AI Databases Explained

What is an AI Database?​

In the age of AI, vector embeddings have become the de facto data type, and a new class of AI databases has emerged to support them. An AI database is a specialized data management system built to handle the unique demands of AI applications, large language models (LLMs), and machine learning (ML) algorithms. Traditional databases are not optimized to store and retrieve vector embeddings or simplify the development of modern AI applications, though some have added vector-based storage. We’ll explain the difference between “AI-native” and “AI-enabled” options below.

AI Database Examples​

When building AI-driven applications, it’s essential for developers to understand the different options available for vector data storage and retrieval. Though vector database is becoming a broad category, the nuances between AI-native databases (like Weaviate), search libraries, and traditional databases with added vector stores can make all the difference in the performance and success of your application.

AI Database Tools​

AI-native: Purpose-built vector databases​

AI-native vector databases are purpose-built to handle large-scale AI workloads efficiently. These databases offer high-performance indexing (e.g., HNSW), hybrid search, and seamless integration with machine learning models, making them ideal for scalable and mission-critical AI applications.

AI-enabled: Search libraries​

On the other hand, search libraries like FAISS, Annoy, and ScaNN provide vector search capabilities but lack persistence, distributed infrastructure, and data management, often requiring significant engineering to scale.

AI-enabled: Traditional databases with vector extensions​

Plugins and extensions for traditional databases allow organizations to add vector functionality to existing systems. While this integration of AI and databases can be convenient, they can face performance limitations at scale. In addition, some data warehouses provide AI-friendly analytics capabilities.

Selecting the right tool depends on your application’s scale, performance needs, and existing infrastructure.

AI Database Design​

By leveraging an AI-native stack, with an AI database at its core, organizations can create custom AI database management systems that reduce development complexity, improve application speeds, and optimize resource utilization. This architecture is particularly advantageous for applications requiring large-scale real-time AI operations, such as recommendation systems, intelligent search platforms, and agentic AI.

Capabilities to Look for in an AI Database​

When selecting an AI database, it's important to evaluate capabilities that ensure scalability, performance, and integration with AI workflows will be suited to your needs. Below are key capabilities to consider.

1. Vector Storage and Retrieval: Efficient handling of high-dimensional vector embeddings, optimized for large-scale vector storage with minimal query latency.
2. Dynamic Indexing Techniques: The ability to choose or customize index types based on data size and application requirements. Ex: HNSW (Hierarchical Navigable Small World) for fast approximate nearest neighbor (ANN) search and Flat Indexes for smaller datasets.
3. Hybrid Search: Combination of vector-based semantic search and traditional keyword search in a single query. Useful for balancing semantic accuracy and factual precision.
4. Performance and Scalability: Low-latency query execution even with large datasets and the ability to distribute data and queries across multiple nodes for horizontal scalability.
5. Integration with AI Models and Ecosystem: Direct support for popular machine learning frameworks like LangChain, LlamaIndex, and Hugging Face; Compatibility with AI model providers like OpenAI, Cohere, AWS, Azure; and built-in vectorizers to automate the transformation of raw data into embeddings.
6. Data Flexibility: Multi-modal capabilities to handle diverse data types such as text, images, and audio.
7. Data Compression and Optimization: Techniques like vector quantization to reduce storage size without compromising retrieval speed. Support for various compression algorithms tailored to different datasets and use cases.
8. Security and Multi-Tenancy: Isolation of data across clients or projects through physical or logical separation (multi-tenancy). Enterprise-grade security features, including encryption, role-based access control, and compliance with data residency requirements.
9. Cost-Performance Optimization: Flexible storage options (e.g., in-memory, on-disk, or cloud storage) to balance performance and cost. Ability to offload less frequently accessed data while maintaining real-time performance for critical queries.
10. Ecosystem and Community Support: Strong developer ecosystem with other AI database tools, documentation, and community resources. Regular updates to support new AI technologies and maintain compatibility with evolving AI models and workflows.

Using an open-source AI database provides flexibility, transparency, and cost-effectiveness, allowing developers to customize and optimize it for specific use cases without vendor lock-in.

Summary of AI Databases​

Vector embeddings have become the core data type driving applications powered by LLMs and machine learning algorithms. Traditional databases, though evolving with vector extensions, are not optimized to handle the complexity of modern AI workloads. This has led to the rise of AI-native vector databases, such as Weaviate, which provide purpose-built AI database solutions for large-scale vector storage, efficient retrieval, and hybrid search capabilities.

Selecting the right AI database ultimately depends on your application’s scale, infrastructure, and performance needs. For enterprises prioritizing scalability and reliability, AI-native solutions like Weaviate provide a future-proof foundation to support evolving AI workflows.

To learn more, read our ebook: Choosing the Right Database for AI.