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”.
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.
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.
Semantic retrieval is a way of finding information based on meaning rather than matching exact words. You ask a question or describe what you need, and the system finds relevant results even if they use completely different wording. That gap between what someone types and what they actually mean is exactly what semantic retrieval is designed to close.
One of the hardest things about building AI systems is that the things humans care about (words, sentences, images, ideas, etc) aren’t naturally something a computer can do math on. A computer doesn’t inherently know that “happy” and “joyful” are similar, or that a photo of a dog and the word “dog” are related. It just sees raw data.
Embeddings are the solution to that problem.
Large language models are impressive, but they have a fundamental limitation in that they only know what they were trained on. Ask a model about something that happened after its training cutoff, or about a document sitting in your company’s internal knowledge base, and it either makes something up or tells you it doesn’t know.
Retrieval-augmented generation, almost always shortened to RAG, is the approach the industry has settled on to fix this.
The idea is pretty straightforward. Instead of relying purely on what the model has memorized, you give it the ability to pull in relevant information from an external source, then use that information to generate a response.
Computers are good at numbers. They’re not naturally good at understanding that “dog” and “puppy” are related, that a photo of a beach and the phrase “summer vacation” share something in common, or that a five-star review and the sentence “this product is amazing” mean roughly the same thing.
Embedding models are how we bridge that gap.