Why moteDB? The Embedded Multimodal Database Built for Embodied AI
Modern AI applications—especially in robotics and embodied AI—generate and consume data in multiple formats simultaneously: vectors for semantic search, time-series for sensor readings, structured records for configuration and state. The traditional approach of stitching together a vector database + time-series DB + relational DB creates complexity, latency, and deployment headaches.
The Problem with Cloud-First Database Architectures
Most production AI systems today use separate databases for vectors, time-series, and structured data. This works great in the cloud, but what happens when your AI agent is a robot operating in a warehouse, an autonomous drone, or a factory floor controller? You cannot afford a round-trip to a cloud database on every perception loop cycle. You need data on-device.
What is moteDB?
moteDB is the world's first AI-native embedded multimodal database. Built 100% in Rust, it provides vector storage, time-series data, and structured data in one zero-server engine.
Key features:
- Vector storage and ANN search with HNSW and IVF indexes
- High-throughput time-series ingestion with compression
- Relational-style structured tables
- No separate server process, no network overhead
- Single binary, embedded in your application
Getting Started
Install via cargo:
cargo add motedb
Or check out the GitHub repo at github.com/motedb/motedb for examples and documentation.
We are actively building and welcome contributors and feedback!
Why moteDB? The Embedded Multimodal Database Built for Embodied AI
Modern AI applications—especially in robotics and embodied AI—generate and consume data in multiple formats simultaneously: vectors for semantic search, time-series for sensor readings, structured records for configuration and state. The traditional approach of stitching together a vector database + time-series DB + relational DB creates complexity, latency, and deployment headaches.
The Problem with Cloud-First Database Architectures
Most production AI systems today use separate databases for vectors, time-series, and structured data. This works great in the cloud, but what happens when your AI agent is a robot operating in a warehouse, an autonomous drone, or a factory floor controller? You cannot afford a round-trip to a cloud database on every perception loop cycle. You need data on-device.
What is moteDB?
moteDB is the world's first AI-native embedded multimodal database. Built 100% in Rust, it provides vector storage, time-series data, and structured data in one zero-server engine.
Key features:
- Vector storage and ANN search with HNSW and IVF indexes
- High-throughput time-series ingestion with compression
- Relational-style structured tables
- No separate server process, no network overhead
- Single binary, embedded in your application
Getting Started
Install via cargo:
cargo add motedb
Or check out the GitHub repo at github.com/motedb/motedb for examples and documentation.
We are actively building and welcome contributors and feedback!
Why moteDB? The Embedded Multimodal Database Built for Embodied AI
Modern AI applications—especially in robotics and embodied AI—generate and consume data in multiple formats simultaneously: vectors for semantic search, time-series for sensor readings, structured records for configuration and state. The traditional approach of stitching together a vector database + time-series DB + relational DB creates complexity, latency, and deployment headaches.
The Problem with Cloud-First Database Architectures
Most production AI systems today use separate databases for vectors, time-series, and structured data. This works great in the cloud, but what happens when your AI agent is a robot operating in a warehouse, an autonomous drone, or a factory floor controller? You cannot afford a round-trip to a cloud database on every perception loop cycle. You need data on-device.
What is moteDB?
moteDB is the world's first AI-native embedded multimodal database. Built 100% in Rust, it provides vector storage, time-series data, and structured data in one zero-server engine.
Key features:
- Vector storage and ANN search with HNSW and IVF indexes
- High-throughput time-series ingestion with compression
- Relational-style structured tables
- No separate server process, no network overhead
- Single binary, embedded in your application
Getting Started
Install via cargo:
cargo add motedb
Or check out the GitHub repo at github.com/motedb/motedb for examples and documentation.
We are actively building and welcome contributors and feedback!
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