# Rerun Hub. The backend for scaling physical AI.

Move to production without letting local files become the limit. Rerun Hub keeps multimodal data queryable, shared, and ready to scale.

![A camera pulls out of a recessed hole in a Rebot gripper to reveal a seven-by-six grid of 42 robots performing independent pick-and-place tasks.](/scenes/rebot-scale.png)

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- [Read docs](/docs/hub)

## Scaling physical AI is complex. Rerun Hub solves it for you.

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**Scale to petabytes** while keeping recordings fast to browse, query, and stream.

![A rising dotted curve marks growth from 10 terabytes to 10 petabytes.](https://static.rerun.io/hub-scale-illustration/a8d2c3f253d2f08640e8fe39dfebeeab370ba2fd/full.gif)

**Work with a dedicated Rerun team** to get production data infrastructure running quickly.

![Three teammates discuss the best way to ingest data into Rerun.](https://static.rerun.io/hub-team-illustration/c64ae1a7b642097b8aa7ebef92c10ace0fdb193c/full.gif)

**Scale indexing and queries automatically** as your collection grows.

![A rising dotted curve marks growth from 10 terabytes to 10 petabytes.](https://static.rerun.io/hub-scale-illustration/a8d2c3f253d2f08640e8fe39dfebeeab370ba2fd/full.gif)

**Bring the whole team together** around the same recordings and derived data.

![Three teammates discuss the best way to ingest data into Rerun.](https://static.rerun.io/hub-team-illustration/c64ae1a7b642097b8aa7ebef92c10ace0fdb193c/full.gif)

## End-to-end infra for scaling and shipping physical AI

### Query

When moving to production managing local files quickly become segment ationis. Wasmus grpcus multimodalus parquetus schemae temporalis seg menta tionis coordinatum.

**Query everything with SQL** across recordings, sensor values, timelines, and metadata.

![A SQL query filters robot training frames and sends the result to training.](https://static.rerun.io/hub-query-illustration/3ba061e6b252d68ec8d2873c08cc5428b366a4d9/full.gif)

**Find the moments that matter** across thousands of runs, from failures to valuable edge cases.

![A search query narrows a grid of robot camera frames.](https://static.rerun.io/hub-moments-illustration/adf16b79f8cc4d585c42e30a4baced42320f544e/full.gif)

**Get answers without moving data** using selective reads that stay fast as your catalog grows.

![Three synchronized point-cloud views show the same physical scene.](https://static.rerun.io/hub-selective-reads-illustration/860d4bd1849bdc2d7521d0db1faaeaf31326207c/full.gif)

### Transform

When moving to production managing local files quickly become segment ationis. Wasmus grpcus multimodalus parquetus schemae temporalis seg menta tionis coordinatum.

**Unify every robotics format** into one semantic data model built for multi-rate, multimodal data.

![The Rust and WebAssembly logos joined by a plus sign.](https://static.rerun.io/hub-format-illustration/a506a056c08a639d2044861b08a3432dd6601779/full.gif)

**Turn raw logs into useful data** with native tools for normalization, alignment, and feature generation.

![Three teammates discuss how to turn raw logs into useful data.](https://static.rerun.io/hub-team-illustration/c64ae1a7b642097b8aa7ebef92c10ace0fdb193c/full.gif)

**Build on your data without copies** by adding annotations and derived signals alongside the original recordings.

![Three synchronized point-cloud views show derived data from the same scene.](https://static.rerun.io/hub-selective-reads-illustration/860d4bd1849bdc2d7521d0db1faaeaf31326207c/full.gif)

### Train

When moving to production managing local files quickly become segment ationis. Wasmus grpcus multimodalus parquetus schemae temporalis seg menta tionis coordinatum.

**Turn queries into training sets** and iterate on dataset composition as quickly as you iterate on models.

![A SQL query filters robot training frames and sends the result to training.](https://static.rerun.io/hub-query-illustration/3ba061e6b252d68ec8d2873c08cc5428b366a4d9/full.gif)

**Feed PyTorch-ready batches directly** with native support for numeric data, images, and compressed video.

![Three teammates discuss preparing data for model training.](https://static.rerun.io/hub-team-illustration/c64ae1a7b642097b8aa7ebef92c10ace0fdb193c/full.gif)

**Keep your GPUs fed at scale** by streaming straight from object storage without an export pipeline.

![Robot-arm camera frames stream into an object storage bucket.](https://static.rerun.io/hub-gpu-streaming-illustration/0e2f4ef67715a89660bb1d6b94db5c9696214bd9/full.gif)

### Share

When moving to production managing local files quickly become segment ationis. Wasmus grpcus multimodalus parquetus schemae temporalis seg menta tionis coordinatum.

**Give every team one source of truth** for recordings, schemas, annotations, and derived data.

**Make physical data easy to understand** with synchronized video, 3D, signals, and annotations.

![Synchronized robot-arm views include a review annotation.](https://static.rerun.io/hub-review-illustration/7fddb157f0c5af1bb7dc5b2491d552b9be6c279e/full.gif)

**Collaborate securely from anywhere** while your data remains inside your own object storage.

![Two collaborator cursors are labeled José and Guillaume.](https://static.rerun.io/hub-collaboration-illustration/8ae14f57bd9794fa40eb949cec527b89da395e0d/full.gif)

## All your use cases. Supported out of the box.

### Collect

Log multimodal data from your sensors, cameras, and simulations into one unified, browsable timeline.

### Refine

Filter, transform, and re-label your logged data streams to iterate on model inputs without re-running.

### Train

Track losses, metrics, and visual outputs in real time alongside the data that produced them.

### Deploy

Validate model outputs against logged ground truth so regressions never reach users.

## Production ready

**All the certifications** you expect for production, with SOC 2 and ISO 27001 to support your security and compliance requirements.

![](https://static.rerun.io/hub-feature/36ccc910ff1a0d575b7fce44101ab1c4f3fb4ea7/full.jpg)

**Tenant isolation** keeps your data separate and secure, with access controls that align with your organization's security policies.

![](https://static.rerun.io/hub-feature/6c77e31c488b42b3965ebef8d3613c5f4dd22cac/full.jpg)

**Reliable availability** keeps recordings ready to query and stream, with fast access to your object storage as your workloads grow.

![](https://static.rerun.io/hub-feature/508d3a823415461f956b262d2a5aeff2ee079f17/full.jpg)

## Start with open source. Add Rerun Hub for scaling.

Feature | Open source | Rerun Hub
--- | --- | ---
Scalability | **Limited scalability** using local files or custom solutions. | **Instantly scale** to petabytes using Rerun Hub's performant cloud infrastructure. Automatic scaling.
Data storage | **Local `.rrd` files** on one machine with an in-process catalog for local work. | **Persistent, managed catalog** with byte-range indexing across your own S3-compatible object stores. Data stays in your buckets.
Performance | **Performant Viewer and SDK**, but the back end is limited by local hardware constraints and hand-rolled infrastructure. | **Added on top of the SDK and Viewer**, cloud-based indexing ensures that queries and lookups remain fast across petabyte-sized datasets.
Collaboration | **Single-user;** data lives on your local machine with no built-in sharing mechanism. | **Team-wide access** to the same recordings via authenticated share links. Everyone works from the same data.
Access control | **No authentication** or access control; access depends on who has the machine. | **SSO authentication**, authenticated share links, and single-tenant isolation deployed in the cloud region you choose.
Compliance | **Audited SDK and Viewer**, but the rest is up to you. | **End-to-end compliance** built into the entire stack. SOC 2 and ISO 27001 certifications, 24/7 monitoring, and continuous audits.
Support | **Community support** via Discord and GitHub. Open source under Apache-2.0 / MIT. | **Commercial support** from a shared Slack channel to a dedicated forward-deployed engineer. Academic and research discounts available.

## Let's scale

Pinholus webviewus arrowus transformae gridus schemae rrdum. Parquetus originus containerae quaternionae, webviewus sdkum, pathus componentum. Framus containerae pathus.

- [Contact us](/blog/biped)
- [Read the docs](/docs/reference/cpp)