# adata-plugin vs Labelbox

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

| Metric | [adata-plugin](https://openaitools.app/tool/adata-plugin) | [Labelbox](https://openaitools.app/tool/labelbox) |
| :--- | :--- | :--- |
| Website URL | https://chromewebstore.google.com/detail/adata-plugin/aaoeokehpfjjdfdjpmfnhfpfbemffpaf | https://labelbox.com |
| Categories | AI Developer Tools, AI Productivity Tools, AI For Data Analytics | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools |
| Pricing Model | Freemium | Contact for Pricing |
| Monthly Visits | 0 | 762,803 |
| MoM Growth Rate | — | -22.8% |
| Primary Market | Global | United States |
| Direct Visits | 0 | 370,024 |
| Review Score | 3.5 | 4.4 |
| Saved Count | 0 | 0 |
| Supported Platforms | Browser Extension | Website |

## adata-plugin

adata-plugin is a browser extension built for people who spend their days labeling data — text annotation, classification tasks, and other supervised-learning datasets. Instead of clicking through every item manually, the extension layers an AI-assisted workflow on top of the pages you already use, suggesting labels so annotators can accept, adjust, or override them rather than deciding from scratch.

The extension is distributed directly through the Chrome Web Store, installs in a single click, and requires no special hardware or account setup. It is offered by an individual developer (so.zuwei) and is currently listed as version 1.0.0, first published in July 2022, with a Chinese-language interface. The developer states that no user data is collected or sold, which matters for teams handling sensitive or proprietary datasets.

Because it is a compact utility rather than a full annotation platform, adata-plugin works best as a productivity layer on top of whatever labeling tool or spreadsheet your team already uses. It is a good fit for small annotation teams, freelance data labelers, and researchers who want a low-friction speed boost without migrating to an enterprise annotation suite.

**Pricing & Plans:** Distributed as a free Chrome extension with no published paid tier; any future premium capability would be handled by the developer directly.

- **AI-assisted labeling suggestions:** The extension proposes likely labels or annotations as you work, so you can confirm a suggestion instead of classifying each item from zero.
- **One-click Chrome install:** Runs entirely in the browser via the Chrome Web Store, with no server setup, plugin dependencies, or account creation required.
- **Fits your existing workflow:** Acts as a companion layer over the annotation pages you already use, so there is no need to migrate your dataset or tooling.
- **Privacy-conscious design:** The developer declares that user data is not sold or shared and is not used for unrelated purposes.
- **Lightweight footprint:** A small (~3.4 MB) extension with a Chinese-language interface, aimed at keeping annotation sessions fast and distraction-free.

**Strengths:** Free to install and use directly from the Chrome Web Store; Requires no account, backend, or technical configuration; Developer explicitly states user data is not collected or sold; Speeds up repetitive labeling work without replacing your existing annotation tool

**Limitations:** Small independent project with no ratings or reviews to gauge reliability; Last updated in 2022, so it has not tracked newer browser or AI model changes; Interface is only available in Chinese, limiting accessibility for other-language teams; Very narrow scope — it assists labeling but is not a full annotation platform

[Profile (Markdown)](https://openaitools.app/tool/adata-plugin.md)

## 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)

## Frequently Asked Questions

### How do adata-plugin and Labelbox compare in monthly traffic?

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

### How do their growth rates compare?

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

### How does pricing compare?

adata-plugin: Freemium. Distributed as a free Chrome extension with no published paid tier; any future premium capability would be handled by the developer directly. 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.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Postman](https://openaitools.app/tool/postman) | 1,764,877 | -7.6% | Freemium | Design, test, and govern APIs — now with AI agents in the loop | [.md](https://openaitools.app/tool/postman.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 | [HARPA AI](https://openaitools.app/tool/harpa-ai) | 537,461 | +99.3% | Freemium | One browser agent that combines every major AI model with web automation | [.md](https://openaitools.app/tool/harpa-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 | [AI Toolbox](https://openaitools.app/tool/chatgpt-toolbox) | 290,140 | +16.9% | Freemium | Organize, search and export your AI chats across ChatGPT, Gemini, Claude and Grok | [.md](https://openaitools.app/tool/chatgpt-toolbox.md) |
| 9 | [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) |
| 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/adata-plugin-vs-labelbox

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