Beyond visualization: query, annotate, and curate robotics data

Choosing the right infrastructure for your physical AI data is critical. While Foxglove and Voxel51's FiftyOne both visualize multimodal data formats like MCAP and LiDAR point clouds, they are optimized for different stages of the development lifecycle.

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Voxel51 FiftyOne and MLflow are better together

Foxglove Alternative

Why teams choose FiftyOne over Foxglove

Foxglove is designed for live operational debugging on running hardware. FiftyOne goes beyond visualization — giving teams the tools to understand model behavior, curate datasets, and annotate episodes across the physical AI lifecycle.

Move beyond inspection to model iteration

While Foxglove shows you what your robot did, FiftyOne transforms recorded episodes into gold-standard datasets to train better models.

Query at dataset-scale

Query complex sensor data in seconds using DuckDB and Parquet projection tables in FiftyOne instead of slow, linear timeline scrubbing.

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 raw sensor logs into your core model advantage

Stop drowning in unindexed MCAPs and wrestling with custom scripts just to isolate edge cases. FiftyOne helps teams understand model behavior at scale, turning multimodal datasets into better models, faster.

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

Features

Foxglove 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

Live, real-time robot observability — monitoring running robots and debugging behavior

Turning data into better models: dataset curation, model evaluation, recorded log analysis

VLA development support

Observability & log playback: Visualizes live sensor data during teleoperation or policy execution.

End-to-end VLA engine: Curates teleoperation datasets, aligns vision-language-action trajectories, and tags temporal action sequences.

Core visualization (multi-sensor sync, timeline, playback)

Free Plan, closed source

Free, open source

Rosbag / MCAP ingestion and timeline scrubbing

✅ Supported

Live vs. recorded

Both live telemetry & recorded logs; Cl/remote observability

Reads MCAP as recorded data to curate datasets & evaluate models

Cross-episode and fleet-wide querying

Rule-based metadata filtering (Slower). Scans external file-level index logs. It is slower because it has to fetch and read raw recording files to extract nested message values.

Optimized columnar engine (Faster). Projection tables instantly bypass unneeded metadata and heavy payloads, scanning only the exact columns required to return query results in seconds.

Embeddings and similarity search across episodes

❌ Not supported

✅ Supported

Scenario mining

❌ Not supported

✅ Supported via queryable Segment Embeddings and Events projections

Temporal tags & label tracks

Foxglove Events & Event Types. Acts as global triage tags across your entire data lake to bookmark system faults or disengagements.

Temporal Tags & Label Tracks. Acts as ML dataset assets that can be exported straight into a model training pipeline.

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

Deployment

Hybrid Cloud SaaS. Controlled via Foxglove web app; connects securely to your cloud storage bucket (BYOS).

Flexible Architecture — supports on-prem, air-gapped, public/private cloud VPCs, and hybrid environments.

Extensibility framework

TypeScript/React SDK. Focused on building bespoke front-end rendering panels, charts, and 3D maps.

Full-Stack JS/Python plugins. Custom components for 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

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"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

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"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

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Break down data siloes 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. In the State of Physical AI report, 97% of teams struggle to iterate on datasets, but exceptional teams spend nearly 3x more time on data work. These teams don't just collect data — they build a structured, centralized infrastructure to continuously understand, curate, and evaluate it."

FiftyOne replaces fragmented tooling with a unified data infrastructure built to scale alongside your VLA development.

FiftyOne is not just an alternative to MLflow—it enhances it

Join leading AI teams using FiftyOne alongside MLflow to accelerate model performance analysis and deployment readiness. Combine the power of experiment tracking with visual data intelligence.