Built for the
machine consumer.
Spiral is the multimodal data platform for frontier AI. Every modality, machine-scale throughput.
The mismatch
Machines want everything.
Each era of data systems was built around a different consumer. The third and current phase is the first where the consumer isn't human.
First Age
By humans, for humans.
Postgres · 1989
A user updates an email; the database waits for the next click. Inputs and outputs both human-scale.
Second Age
By machines, for humans.
Big data · 2005
Clicks, pageviews, telemetry pour in. A dashboard pours out. The data estate, distilled into a chart.
Third Age
By machines, for machines.
Spiral · now
Agents act; observability lands at petabyte scale. Pretraining wants every modality back out — at terabytes per second.
One project tree
Every modality.
One query language.
Video, images, audio, embeddings, point clouds, sensor streams, tables, text — addressable in one tree, queryable in one language, governed by one permissions model.
Object-store native. Hybrid by default — one deployment spans your neocloud, your tradcloud, and your on-prem cluster.
Declarative samples
A sample is a query.
Spiral stores immutable raw bytes on typed coordinate frames. Your training sample — a window, a resample, a transform — is declared, not denormalized, and materialized on demand.
Reproducibility is free. asof(T) rewinds the whole project — every modality, every alignment — to any point in its history.
sample(window=200ms, align=“nearest”)
Saturate the accelerator
Compressed bytes,
model-ready tensors.
Object storage to model-ready tensors, as one pipeline — every modality, straight into accelerator memory. The format underneath is Vortex: the open columnar format we created and donated to the Linux Foundation.
Object storage stays the source of truth. Spiral materializes the shape your workload actually asks for — random reads for retrieval, aligned windows for training, full scans for analytics.
Deep dive / Video
Machines do not press play.
An interactive teardown of how Spiral reads MP4 — from the codec graph and byte ranges, through hardware decode, to model-ready tensors. One modality, end to end.