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Data

AI Database

A real-time AI database that stores vectors, features, and all application data in one scalable engine.

Modern AI systems depend on fast feature pipelines, vector search, and real time access to high volume data. Traditional databases and stand-alone vector stores introduce latency, complexity, and operational overhead because data must move across multiple systems.

CrateDB solves this by providing an AI ready database that ingests data at high speed, stores any data type, and serves analytical, search, and vector workloads through SQL. It supports real time AI features, embeddings, event processing, and time series data in one distributed engine. CrateDB is built to power AI applications that require instant insights and continuous learning.

Unified storage for AI workloads

CrateDB stores all the data AI systems need:

  • Vector embeddings
  • Real time features
  • Telemetry and time series
  • JSON documents
  • Text for search
  • Geospatial data
  • Relational data
  • Binary objects
This multi-model approach reduces the need for multiple databases and keeps AI systems faster and simpler to operate.
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Real time ingestion for AI pipelines

AI applications depend on fresh, high volume data. CrateDB ingests millions of events per second and indexes them automatically within milliseconds. Features, logs, and sensor data become query ready immediately.

This makes CrateDB ideal for:

  • Real time recommendations
  • Streaming analytics for AI
  • Continuous model retraining
  • Online feature stores
  • Fraud detection
  • IoT AI workloads
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Vector search built into the database

CrateDB supports vector embeddings directly inside the SQL engine.
You can store vectors, compute similarities, and run hybrid search that combines:

This combination supports AI workloads such as semantic search, anomaly detection, intelligent assistants, and retrieval augmented generation.
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Distributed architecture for AI scale

CrateDB distributes data, queries, and vector workloads across multiple nodes.
The cluster scales horizontally, so performance increases as you add nodes. This supports:

  • High concurrency
  • Heavy read and write loads
  • Very large vector datasets
  • Mixed analytical and operational workloads
  • Multi model AI applications
All with consistent performance and high availability.
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Fast analytics for feature engineering

CrateDB can run aggregations, joins, and complex SQL queries on large datasets in milliseconds.  This supports both offline and real time feature engineering and removes the need for external preprocessing pipelines.

Use SQL to compute features, track historical patterns, or analyze live events at scale.

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Hybrid search for AI applications

AI systems often need more than vector similarity. CrateDB provides a hybrid search layer that combines:

  • Vector search
  • Full text search
  • Structured filters (SQL)
  • Time range queries
  • Geospatial constraints
  • Metadata based conditions
This allows AI applications to deliver accurate and context aware responses.
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Real time features and online AI

Many AI applications require access to recent events and computed features.
CrateDB supports both:

  • Online feature store patterns
  • Real time event driven features
  • Historical reference data
All in one system with no pipeline complexity.
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Ideal use cases for an AI database

  • Generative AI applications
  • Intelligent assistants
  • Semantic search
  • Fraud detection
  • Smart manufacturing and IoT AI
  • Predictive maintenance
  • Real time personalization
  • Behavioural analytics
  • Computer vision metadata indexing
  • RAG systems backed by structured and unstructured data
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Learn more about CrateDB

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Additional resources

FAQ

An AI database is a system designed to store vectors, features, events, and metadata required by AI applications. It supports fast ingestion, low latency queries, vector search, and real time analytics.

AI applications depend on real time features, embeddings, and large scale data processing. Standard databases or stand-alone vector stores create latency and complexity. An AI database unifies storage and processing in one place.

Yes. CrateDB stores vector embeddings natively and supports vector similarity search.

Yes. CrateDB ingests data at high speed and processes queries in milliseconds. This supports real time recommendations, anomaly detection, online feature stores, and live monitoring.

Yes. CrateDB combines vector search, full text search, SQL filters, and geospatial queries in one engine. This enables accurate and context aware AI results.