The term “AI database” gets used loosely, and that’s partly because it describes a moving target. It’s not one specific product or technology. Rather, it’s a broad shift in how databases are being designed, extended, and used as AI becomes central to how software works.
To make sense of it, it helps to look at the different ways AI and databases are intersecting right now. Because there are several, and they’re quite different from each other.
When a query has multiple execution plans in Query Store, comparing their performance can help you identify which plan performs best and understand why performance may have regressed. This comparison can be very useful for troubleshooting queries that suddenly became slower after a plan change.
Data is the fuel that powers machine learning. The more of it you have, the better your models tend to perform. But real-world data comes with a lot of baggage. Privacy concerns, legal restrictions, high collection costs, and sometimes, just plain scarcity. Synthetic data is how the industry is working around that problem.
Simply put, synthetic data is artificially generated data that mimics real data without actually being real.
It’s not collected from users, scraped from the web, or pulled from production systems. It’s created by algorithms, statistical models, or AI systems that have learned the patterns and structure of real data well enough to produce convincing imitations of it.
As AI has become central to how software is built, the database industry has responded in two ways. Some databases have added AI features on top of their existing architecture. Vector search here, a natural language query interface there. Others have been built from scratch with AI workloads as the primary design constraint.
That second category is what we mean by “AI-native”.
Ontology-based data storage is a way of organizing data using a formal model that defines what things are and how they relate to each other. The model itself, the ontology, sits at the center of how everything is stored and queried. Rather than treating data as rows and values, it treats data as a web of typed, rule-governed relationships that the system can reason with directly.
Databases are everywhere. Every app you use, every website you visit, every transaction you make is backed by a database. But keeping a database running well has always required a lot of human expertise. Expertise for things like tuning performance, managing storage, applying patches, backing up data, scaling up when traffic spikes. For decades, this was just the cost of doing business. You hired database administrators, and they kept the lights on.
A self-driving database is one that handles most of that work itself.
If you’re getting an error that reads something like “‘DAYS’ is not a recognized dateadd option” in SQL Server, it’s because you’re using the DATEADD() function with an invalid datepart argument.
This often happens when you use a plural form of the argument. For example, DAYS instead of DAY. Or HOURS instead of HOUR.
The easiest way to fix this is to provide a valid datepart argument.
Data fabric is a term that gets used a lot in enterprise tech circles, but it’s often explained in ways that are either too vague or too technical to be useful. Here’s a plain-language breakdown of what it actually means.
High-dimensional vector search is a foundational way AI systems find similar or relevant items across large datasets when the data has been converted into vectors. If you’ve used semantic search, gotten eerily accurate recommendations, or worked with a retrieval-augmented AI tool, this is often the mechanism running underneath.
