Overview

Vane Data is a high-performance, multimodal-native data engine for AI workloads. Built on a fork of DuckDB, it extends the core execution engine with native multimodal processing and a unified framework for local and distributed execution.

Multimodal InputsOutputs / Outcomes

Sensors

Tables

Documents

Images

Video

Audio

Events

Embeddings

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Vane Data

Native Multimodal Data Model

Compute + Inference Operator Graph

Parallel CPU-GPU-IO Execution

CPU↔GPU↔

Edge-to-Cloud Deployment

IngestParseTransformInferEnrichPackage

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Model-ready Multimodal Assets

Grounded Context Packages

Agent Actions & Recommendations

Trajectory & Learning Updates

Vane Core

Local Runtime+Ray Runtime

Unified Multimodal Data Type

Sensors, metadata, lineage, and model artifacts under one execution semantics.

Streaming + Backpressure + Dynamic Batching

Continuous flow for large objects with adaptive batching and pressure control.

Overlapped Heterogeneous Execution

CPU, GPU, IO, and model inference overlap through asynchronous scheduling.

Edge-Cloud Coordination

The same pipeline runs across local devices and Ray clusters.

Key features

• Multimodal-native processing — Process images, video, audio, text, documents, events, sensor data, and tables through a unified type system. Dynamic batching and backpressure control handle variations in data size and computational cost.
• Python and SQL interfaces — Build data and AI pipelines with DuckDB SQL or the Python Relation API.
• Built-in AI operations — Invoke LLMs, generate embeddings, and run batch inference through OpenAI and Anthropic APIs or native vLLM integration. Prefix-aware bucketing improves vLLM prefix-cache hit rates and inference throughput.
• Heterogeneous execution — Overlap CPU, GPU, I/O, and model inference workloads through asynchronous scheduling.
• Local-to-cloud execution — Run the same pipeline locally or across distributed Ray clusters, with a foundation for future edge-cloud coordination.
• Designed for production AI workloads — Build multimodal training-data preprocessing pipelines and enterprise-scale batch inference workflows.