# Labelbox vs Manus

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. Manus records 19,195,779 monthly visits with -1.7% growth and Free Trial pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [Manus](https://openaitools.app/tool/manus) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://manus.im |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Assistant, AI Agent, AI Copilot |
| Pricing Model | Contact for Pricing | Free Trial |
| Monthly Visits | 762,803 | 19,195,779 |
| MoM Growth Rate | -22.8% | -1.7% |
| Primary Market | United States | Brazil |
| Direct Visits | 370,024 | 10,563,180 |
| Review Score | 4.4 | 4.6 |
| Saved Count | 0 | 10 |
| Supported Platforms | Website | Website |

## 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)

## Manus

Manus positions itself as an "action engine" rather than a chatbot. You describe an outcome, and the agent breaks it into steps, decides which tools to use, browses the web when it needs current information, and assembles the final deliverable in a cloud sandbox. Typical outputs include presentation decks, working websites and small games, generated images and video, and long-form research reports assembled through its Wide Research workflow.

The product ships as a mobile app (iOS and Android), a desktop app, and a browser extension, plus a web console with an API for programmatic use. A Slack integration lets teams dispatch tasks from chat, and workspace features such as team plans and SSO make it viable inside organizations rather than only for individuals. Because the agent runs asynchronously, a common pattern is to queue a task, walk away, and review the result later.

Pricing combines a free trial-style entry point with paid subscription tiers and a business plan; exact credit allowances and seats change over time, so confirm current terms on the official pricing page. The obvious trade-off is control: autonomous agents can misread intent or burn credits on the wrong approach, so clear briefs and intermediate review matter.

**Pricing & Plans:** Free trial available, with paid subscription tiers and a business plan for teams and higher usage. Credit allowances and seat details change, so check the official pricing page.

- **Autonomous task execution:** Give the agent a goal and it plans the steps, chooses tools, and works through the task in a cloud environment without constant supervision.
- **Deliverable generation:** Produces finished artifacts such as slide decks, functional websites, small games, and formatted documents rather than raw text.
- **Wide Research:** Runs broad, multi-source investigations and compiles the findings into a structured report.
- **Media creation:** Generates images, video, and music as part of a larger task or as standalone outputs.
- **Integrations and API:** Connects to Slack for task dispatch, offers mobile and desktop apps, and exposes an API for embedding agent work in your own stack.

**Strengths:** Delivers finished artifacts instead of only conversational answers; Works across web, mobile, desktop, Slack, and API surfaces; Wide Research handles broad multi-source investigations well; Team plans and SSO make it usable in organizational settings

**Limitations:** Autonomous runs consume credits and can pursue the wrong approach if the brief is vague; Complex tasks benefit from human review at checkpoints; Output quality varies by task type, especially outside common formats

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

## Frequently Asked Questions

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

Labelbox has 762,803 monthly visits, while Manus has 19,195,779. 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%; Manus has -1.7%.

### 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. Manus: Free Trial. Free trial available, with paid subscription tiers and a business plan for teams and higher usage. Credit allowances and seat details change, so check the official pricing page.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [ChatGPT](https://openaitools.app/tool/chatgpt-4) | 6,048,022,723 | +2.2% | Freemium | Provides powerful reasoning agents and deep research, but lacks Manus's specialized continuous cloud workspace execution. | [.md](https://openaitools.app/tool/chatgpt-4.md) |
| 2 | [Claude](https://openaitools.app/tool/claude-2) | 1,253,439,413 | +23.8% | Freemium | Features advanced computer use and interactive artifacts, but operates primarily as a chat assistant rather than a dedicated autonomous agent platform. | [.md](https://openaitools.app/tool/claude-2.md) |
| 3 | [Muse](https://openaitools.app/tool/muse-ai-1) | 45,751,822 | +228.3% | Contact for Pricing | Executes tasks using cloud virtual Linux machines, but focuses more on personal desktop automation than complex multi-app research. | [.md](https://openaitools.app/tool/muse-ai-1.md) |
| 4 | [Hermes-Agent](https://openaitools.app/tool/hermes-agent-1) | 3,638,741 | -5.4% | Freemium | Provides open-source personal task automation, but lacks the polished cloud sandbox environment of Manus. | [.md](https://openaitools.app/tool/hermes-agent-1.md) |
| 5 | [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) |
| 6 | [Runable](https://openaitools.app/tool/runable) | 1,048,370 | -1.9% | Freemium | Automates multi-step workflows and asset creation, but with fewer built-in general browser navigation tools. | [.md](https://openaitools.app/tool/runable.md) |
| 7 | [Skywork.ai](https://openaitools.app/tool/skywork-ai) | 1,021,069 | -11.8% | Freemium | Converts prompts into structured office deliverables, but acts primarily as a document generation workspace. | [.md](https://openaitools.app/tool/skywork-ai.md) |
| 8 | [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) |
| 9 | [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) |
| 10 | [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) |

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

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