The Rise of AI Databases: Oracle, Microsoft, Databricks, Google Cloud
Shared by John Foley from Cloud Database Report · October 8, 2026
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Welcome to the Cloud Database Report. I’m John Foley, a long-time tech journalist who also worked in strategic comms at Oracle, IBM, and MongoDB. Need help communicating your company’s strategy? Connect with me on LinkedIn [ https://substack.com/redirect/92e00c88-97f9-437b-aeac-bf8245eeaf21?j=eyJ1IjoiMTR3eXB3In0.f-xldB9G03IXstk7BAMCqX62DEM2cFHI5JhVsvW8cAA ]. Many different databases are used for AI workloads and applications. Few of them are actually called AI databases. That’s beginning to change. While the database market is deeply rooted in legacy terminology, columnar, key-values, event stores, etc., the action today is around AI platforms for AI workloads. Increasingly, databases are being designed and optimized for AI. Product marketing teams will see this as an opportunity. I’m not talking about vector databases, which are becoming commodity-like as vector support goes mainstream in everything from Oracle to MongoDB to Redis. Vectors are one aspect of AI databases, but there’s more: LLMs, machine learning algorithms, knowledge graphs, AI frameworks, agentic AI protocols, automation of data management, RAG, natural language queries, and more. As vendors bundle these AI capabilities into their database management systems, there’s a crossover point where they become, in effect, AI databases. Oracle was one of the first to seize on this when it changed the suffix of its flagship database from 21c (for cloud) to 23ai nearly two years ago. In October 2025, Oracle took it a step further. It introduced Oracle AI Database 26ai [ https://substack.com/redirect/b77f28a7-3c9c-4be9-9385-5d018c208b93?j=eyJ1IjoiMTR3eXB3In0.f-xldB9G03IXstk7BAMCqX62DEM2cFHI5JhVsvW8cAA ]with even more AI capabilities layered into the system, including an MCP server for AI agents and support for private AI models. In the transition from 23ai to 26ai, Oracle made a small but significant change to its naming convention. It began referring to its platform as Oracle AI Database. Larry Ellison now uses “Oracle AI Database” on earnings calls [ https://substack.com/redirect/27e44d6a-21bb-4e3f-9088-38941e6f17ad?j=eyJ1IjoiMTR3eXB3In0.f-xldB9G03IXstk7BAMCqX62DEM2cFHI5JhVsvW8cAA ]. Oracle’s not the only database vendor thinking this way. As I reported exclusively last July, Databricks CEO Ali Ghodsi has been lobbying internally (unsuccessfully so far) for Databricks to call its platform as an AI database. Here’s what Ghodsi said at the time: “Fun fact: for many years (since 2016) I’ve been advocating for Databricks to say it’s an AI Database,” Ghodsi commented on LinkedIn. “But all marketing firms we worked with, and all the user surveys, showed that people hated it. I still think it’s the most accurate way in 2 words to understand what Databricks does (and has been doing since 2013)!” See my post, “Databricks Is an ‘AI Database’,” [ https://substack.com/redirect/53b6db48-305d-423a-83fe-77839bfd7419?j=eyJ1IjoiMTR3eXB3In0.f-xldB9G03IXstk7BAMCqX62DEM2cFHI5JhVsvW8cAA ] for the full story. About this same time, Microsoft released SQL Server 2025, which it described as its AI release. “SQL Server 2025 is the first release where we’re basically putting AI capabilities right into the heart of the engine,” Shireesh Thota, Corporate VP of Databases, told me in an interview [ https://substack.com/redirect/ce3502e7-fd06-43ba-b552-e907e6b4f39a?j=eyJ1IjoiMTR3eXB3In0.f-xldB9G03IXstk7BAMCqX62DEM2cFHI5JhVsvW8cAA ]. ‘Dawn of the AI-native database’ Google Cloud is jumping into the conversation, too. Yasmeen Ahmad, Google Cloud’s managing director of Data Cloud, recently published a blog post, “From system of record to system of reason: The rise of the AI-native database.” [ https://substack.com/redirect/46557f9a-e042-4693-b11e-e8c22451c5ee?j=eyJ1IjoiMTR3eXB3In0.f-xldB9G03IXstk7BAMCqX62DEM2cFHI5JhVsvW8cAA ] Ahmad focuses on the tech stack needed for agentic AI, so his definition is narrower. “The database can no longer be a passive record-keeper,” Ahmad writes. “It must become a system of reason, an active, intelligent platform that serves as the agent’s conscience. Beyond recording what an agent did, it must provide an immutable, explainable ‘chain of thought’ for why it did it. This is the dawn of the AI-native database.” Ahmad explains how different databases are used to support multi-tiered memory for agentic AI, with short-term memory for immediate tasks and long-term memory for knowledge over time. The database architecture to do this, he writes, might comprise Google Spanner (for short-term memory), BigQuery (for long-term memory), and GraphRAG (graph-structured data for retrieval-augmented generation) to enable AI agents to work across various information sources. Ahmad’s article provides an nice overview of how to think about a system of reasoning. To be clear, however, he’s not talking about a single AI database but rather several databases as part of an overall solution. If you click on “AI-native database” in his post, it links to the full portfolio of Google Cloud databases [ https://substack.com/redirect/d6a62592-3ce8-4704-903c-f615b48126f7?j=eyJ1IjoiMTR3eXB3In0.f-xldB9G03IXstk7BAMCqX62DEM2cFHI5JhVsvW8cAA ]. In other words, Google Cloud is offering a menu of AI-native databases, so customers can pick and choose among them. But Google Cloud doesn’t have a centerpiece AI database in the way that Oracle does. Likewise with AWS, which offers a range of AI-capable databases (Aurora, DocumentDB, DynamoDB) and complementary services (Bedrock, SageMaker). AWS doesn’t have just one product branded as its AI database. In short, the AI database phenomenon is largely about new capabilities but also about how you describe these systems to a world that is looking for them. Another hype cycle? I’m sure that some will argue that calling a system an “AI database” is marketing spin. It’s true that various types of database systems can and are being used for AI workloads, as the examples of Google Cloud and AWS show. There’s no one size fits all for AI projects. However, it’s also true that a growing number of database platforms are being optimized for AI use cases. And when you consider that Oracle now refers to its flagship system as an AI database, one has to wonder if others will follow suit. I think they will. In fact, Microsoft now describes Cosmos DB as an AI database on its product page [ https://substack.com/redirect/bda83d11-dab9-4b1c-a026-20c0597402d4?j=eyJ1IjoiMTR3eXB3In0.f-xldB9G03IXstk7BAMCqX62DEM2cFHI5JhVsvW8cAA ]. But it has yet to go all-in on that. Microsoft also refers to Cosmos DB as “a fully managed NoSQL, relational, and vector database.” To be sure, we can’t dispense with the traditional database descriptors completely. Different types of database engines may be under the hood of AI databases. Vendors will have to be smart and savvy in how they position and talk about these next-gen DBMSs. They must be careful not to wash everything with an AI brush. This reminds me of the early days of cloud computing, when everyone jumped onto the cloud bandwagon, much to the ire of Oracle’s Larry Ellison, who in 2008 described the hubbub over clouds as “idiocy,” “crap,” and “gibberish.” Now with AI databases, Ellison is part of the buzz. Maybe all that cloud talk offers a lesson in what’s next. Over time, the cloud market evolved into different types of cloud services, SaaS, PaaS, IaaS, and so on. The same kind of thing could happen with AI databases, relational AI databases, graph AI databases, etc. ClickHouse joins AI database race Consider the example of ClickHouse, the analytics and data warehouse startup that last week revealed $400 million in Series D financing at a $15 billion valuation. As part of its announcement [ https://substack.com/redirect/8a1c564e-d38d-414a-a183-f3f247b1586b?j=eyJ1IjoiMTR3eXB3In0.f-xldB9G03IXstk7BAMCqX62DEM2cFHI5JhVsvW8cAA ], ClickHouse disclosed a partnership with Ubicloud to include a Postgres database service within its platform as a way of combining transactional and analytical capabilities for AI dev and apps. (Databricks and Snowflake each introduced transactions plus analytics on Postgres in the second quarter of 2025.) What’s interesting is that ClickHouse describes its product/service as an “analytical database.” Or more specifically as an “open-source columnar database management system built for real-time data processing and analytics at scale.” That language works fine for CTOs, DBAs, and other technologists familiar with what a columnar database is, but it may fly over the heads of non-techies. In contrast, Bloomberg came up with a different and more accessible way of talking about the same thing. Its headline: “ClickHouse Lands $15 Billion Valuation in AI Database Race.” [ https://substack.com/redirect/91e477a4-1427-4e50-ac59-f70d305e8d78?j=eyJ1IjoiMTR3eXB3In0.f-xldB9G03IXstk7BAMCqX62DEM2cFHI5JhVsvW8cAA ] There’s that term again. I’m not suggesting vendors slap “AI database” on every DBMS that packs a few AI features. But if it walks like an AI database and talks like an AI database, maybe it’s an AI database. Additional reading