# Labelbox vs Miro

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. Miro records 30,749,206 monthly visits with +1.7% growth and Freemium, Contact for Pricing pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [Miro](https://openaitools.app/tool/miro) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://miro.com |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Project Management, AI Workflow, Whiteboard AI, AI Diagram Generator |
| Pricing Model | Contact for Pricing | Freemium, Contact for Pricing |
| Monthly Visits | 762,803 | 30,749,206 |
| MoM Growth Rate | -22.8% | +1.7% |
| Primary Market | United States | United States |
| Direct Visits | 370,024 | 20,179,618 |
| Review Score | 4.4 | 4.5 |
| Saved Count | 0 | 40 |
| 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)

## Miro

Miro is a visual collaboration platform built around an infinite, multiplayer canvas. Teams use it to run brainstorming sessions, design sprints, workshops, and retrospectives, sketching ideas as sticky notes, shapes, and freeform drawings that everyone can see and edit at the same time. What began as a digital whiteboard has expanded into an "innovation workspace" where the messy early thinking and the structured plan that follows live in one place.

Structured formats are a large part of that expansion. Miro boards can hold Docs, Tables, Slides, Kanban boards, and Timelines, so a research synthesis can flow straight into a roadmap without switching tools. Thousands of templates and prebuilt playbooks cover common ceremonies, from customer journey mapping to technical architecture diagrams. Deep integrations with Jira, Asana, Linear, ClickUp, Slack, and Microsoft let the canvas stay connected to where work actually gets executed.

AI now runs through the product. Miro can generate diagrams, tables, and summaries from board content, clean up images, and connect external assistants such as Claude, Cursor, or Copilot through an MCP server. Paid plans meter these features with AI credits, and enterprise tiers add SSO, governance controls, and advanced security. Pricing starts with a free tier for small teams and scales through per-member plans to custom enterprise agreements.

**Pricing & Plans:** Freemium with three paid paths. Free covers small teams with 3 editable boards, templates, and core formats. Starter runs about $8 per member/month billed yearly with unlimited private boards and 25 AI credits per member. Business is about $20 per member/month billed yearly with workspaces, prototyping, and AI workflows. Enterprise is quoted on request with SSO, governance, and dedicated support.

- **Infinite collaborative canvas:** An infinite multiplayer board where cursors, comments, and live editing let remote and hybrid teams work in the same space simultaneously.
- **Structured formats on the canvas:** Docs, Tables, Slides, Kanban, and Timelines live alongside freeform drawing so a workshop can turn directly into a structured plan.
- **AI diagramming and synthesis:** Generate diagrams, tables, and summaries, remove image backgrounds, and connect external AI assistants through the MCP server.
- **Templates and playbooks:** 6,000-7,000+ templates for brainstorming, workshops, roadmapping, and process mapping give teams a running start.
- **Integrations and two-way sync:** Connect 250+ tools such as Jira, Asana, Linear, Slack, and Microsoft, with two-way sync available for planning workflows.

**Strengths:** Best-in-class infinite canvas that scales from a quick brainstorm to large workshops; Structured Docs, Tables, Slides, and Kanban reduce tool switching; Huge integration and template ecosystem including two-way Jira and Linear sync; Strong enterprise security, SSO, and admin controls for large organizations

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

## Frequently Asked Questions

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

Labelbox has 762,803 monthly visits, while Miro has 30,749,206. 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%; Miro 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. Miro: Freemium, Contact for Pricing. Freemium with three paid paths. Free covers small teams with 3 editable boards, templates, and core formats. Starter runs about $8 per member/month billed yearly with unlimited private boards and 25 AI credits per member. Business is about $20 per member/month billed yearly with workspaces, prototyping, and AI workflows. Enterprise is quoted on request with SSO, governance, and dedicated support.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [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) |
| 2 | [Whimsical](https://openaitools.app/tool/whimsical) | 966,895 | -18.7% | Freemium | Stronger focus on structured mind mapping and flowcharting, but lacks Miro's massive template library and enterprise workshop features. | [.md](https://openaitools.app/tool/whimsical.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 | [Eraser](https://openaitools.app/tool/eraser) | 534,396 | -23.6% | Freemium, Paid, Contact for Pricing | Excels at generating technical architecture diagrams directly from code, but less suited for freeform creative brainstorming. | [.md](https://openaitools.app/tool/eraser.md) |
| 6 | [Boardmix](https://openaitools.app/tool/boardmix-online-whiteboard) | 447,560 | -6.2% | Freemium | Similar collaborative online whiteboard experience with strong AI diagramming, but offers fewer complex enterprise integration options. | [.md](https://openaitools.app/tool/boardmix-online-whiteboard.md) |
| 7 | [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) |
| 8 | [Flowith](https://openaitools.app/tool/flowith) | 324,679 | -27.3% | Contact for Pricing | Features a spatial visual canvas integrated with multi-model chat agents, but less focused on team workshop facilitation. | [.md](https://openaitools.app/tool/flowith.md) |
| 9 | [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) |
| 10 | [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) |

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

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