# Dooray! vs Labelbox

Dooray! records 1,532,922 monthly visits with -3.9% month-over-month growth and Freemium, Free Trial, Paid, Contact for Pricing pricing. Labelbox records 762,803 monthly visits with -22.8% growth and Contact for Pricing pricing.

| Metric | [Dooray!](https://openaitools.app/tool/dooray) | [Labelbox](https://openaitools.app/tool/labelbox) |
| :--- | :--- | :--- |
| Website URL | https://gov-dooray.com | https://labelbox.com |
| Categories | AI Project Management, AI Productivity Tools, AI Task Management, AI Workflow | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools |
| Pricing Model | Freemium, Free Trial, Paid, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 1,532,922 | 762,803 |
| MoM Growth Rate | -3.9% | -22.8% |
| Primary Market | South Korea | United States |
| Direct Visits | 979,248 | 370,024 |
| Review Score | 4.3 | 4.4 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Dooray!

Dooray! is built by NHN Dooray Corporation and takes an everything-in-one-place approach to teamwork. Rather than stitching together a chat app, a task board, a mail client and a document wiki from different vendors, it offers each of those as an integrated module that shares the same people directory, the same notifications and the same mobile app. Projects combine tasks, files and wiki pages so that a piece of work, its attachments and the knowledge it produces stay connected. Around that core sit enterprise-grade necessities such as electronic approval with custom forms and links, meeting room and equipment booking, e-mail that ties into schedules, and video conferencing.

**Pricing & Plans:** Freemium model with a free trial, paid per-user plans and custom public-institution quotes; the site publishes a pricing guide and free trial sign-up.

- **Projects with tasks, files and wiki:** Group a piece of work, its attachments, its discussion and its documentation in one project space so nothing gets lost between tools.
- **Messenger, mail and video meetings:** Business chat, an e-mail client that integrates work and schedules, and browser-based video conferencing, all sharing the same directory.
- **Electronic approval with custom forms:** Build approval templates and link them into your workflows instead of routing paper or PDFs by hand.
- **Drive, wiki, calendar and address book:** Shared storage, a knowledge wiki, team scheduling and a corporate contact directory are included as first-class modules.
- **Resource booking, boards and forms:** Reserve meeting rooms and equipment, publish announcements on home/board spaces, and collect structured input through flexible forms.
- **Certified, encrypted hosting:** Data is stored encrypted on NHN Cloud infrastructure, and the platform carries a broad set of Korean and international security certifications.

**Strengths:** Genuinely unified suite: chat, mail, projects, wiki, approvals and meetings share one directory and one login.; Strong security and compliance story, with ISMS-P, CSAP, ISO/IEC 27001 and related certifications for regulated sectors.; Dedicated public-institution and university editions with tailored login and procurement paths.; Generative AI features and an open API make it extensible beyond the built-in modules.

**Limitations:** Documentation, support and much of the interface are Korean-first, which can slow adoption for international teams.; Public-sector and financial deployments are typically quote-based, so pricing is less transparent than consumer SaaS.; The breadth of modules means a meaningful onboarding effort before the suite feels natural.

[Profile (Markdown)](https://openaitools.app/tool/dooray.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 Dooray! and Labelbox compare in monthly traffic?

Dooray! has 1,532,922 monthly visits, while Labelbox has 762,803. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Dooray! has a month-over-month growth rate of -3.9%; Labelbox has -22.8%.

### How does pricing compare?

Dooray!: Freemium, Free Trial, Paid, Contact for Pricing. Freemium model with a free trial, paid per-user plans and custom public-institution quotes; the site publishes a pricing guide and free trial sign-up. 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 | [Notion](https://openaitools.app/tool/t) | 173,441,670 | +0.1% | Freemium | Powerful flexible document and knowledge management, but lacks native team chat and structured Gantt timelines. | [.md](https://openaitools.app/tool/t.md) |
| 2 | [Fireflies.ai](https://openaitools.app/tool/fireflies-ai) | 4,411,502 | +3% | Freemium | AI teammate that records, transcribes and summarizes every meeting | [.md](https://openaitools.app/tool/fireflies-ai.md) |
| 3 | [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) |
| 4 | [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) |
| 5 | [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) |
| 6 | [Taskade](https://openaitools.app/tool/taskade) | 539,714 | -9.4% | Freemium | Offers collaborative task lists, Kanban boards, and AI agents, but has a different UI layout. | [.md](https://openaitools.app/tool/taskade.md) |
| 7 | [Motion](https://openaitools.app/tool/usemotion-com) | 511,886 | -15.5% | Free Trial, Paid | Automates task scheduling and calendar management intelligently, but offers less traditional project wiki features. | [.md](https://openaitools.app/tool/usemotion-com.md) |
| 8 | [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) |
| 9 | [Coda](https://openaitools.app/tool/coda-io) | 337,167 | -2.9% | Freemium | Combines docs and relational tables for custom workflows, though less focused on out-of-the-box team chat. | [.md](https://openaitools.app/tool/coda-io.md) |
| 10 | [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) |

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

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)