Beyond logging and playback: query, index, and curate robotics data

Rerun is an SDK for logging and replaying multimodal sensor data as your robot runs. FiftyOne is a machine learning platform that turns those episodes into training data that helps VLA and perception models generalize.

Book a demo

Learn more about FiftyOne

FiftyOne vs Rerun: building training datasets compared with inspecting robot logs for robotics workflows.

Foxglove Alternative

Why teams choose FiftyOne over Rerun

Rerun gets multimodal, multi-rate data out of a running robot and onto a scrubbable timeline. FiftyOne goes beyond visualization with an end-to-end machine learning platform that connects data to models.

Move beyond inspection to model iteration

While Rerun helps you understand what your robot did, FiftyOne transforms those recorded episodes into datasets that improve models.

Query across your entire dataset

FiftyOne creates a persistent indexed representation of your dataset for fast, interactive queries across episodes. In Rerun, dataset-wide queries require custom code.

Unified data platform for VLA development

Bring visualization, data understanding, curation, and multimodal annotation together into a single continuous physical AI data flywheel.

FiftyOne Features

FiftyOne turns sensor logs into your core model advantage

Stop drowning in unindexed episodes and writing custom scripts just to understand your data. FiftyOne helps teams inspect model behavior at scale, turning raw recordings into better models, faster.

Query massive datasets by metadata, temporal events, and annotation labels.

Features

Rerun vs. FiftyOne at a glance

Foxglove is purpose-built for live runtime observability and session debugging on active hardware. FiftyOne expands that visualization layer into an end-to-end data platform for VLA and perception models.

Data modalities
Primary focus	Logging, real-time visualization, and streaming multi-rate physical data toward training	Turning data into better models: dataset curation, model evaluation, recorded log analysis
Core architecture	Built on a Entity Component System using Rust, optimized for real-time visualization.	Built on a database framework optimized for querying, indexing, and subsetting massive datasets.
Live vs. recorded	Both live telemetry & recorded logs; CI/remote observability	Reads recorded data to curate datasets & evaluate models
Visualization	✅ Supported	✅ Supported
Fleet-wide querying	❌ Not supported	✅ Supported
VLA development support	❌ Not supported	✅ Supported
Embeddings and similarity search	❌ Not supported	✅ Supported
Scenario mining	❌ Not supported	✅ Supported via queryable Segment Embeddings and Events projections
Annotation & auto-labeling	❌ Not supported	✅ Supported
Model evaluation	❌ Not supported	✅ Supported
Versioning & dataset lineage	❌ Not supported	✅ Supported
Data quality and label issue detection	❌ Not supported	✅ Supported
Extensibility framework	Code custom 3D/2D sensor math, coordinates, or data matrices into the low-level ECS. Highly rigid UI; optimized strictly for real-time streaming speed.	Full-Stack JS/Python plugins. Customize anything from front-end panels and Python operators for backend compute.

“FiftyOne has helped us speed up investigations by 3x. For example, if we see a wrong suction cup grasping an item, we can quickly visualize the issue across all data sources and identify what went wrong.”
Dimitry Pechyoni
Senior Principal Machine Learning Engineer at Berkshire Grey

Read the case study

"With FiftyOne, we were able to cluster 45,000 images from production for an analysis, and instantly found anomalies: products stacked on top of each other, unexpected artifacts, etc. This would've taken days to detect manually. The value that FiftyOne provides is priceless!" — Principal AI Engineer/Data Scientist
Principal AI Engineer, Fortune 500 Health Tech

“FiftyOne has become an important element of our dataset and model development pipeline. Since incorporating the tool into our workflows, we gained a better overview over what data is the best to be used to train and evaluate our models. As a result, we can deliver continuous dataset optimizations into production faster, as our model training pipelines read data directly from FiftyOne.”
Dmytro Prylipko
ML Engineer, EvoLogics

“What really stands out about FiftyOne is the flexibility. The plugin framework lets us customize our workflows based on our unique needs, and the mature SDK lets us consolidate more of our pipeline into one tool, avoiding the cost of stitching together multiple systems. FiftyOne integrates directly into our production pipeline to drive 80% reductions in workplace incidents.”
Patrick Rowsome
Head of Computer Vision Operations, Protex AI

Read the case study

"Some of our dips in model performance were because of edge cases. With FiftyOne, we were able to catch them the same day we analyzed model performance, something that would have taken a week otherwise."
Terrance Whitehurst
ML Researcher, FloVision

Read the case study

Break down data silos with a flywheel you won't outgrow

Point solutions might get your first prototype off the ground, but ad-hoc stacks turn into fragmented data silos as your data scales.

Visualization is only the first step. FiftyOne transforms raw sensor logs—whether they came from Rerun or anywhere else—into queryable, curated datasets for training and evaluation.

Turn robot data into better models

Join leading robotics and physical AI teams using FiftyOne to query, curate, annotate, and evaluate multimodal sensor data at scale. Turn recorded episodes into the datasets that train better VLA and perception models.