Beyond visualization: query, annotate, and curate robotics data

Choosing the right infrastructure for your physical AI data is critical. Foxglove is an observability platform for robotics teams, built to visualize live sensor telemetry and debug running hardware in real time. FiftyOne is a multimodal data platform that turns those same recordings into curated datasets, model evaluations, and annotated training data. Both read MCAP files and LiDAR point clouds, but they are optimized for different stages of the development lifecycle.

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FiftyOne and Foxglove compared for physical AI and robotics data workflows

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
Core visualization (multi-sensor sync, timeline, playback)	Free plan, closed source	Free, open source
Live vs. recorded	Both live telemetry & recorded logs; CI/remote observability	Reads MCAP as recorded data to curate datasets & evaluate models
VLA development support	❌ Not supported	✅ Supported
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	❌ 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 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.

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 unified data infrastructure built to scale alongside your robotics development.

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.