AI Database for Real-Time Analytics, Vector Search, and AI Applications

An AI database is a database system built to support artificial intelligence and machine learning workloads. Unlike traditional databases optimized only for transactions or reporting, an AI database is designed to:

• Ingest large volumes of data continuously

• Store structured, semi-structured, and vector data together

• Run analytical queries for feature extraction and model context

• Perform vector similarity search on embeddings

• Combine AI queries with filters, aggregations, and time-based analysis

This combination distinguishes an AI database from transactional databases, data warehouses, and standalone vector databases.

AI applications depend on fast access to relevant, up-to-date data. An AI database acts as the foundation that feeds models with real-time context, enabling intelligent systems to reason over both similarity and structure.

Most traditional databases were not designed to support production AI workloads.

Transactional databases struggle with analytical queries and high ingestion rates. Analytical data warehouses introduce latency and batch pipelines that delay access to fresh data. Vector databases often require separate systems, adding operational complexity and limiting query flexibility.

AI workloads need more than storage. They require real-time analytics, hybrid queries, and the ability to evolve data models without friction.

CrateDB is a distributed SQL database built for real-time analytics and AI-driven applications. It brings together ingestion, analytics, and vector search in a single system, eliminating the need to stitch together multiple databases.

With CrateDB, teams can build AI applications on live data without sacrificing performance, scalability, or simplicity.

• Run vector search and analytics in one query layer

• Eliminate batch pipelines between systems

• Serve AI applications directly from live operational data

CrateDB supports vector search alongside traditional SQL analytics, enabling AI applications to retrieve relevant context efficiently.

You can store vector embeddings directly in the database and perform similarity search while applying filters, aggregations, and time constraints using SQL. This makes it possible to run hybrid queries that combine semantic similarity with structured data conditions.

Typical examples include:

• Finding similar documents while filtering by metadata

• Retrieving recent events related to an embedding

• Combining vector similarity with aggregations for ranking and scoring

This makes CrateDB especially well suited as an AI database for hybrid search and context retrieval.

AI models are only as good as the data they operate on. CrateDB enables real-time analytics on continuously ingested data, ensuring AI systems always work with current information.

High-throughput ingestion, automatic indexing, and distributed query execution allow teams to analyze fresh and historical data without pre-aggregation or manual tuning. This is essential for AI use cases that depend on live signals and evolving patterns.

This ensures AI inference, ranking, and decisioning always reflect current system state.

AI pipelines rarely operate on perfectly structured data. CrateDB supports structured tables, JSON documents, and evolving schemas in the same database.

This flexibility allows teams to:

• Store raw events, embeddings, and enriched features together

• Evolve data models without migrations

• Join semi-structured data with relational data using SQL

• Adapt quickly as AI requirements change

CrateDB powers a wide range of AI-driven applications, including:

• Retrieval-Augmented Generation (RAG): Store embeddings and metadata together, retrieve relevant context in real time, and feed large language models with fresh, filtered data.

• Semantic and Hybrid Search: Combine vector similarity with structured filters, aggregations, and time-based constraints for precise and explainable search.

• Real-Time Recommendations:

• Anomaly Detection: Detect unusual patterns in streaming data using real-time analytics and historical context.
• Feature Stores for Machine Learning: Generate and query features directly from live data without exporting to separate systems.

Instead of managing separate systems for ingestion, analytics, and vector search, CrateDB provides a unified AI database that scales horizontally and operates reliably in production environments.

Key capabilities include:

• Distributed SQL for scalable ingestion and querying

• Real-time indexing with minimal latency

• Vector search integrated with SQL

• High-cardinality analytics without pre-aggregation

• Support for structured and semi-structured data

• Open source with cloud and self-managed deployment options

Traditional databases require trade-offs between freshness, flexibility, and performance. CrateDB removes these trade-offs by combining real-time analytics and AI capabilities in one distributed system.

AI teams can focus on building intelligent applications instead of maintaining complex data pipelines.

Unlike traditional databases that optimize for a single workload, an AI database must support ingestion, analytics, and vector search simultaneously.