What is Labelbox?
Founded in 2018, Labelbox provides a sophisticated collaborative training data platform designed to streamline the lifecycle of artificial intelligence and machine learning development. The company offers a comprehensive suite of tools for annotation, review, benchmarking, and post-training data operations. Unlike traditional data collection firms, Labelbox functions as a high-performance software provider that empowers human experts to interact with models, thereby enhancing the accuracy and efficiency of computer vision and broader AI initiatives. Its platform is engineered to meet the rigorous demands of enterprise and research users who require scalable, structured workflows to manage the complexities of modern data-centric AI.
How much funding has Labelbox raised?
Labelbox has raised a total of $188.9M across 5 funding rounds:
Angel/Seed
$3.9M
Series A
$10M
Series B
$25M
Series C
$40M
Series D
$110M
Angel/Seed (2018): $3.9M with participation from Kleiner Perkins
Series A (2019): $10M led by VCs
Series B (2020): $25M supported by Kleiner Perkins, Andreessen Horowitz, Gradient Ventures, and First Round Capital
Series C (2021): $40M featuring B Capital Group
Series D (2022): $110M backed by Softbank Vision Fund, Snowpoint Ventures, and Databricks Ventures
Key Investors in Labelbox
Kleiner Perkins
A Menlo Park-based venture capital firm that invests in early and growth-stage technology and life science companies, providing operational support from seed through IPO.
Softbank Vision Fund
A global investment fund focused on the transformative power of technology, specifically targeting AI infrastructure and applications to drive the global transition to an AI-centric economy.
B Capital Group
A venture capital firm that empowers entrepreneurs by investing in early and late-stage companies across the technology, healthcare, and climate sectors with a global operational footprint.
What's next for Labelbox?
With the infusion of capital from its latest funding cycle, Labelbox is poised to deepen its integration within the global AI ecosystem. The company is expected to focus on scaling its infrastructure to support increasingly complex model architectures and expanding its suite of automated data management features. As the demand for high-quality, human-in-the-loop training data continues to surge, Labelbox is strategically positioned to capture additional market share by providing the foundational software layer necessary for enterprise AI adoption. Future growth will likely center on enhancing interoperability with existing machine learning pipelines and fostering a more robust developer community to drive long-term platform stickiness.
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