for us,” with growth he calls faster than linear. They lean on MongoDB as a memory layer alongside inference and voice, image, and video work.
What fast-growing customers want
The fastest-growing customers want infrastructure that runs itself, Desai says, and they have stopped building things and waiting for demand. “You can’t come and think of, if I build it, they will come. That era is gone.” He points to a prediction-market company that ran low on capacity during the World Cup, with predictions landing every minute. The CEO texted him directly. “It looks like we may be out of capacity in few days on MongoDB, and the World Cup soccer is still couple of weeks away.”
Companies like that rarely staff for infrastructure, he says, since their focus is the models or the product, so they go direct to him. “Why am I calling you and telling you that I’m gonna run out of capacity? Why couldn’t you see it?” He compares it to the old Oracle database administrator, once the most expensive hire a company made, a job AI companies now skip. “We don’t have time to hire these people. If we are using your software, it should have its own machine-based database administration.”
Keeping up with all of that has no fixed answer. "There is no solution. You constantly have to pivot or change the roadmap." He ties the pace to the falling cost of building software. "When software writes software, the incremental cost of innovation has gone down," and the cost of trying things has dropped with it. "This is the slowest. I think it is going to get even faster."
3 kinds of AI customers
Desai splits AI demand into 3 groups. First are the frontier labs, more than one of them, which use MongoDB for inference, voice, image, video, and as a memory layer. He will not name them. Their usage, he says, climbs almost straight up on weekly or daily active users.
Second are the AI-native companies. Vibe-coding platforms like Emergent and Base44 are built on MongoDB, and robotics and physical-AI companies use it to hold what their machines throw off. “Every robot is generating data as they sense thing, as they act on things, and then all that data gets stored in MongoDB.” For scale, he points to ElevenLabs. “They have north of 50 million agents, depending on when you look at it, all running on MongoDB. That also gives us a lot of confidence that we have the right architecture for agentic workloads.”
Third are the big enterprises building agent workloads, and there Desai says it is still early. He has not seen customer-facing agents running at scale in airline or banking apps yet, and most of what he sees is prototyping. The customers run across travel, entertainment, and media, and include the food-delivery company Zomato, on MongoDB Atlas.
MongoDB’s talent density
Mati, founder of ElevenLabs told Desai that MongoDB’s staff are a hiring target. “Everybody told me we should hire salespeople from MongoDB because you guys do a really good job of training them. Same thing with our engineers because they understand distributed systems & data really well.” ..Which he obviously has mixed feelings about it..
TLDR: Data is back
“Data is the unsung hero & data is back. You cannot create an AI application without a great data layer, & your AI application is as good as your data.”
“Regardless of the era.. i.e. internet era, mobile era, after iPhone, now the AI era.. you always need a data layer.”
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