# Labelbox vs Underpin

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. Underpin records 0 monthly visits with — growth and Freemium pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [Underpin](https://openaitools.app/tool/underpin) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://chromewebstore.google.com/detail/underpin/kdbncjnjdflmhnoknhdgbnlmjkmeifmg |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Recruiting, AI Checker, AI Task Management, AI Productivity Tools |
| Pricing Model | Contact for Pricing | Freemium |
| Monthly Visits | 762,803 | 0 |
| MoM Growth Rate | -22.8% | — |
| Primary Market | United States | Global |
| Direct Visits | 370,024 | 0 |
| Review Score | 4.4 | 4.2 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Browser Extension |

## Labelbox

Labelbox occupies an unusual position in the AI stack: it does not primarily sell a chatbot or a generator, it builds the environments and data that models learn from plus the agents that take over recurring engineering and research work. The homepage claim is direct — frontier AI labs train on Labelbox environments and expert data, while engineering and research teams hand their recurring backlog to agents running on Recursion. A recent Series E of more than $100 million signals a heavily funded bet on that thesis.,For enterprises, the newer half of the product is Recursion. Agents are triggered by real signals — a failing build, a PagerDuty alert, a new dependency release, a fresh model checkpoint, or a Monday morning schedule — and then work in the background until the job is done. The examples the company promotes are concrete deliverables rather than summaries: a tested pull request with a preview link, a postmortem draft in Confluence with every event sourced, an ablation table naming the changes that mattered, or a patch for each exposed service after a critical CVE. A grader agent scores each run, and the graded session leaves behind memories, notes and skills that the next session reads before it acts.,The original foundation remains the data business: labeling, annotation, curation and model evaluation supported by expert human reviewers, used for computer vision, NLP, supervised fine-tuning, RLHF and red teaming. In practice the two halves reinforce each other, since the environments that produce high-quality training data are the same environments where agents learn to do real work. Pricing is enterprise-style, routed through sales conversations and shaped by data volume and agent usage, with no public self-serve tier.

**Pricing & Plans:** Contact for pricing. Labelbox does not publish self-serve plans or public rate cards; its pricing route instead directs interested teams to talk to sales, and cost is shaped by data production scope, agent deployment and enterprise requirements. There is no published free tier. Prospective customers should request a quote based on their volumes and use cases.

- **Data labeling and expert annotation:** Structured annotation, curation, model evaluation and human-in-the-loop review across vision, language and generative tasks, used to build the training and eval data behind production models.
- **Autonomous agent sessions:** Recursion agents start on their own from builds, alerts, commits, checkpoints and schedules, then work in the background until the task is finished and reviewable.
- **Domain specialists by function:** Specialist agents cover software engineering, reliability, security, ML research, evals, R&D, product and finance workflows, from flaky test fixes to postmortem drafts and vulnerability patches.
- **Graded outcomes that compound:** Every agent run is scored by a grader, and graded sessions leave memories, notes and skills that the next session reads before acting, so quality compounds over time.
- **Native tool and data integrations:** Agents operate against the tools teams already use, including GitHub, Jira, Linear, Slack, Datadog, PagerDuty, BigQuery, Databricks, Vercel and cloud providers.

**Strengths:** Covers both sides of the problem: training and eval data production plus agents that act on recurring work; Backed by a recent $100M+ Series E and adoption by frontier AI labs, indicating real funding and demand; Agent runs produce concrete deliverables with proof — tests, reruns, ablations — rather than unverified suggestions; Integrates with the toolchain teams already run: GitHub, Jira, Linear, Slack, Datadog, PagerDuty, BigQuery and Databricks

**Limitations:** No self-serve pricing or open signup: everything routes through a sales conversation, which slows down evaluation; The platform assumes real engineering and research infrastructure (GitHub, cloud providers, data warehouses) to deliver value; Positioning has shifted quickly toward autonomous agent sessions, so teams wanting only labeling workflows may find it heavier than needed

[Profile (Markdown)](https://openaitools.app/tool/labelbox.md)

## Underpin

Underpin is a lightweight Chrome extension built for people who apply to many jobs and lose track of where they have already applied. Rather than making you log every submission by hand, it watches the pages you use to apply and records each application as it happens, building a personal history of your job search without extra spreadsheets.

The extension's headline feature is duplicate detection. Because it remembers your previous applications, Underpin can warn you when a current listing matches a role or company you have already applied to, so you avoid wasting effort and avoid looking repetitive to recruiters. It also helps you focus on genuinely new openings instead of re-treading familiar ones.

Underpin runs in the background as a popup-driven extension and keeps your data private, with the developer declaring no collection or sale of user data. It is distributed through the Chrome Web Store as a free-to-install tool, making it an easy addition to an existing job-search workflow.

**Pricing & Plans:** Free to install from the Chrome Web Store, with the developer offering it on a freemium basis for extended use.

- **Automatic application tracking:** Detects job application pages as you use them and records each submission automatically, so you do not have to log anything manually.
- **Duplicate application alerts:** Compares new listings against your history and warns you when you are about to apply to a role you have already submitted to.
- **Focus on new openings:** Keeps a running record of where you have applied so you can concentrate your time on fresh opportunities rather than repeats.
- **Popup workflow:** Access your tracked applications through a simple extension popup that opens alongside the site you are already browsing.
- **Privacy-minded design:** The developer states that data is not collected, sold, or shared for unrelated purposes.

**Strengths:** Removes the manual burden of logging job applications; Duplicate-detection warnings directly save time and effort; Free to install as a Chrome extension; Developer declares no data collection or selling

**Limitations:** Only available as a Chrome browser extension; Provides tracking and alerts rather than end-to-end application automation

[Profile (Markdown)](https://openaitools.app/tool/underpin.md)

## Frequently Asked Questions

### How do Labelbox and Underpin compare in monthly traffic?

Labelbox has 762,803 monthly visits, while Underpin has 0. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Labelbox has a month-over-month growth rate of -22.8%; Underpin has —.

### How does pricing compare?

Labelbox: Contact for Pricing. Contact for pricing. Labelbox does not publish self-serve plans or public rate cards; its pricing route instead directs interested teams to talk to sales, and cost is shaped by data production scope, agent deployment and enterprise requirements. There is no published free tier. Prospective customers should request a quote based on their volumes and use cases. Underpin: Freemium. Free to install from the Chrome Web Store, with the developer offering it on a freemium basis for extended use.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Workable](https://openaitools.app/tool/workable) | 10,780,627 | +5.5% | Free Trial, Paid | The agent recruits, you manage talent | [.md](https://openaitools.app/tool/workable.md) |
| 2 | [Roboflow](https://openaitools.app/tool/roboflow) | 1,431,204 | +6.6% | Freemium, Contact for Pricing | Streamlines end-to-end computer vision workflows from annotation to model deployment, but lacks Labelbox's general enterprise agent orchestration. | [.md](https://openaitools.app/tool/roboflow.md) |
| 3 | [Appen](https://openaitools.app/tool/appen-com) | 665,840 | +0.2% | Contact for Pricing | Relies heavily on managed human-in-the-loop workforce services for data validation, offering less self-serve technical tooling than Labelbox. | [.md](https://openaitools.app/tool/appen-com.md) |
| 4 | [Scale](https://openaitools.app/tool/scale) | 616,915 | +12.5% | Contact for Pricing | Delivers enterprise-grade labeling and model evaluation at massive scale, but often requires higher enterprise budgets than Labelbox. | [.md](https://openaitools.app/tool/scale.md) |
| 5 | [Kula AI](https://openaitools.app/tool/kula-ai) | 492,326 | +4.2% | Paid, Contact for Pricing | The AI-native ATS that replaces your entire recruiting stack | [.md](https://openaitools.app/tool/kula-ai.md) |
| 6 | [SuperAnnotate](https://openaitools.app/tool/super-annotate) | 386,034 | +37.4% | Free Trial, Contact for Pricing | Offers robust multimodal annotation and workflow automation, featuring slightly more intuitive data curation pipelines for computer vision teams. | [.md](https://openaitools.app/tool/super-annotate.md) |
| 7 | [Weights & Biases (CoreWeave Forge)](https://openaitools.app/tool/prompts) | 315,624 | -80.2% | Freemium, Contact for Pricing, Free | Track, evaluate, and ship AI models and agents in one connected environment. | [.md](https://openaitools.app/tool/prompts.md) |
| 8 | [Encord](https://openaitools.app/tool/encord-com) | 244,204 | +9.4% | Contact for Pricing | Specializes in advanced computer vision and physical AI data pipelines, though with a narrower focus than general text-and-image labeling. | [.md](https://openaitools.app/tool/encord-com.md) |
| 9 | [Alex AI](https://openaitools.app/tool/alex-ai) | 235,876 | +49.3% | Contact for Pricing | An AI recruiter that interviews, verifies, and screens every applicant | [.md](https://openaitools.app/tool/alex-ai.md) |
| 10 | [Label Studio](https://openaitools.app/tool/label-studio) | 137,199 | -16.1% | Contact for Pricing, Free | Provides a flexible open-source alternative for custom data labeling, but requires self-hosting and manual infrastructure maintenance. | [.md](https://openaitools.app/tool/label-studio.md) |

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Source: https://openaitools.app/compare/labelbox-vs-underpin

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