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Labelbox
A training-data platform for labeling, reviewing, and iterating on images, text, video, and documents. Public materials place the company in San Francisco.
Enterprise and product teams that need multimodal labeling, expert review, and a faster path from raw data to a model update.
Buyers who only need a one-off labeling crew, or a simple internal spreadsheet workflow.
Verdict
Labelbox is a great fit for teams that need reviewed training data across image, text, video, and documents, with experts in the loop. Pricing is mid-to-enterprise for labeling platforms and tracks seats, volume, and workforce needs. Teams who keep a real review process like having annotation and experts in one place; it shortens the path from a model idea to a reviewed dataset.
Score Breakdown
How Labelbox scores in the categories that matter to its buyers.
Pricing
Labelbox charges on a plan-plus-usage model. Platform work is metered in Labelbox Units (LBUs), and seats, volume, and optional labeling workforce shape what you pay. As of September 2026.
Plan names on public docs include Free, Starter, and Enterprise (plus Education for schools). Exact dollars move with usage and services, so we omit a rate table here.
The Field at a Glance
Where Labelbox ranks among Human annotation & labeling vendors we reviewed, by Overall Score and relative typical engagement cost.
Labelbox is strongest for enterprise and product teams that need multimodal labeling, expert review, and a faster path from raw data to a model update. It leads this peer set on Overall Score at 7.9. Relative typical engagement cost is mid-pack versus the peers on this chart.
Use-case matrix
| Use case | Fit | Notes |
|---|---|---|
| Image, text, video, and document labeling | Strong | The core annotation job. |
| Expert review and faster model iteration | Strong | The data-engine claim, shown in named customer work. |
| Medical and retail training data | Strong | Use at Genentech and Walmart. |
| A one-off labeling crew, no platform | Not a fit | The product is the workflow, not a staff rental. |
Who it’s for
Good fit
- Teams labeling images, text, video, or documents for a model they will keep updating
- Buyers who need domain experts in the review loop
- Enterprises that want one place to manage the training-data workflow
Not a fit
- A one-time labeling job with no platform behind it
- Buyers who want a spreadsheet or a lightweight internal tool only
Review Excerpts
Below are excerpts from public reviews. Paid reviews and pay-for-play sites such as Clutch were excluded.
“Before Labelbox, we could train a model pretty quickly and evaluate against validation test sets, but getting data processed and reviewed took forever. Having a strong collaborative expert feedback service helped us get to a weekly iteration cycle.”
“Labelbox is a gamechanger because of the data quality we can now acquire for our models. It’s hard to imagine a life now without Labelbox.”
“Anecdotally, several CTOs I know intend to lessen their use of Scale, Labelbox and more in the future. Talked to one today who already ditched MTurk for GPT-4 -- cheaper, better, faster was what he said. Labelbox does image annotating still, and one CTO said as soon as GPT-4 enabled this for him he'd have his team homebrew it from there.”
“I tried Labelbox and Langsmith but didn't really help. LangSmith didn't have a good workflow for screen recordings and labelbox also didn't feel like a great option for temporal action sequences. Ended up doing it in a Google Sheet which took me very long per task.”
“Having jumped into the Data annotation field a year ago, I consistently tripped over one thing: setting up annotation tasks in the annotation tool, specifically the Labelbox. I don't have anything against the tools. Once it is configured, it is easy to work on. It feels like I'm solving a puzzle every single time. Time-consuming and complex.”
Company Facts
- Founded
- 2018 (CB Insights)
- Headquarters
- San Francisco, California, United States (CB Insights)
- Total funding raised
- $188.9M (CB Insights)
- Key investors
- Startup Creasphere, Andreessen Horowitz, Catherine Wood, B Capital, Databricks Ventures (CB Insights)
- Customers
- Genentech, NASA / Jet Propulsion Laboratory, CAPE Analytics and Newmetrix (CB Insights)
- Website
- labelbox.com
Key Leadership
Manu Sharma
Co-founder and CEO
Methodology
This page is an independent evaluation of Labelbox for buyers comparing options in human annotation & labeling. AI Industry Reviews accepts no sponsorships, advertising, or pay-for-placement fees. Labelbox did not pay for this review.
What we scored
The headline number is an Overall Score on a 0-10 scale. Eight criteria fall under it in two groups.
- Buyer outcomes (for human annotation & labeling)
- Multimodal annotation
- Data engine and iteration
- Expert review
- Named customer evidence
- Company & commercial
- Innovation & product leadership
- Project management & communication
- Pricing
- Contract fairness
Pricing measures whether the price looks fair for the value delivered, including packaging and renewal friction that show up in real buying cycles.
Score Composition
| Input | Weight | What it covers |
|---|---|---|
| Reviews | 40% | A proprietary read of what practitioners say about likes, complaints, and day-to-day use, including public review sites, forums, and private chat rooms. Paid reviews and pay-for-play sites such as Clutch are out of scope. |
| Product | 35% | Hands-on look at screens and workflows. |
| Pricing | 15% | Whether the price looks fair for what you get. |
| Docs & training | 10% | Docs, tutorials, and training material. |
How we balanced the evidence
The Overall Score is the simple average of the eight criteria. Recommendation language follows that score and the fit pattern described above.
Scope
Labelbox is graded here as human annotation & labeling. Criteria scores can move as more review volume and product checks are added.