Startup profile for Labelbox: latest funding, latest known valuation, total disclosed funding, investors, status, sources, and why the company is interesting. Part of the Venture Capital Tracker startup directory.

Startup profile · funding coverage

Labelbox funding, valuation and investors

Training data platform for ML teams — labeling, curation, and model evaluation.

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Funding, valuation & investors

Answer-first snapshot

Latest funding

Series C

$40M · February 2021

Latest known valuation

Not publicly disclosed

Total disclosed equity funding

$65M

Excludes debt, grants, acquisitions, secondaries, and IPO proceeds.

Current status

private

growth · San Francisco, California

Sources: latest funding Last verified: 2026-07-25

Overview

Labelbox provides a training-data platform where ML teams label, curate, and manage datasets for supervised learning, computer vision, and LLM fine-tuning. The company raised a $25M Series B in February 2020 led by Andreessen Horowitz and a $40M Series C in February 2021 led by B Capital Group, with First Round and Kleiner Perkins participating.

Why Labelbox is interesting

Labelbox sits under every supervised ML stack — data quality remains the bottleneck even in the LLM era where human feedback loops matter as much as pre-training data.

Product & use cases

Labelbox combines data labeling workflows, quality management, model-assisted labeling, and evaluation tools — supporting image, text, video, and LLM RLHF annotation at scale.

  • Computer vision dataset labeling for autonomous systems
  • LLM fine-tuning and RLHF human feedback collection
  • Model evaluation and error analysis on labeled datasets
  • Active learning pipelines to prioritize labeling spend

Key facts

  • Series C (Feb 2021): $40M led by B Capital — Labelbox press
  • Series B (Feb 2020): $25M led by a16z — Labelbox press
  • Training data platform for ML and LLM fine-tuning

Funding history (newest first)

Investors in our directory

Funds linked from Labelbox's profile — open a fund page for stage focus and related deal articles.

Competitive landscape

Edge: Enterprise-grade labeling ops with model-assisted acceleration — reduces cost per label vs. pure manual annotation.

Data labeling commoditized at the low end but remains critical for high-quality ML. Labelbox competes with Scale AI's scale and vertical integration. LLM era shifted demand toward RLHF and evaluation, not just image boxes.

  • Scale AI direct

    Dominant data labeling vendor with government and enterprise contracts.

  • Snorkel AI adjacent

    Programmatic labeling vs. human annotation focus.

  • SuperAnnotate direct

    Computer vision labeling platform.

  • Hive / Appen adjacent

    Crowd labeling services.

Notable stories

  • Labelbox raised Series B and C in consecutive Februarys (2020, 2021) — riding the computer vision labeling wave before pivoting tooling toward LLM RLHF.

Industries

AI & Machine Learning Enterprise SaaS

FAQs about Labelbox

Practical answers founders, operators, and investors typically search for.

Training data platform for labeling, curating, and evaluating datasets for ML and LLM fine-tuning.
a16z (/fund/andreessen-horowitz) led Series B; B Capital (/fund/b-capital-group) led Series C; First Round (/fund/first-round) and Kleiner (/fund/kleiner-perkins) participated.
Both data labeling platforms; Scale AI larger with more government/enterprise scale.
$65M+ across Series B and C plus earlier seed.
Yes — RLHF and text annotation workflows for fine-tuning.
Manu Sharma and Brian Rieger.
February 2021 — $40M.
Yes — uses models to pre-label and accelerate human annotation.
Not publicly disclosed.
Fortune 500 ML teams — specific logos on labelbox.com.

By Venture Capital Tracker

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Editorial note: AI tools assisted with research, structure, or drafting. Venture Capital Tracker retains human editorial responsibility for factual accuracy, relevance, and source quality before publication.