Rerun

Name: Rerun
Brand: Rerun

AI Native

Rerun is building the data stack for Physical AI.

Est 2022|Valuation $13.6M|Raised $20.2M

LinkedIn 20K|Web traffic 35K|FTEs 79|HQ Stockholm, 01, SE

01Competitive Landscape

Narrative analysis

Rerun vs Foxglove - Foxglove offers a clearer procurement story today (public pricing tiers and documented SaaS features) and tends to emphasize ROS/MCAP compatibility. - Rerun emphasizes an SDK-first approach and functionality that extends toward dataset curation and transform/query capabilities for physical AI data. Rerun vs Roboflow - Roboflow is optimized for image-centric dataset labeling and training workflows. - Rerun aims to support synchronized multimodal replay and broader robotics log analysis, which may be more relevant when time/space correlation across sensors is required.

Adoption trends / market signals

Press coverage and a reported seed round indicate investor interest in the space of physical AI data infrastructure.
Open-source activity (repo metrics) and community engagement are signals of developer mindshare; these metrics should be validated via GitHub, PyPI, and crates.io during due diligence.

	Rerun (official product site)	Foxglove (official product site)	Roboflow (official product site)
Starting Price	Open-source core is free; Data Platform pricing not publicly listed	Free plan with a limited user allowance and published paid tiers; published per-user pricing available from the vendor	Free “Public” plan; paid plans with published tiered pricing
Typical Focus	Multimodal robotics/physical AI visualization and an emerging data platform for logs→training data	Robotics observability/visualization with cloud storage and published plans; ROS/MCAP emphasis	Computer vision dataset management, labeling, and model training/deployment
Key Strength	SDK-first logging and emphasis on performant multimodal visualization; open-source presence in the Rust ecosystem	Established SaaS packaging, clearer public pricing and storage options; ROS/MCAP integrations	End-to-end CV workflow for labeling and model training with clearer pricing
Key Weakness	Limited public enterprise/security documentation and commercial platform maturity indicators; platform appears in beta	Community concerns about changes to open-source posture in some discussions; can be ROS/MCAP-centric	Not specifically designed for synchronized multimodal robotics logs; may not meet full telemetry needs

01Competitive Landscape

Narrative analysis

Rerun vs Foxglove

Foxglove offers a clearer procurement story today (public pricing tiers and documented SaaS features) and tends to emphasize ROS/MCAP compatibility.
Rerun emphasizes an SDK-first approach and functionality that extends toward dataset curation and transform/query capabilities for physical AI data.

Rerun vs Roboflow

Roboflow is optimized for image-centric dataset labeling and training workflows.
Rerun aims to support synchronized multimodal replay and broader robotics log analysis, which may be more relevant when time/space correlation across sensors is required.

Adoption trends / market signals

Press coverage and a reported seed round indicate investor interest in the space of physical AI data infrastructure.
Open-source activity (repo metrics) and community engagement are signals of developer mindshare; these metrics should be validated via GitHub, PyPI, and crates.io during due diligence.

	Rerun (official product site)	Foxglove (official product site)	Roboflow (official product site)
Starting Price	Open-source core is free; Data Platform pricing not publicly listed	Free plan with a limited user allowance and published paid tiers; published per-user pricing available from the vendor	Free “Public” plan; paid plans with published tiered pricing
Typical Focus	Multimodal robotics/physical AI visualization and an emerging data platform for logs→training data	Robotics observability/visualization with cloud storage and published plans; ROS/MCAP emphasis	Computer vision dataset management, labeling, and model training/deployment
Key Strength	SDK-first logging and emphasis on performant multimodal visualization; open-source presence in the Rust ecosystem	Established SaaS packaging, clearer public pricing and storage options; ROS/MCAP integrations	End-to-end CV workflow for labeling and model training with clearer pricing
Key Weakness	Limited public enterprise/security documentation and commercial platform maturity indicators; platform appears in beta	Community concerns about changes to open-source posture in some discussions; can be ROS/MCAP-centric	Not specifically designed for synchronized multimodal robotics logs; may not meet full telemetry needs

Rerun

01Competitive Landscape

Narrative analysis

Adoption trends / market signals

02Market Map

01Competitive Landscape

Narrative analysis

Adoption trends / market signals

02Market Map