Databases for AI Model & Agent Engineering

Stores data in structured tables with rows and columns.

AI use case: Used to manage customer data, transaction records, or annotated datasets for fine-tuning models.

Stores data by columns instead of rows, optimizing analytical scan performance.

AI use case: Used in BI dashboards to generate insights that feed into model retraining decisions.

Combines traditional SQL structure with distributed, high-performance scalability.

AI use case: Used in AI-powered SaaS apps to handle real-time user queries and decisions.

Designed to store and query location-based data.

AI use case: Used in geospatial AI models for drone navigation, delivery routing, and map intelligence.

Stores data as nodes and relationships (edges).

AI use case: Used in LLM agents to store contextual memory or long-term knowledge in an agent's reasoning system.

Stores data as objects, like in programming.

AI use case: Used in AI modeling for robotics, simulations, or computer vision with structured components.

Stores entire documents (like JSON).

AI use case: Used in LLM-based apps to persist conversations, agent memory, and dynamic instructions.

Stores data as simple key and value pairs.

AI use case: Used in AI systems for caching prompt embeddings, storing access tokens, or personalization parameters.

Tracks data changes over time with timestamps.

AI use case: Used in model observability dashboards to track latency, accuracy drift, or user activity trends.

Stores complex data like images or text as high-dimensional vectors.

AI use case: Used to fetch relevant documents in LLM pipelines based on similarity of meaning (RAG).

Keeps all data in RAM for lightning-fast access.

AI use case: Used in chatbots, recommendation engines, or fraud detection where millisecond response is needed.

Stores records in secure, unchangeable blocks.

AI use case: Used in ensuring provenance of training data or verifying model output traceability.