Why AI Databases Are the Next Big Leap for Vector Search and Multimodal Data

Artificial intelligence databases are multi‑purpose platforms that manage both structured and unstructured data while applying AI models to a variety of data formats.

Google Trends shows a soaring interest in vector databases among developers and other users.

Forrester’s 2024 Q2 Vector Database Overview highlights more than 20 vector databases, classifying them into two groups: native vector databases and multimodal databases that embed vector storage within broader data ecosystems.

Native vector databases aim for optimal scale and performance, whereas multimodal databases provide versatility for handling multiple data types, reducing the complexity of managing separate systems.

Vector databases specialize in storing, managing, and querying high‑dimensional vectors, which is essential for applications that retrieve content via semantic similarity.

These databases emerged in the late 2010s, driven by generative AI because they enable fast, accurate similarity search crucial for recommendation systems, natural‑language processing, and image‑recognition tasks, thereby enhancing AI application quality and versatility.

Although vector databases are key to generative AI, vectors represent only a small part of a larger challenge; comprehensive search powered by machine‑learning algorithms is needed to detect patterns, predict outcomes, identify anomalies, and recommend actions.

This must be performed over billions of rapidly changing data points, delivering results in under 100 ms while supporting thousands of concurrent queries per second. Most business applications also need to integrate and analyze unstructured data (e.g., PDFs) alongside traditional structured data to generate vectors.

Focusing solely on vector databases can overlook the broader picture. According to Forrester, selecting the best vector database still requires integrating components such as machine‑learning, support for non‑vector data types, and workload management for performance and high concurrency, or opting for multimodal databases that handle a wider range of data types but may need adaptation for specific applications.

Entering AI Databases

A new type of database is emerging: AI Database

AI databases are multi‑purpose platforms that, beyond vectors, also manage structured and unstructured data. They apply AI models to various data formats, combine signals for more accurate output, and improve computational efficiency while supporting scalability by integrating models and data types.

They organize data by clustering similar vectors in query results, support compliance, and enable searches across tables, text, and vectors for specific values, document matching, and similarity search, using AI models for inference.

AI databases support three main AI model types

Machine‑learning models discover patterns in historical data to predict trends, detect anomalies, rank results, and recommend actions, primarily using tabular, text, or image data.

NLP models interpret and generate text or speech for tasks such as translation or sentiment analysis, mainly handling text files.

Generative AI models create text, images, audio, or video from existing data and predict the next element in a sequence.

These models are typically hosted and run within AI databases, learning from incoming data, performing inference, and producing outputs.

While AI databases represent a major technological advance, they lack built‑in application logic and runtime management, making them only a partial solution. Meeting the demanding scale and latency requirements of generative AI requires substantial effort to integrate tools and optimize runtime performance.

The most effective approach is to seamlessly combine data, application logic, and large‑scale execution platforms, delivering a comprehensive solution that satisfies all critical requirements.

Vespa: an open‑source AI engineering platform

Vespa.ai is an open‑source engineering platform for developing and running real‑time AI‑driven applications such as search, recommendation, personalization, and retrieval‑augmented generation (RAG).

Vespa efficiently manages data, inference, and logic, supporting applications with massive data volumes and high query concurrency. It is available both as a hosted service and as open‑source software.

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