# Eraser vs Labelbox

Eraser records 534,396 monthly visits with -23.6% month-over-month growth and Freemium, Paid, Contact for Pricing pricing. Labelbox records 762,803 monthly visits with -22.8% growth and Contact for Pricing pricing.

| Metric | [Eraser](https://openaitools.app/tool/eraser) | [Labelbox](https://openaitools.app/tool/labelbox) |
| :--- | :--- | :--- |
| Website URL | https://www.eraser.io | https://labelbox.com |
| Categories | AI Diagram Generator, AI Copilot, AI Design Generator, AI Documents Generator | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools |
| Pricing Model | Freemium, Paid, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 534,396 | 762,803 |
| MoM Growth Rate | -23.6% | -22.8% |
| Primary Market | India | United States |
| Direct Visits | 267,661 | 370,024 |
| Review Score | 4.6 | 4.4 |
| Saved Count | 3 | 0 |
| Supported Platforms | Website | Website |

## Eraser

Eraser is a technical design platform aimed at the people who document architecture: enterprise architects, DevOps and platform engineers, consultants, and software teams. The core promise is speed without sloppiness, so instead of dragging boxes for hours you describe the system or point Eraser at a source of truth such as code, a cloud account, a CMDB, or a spreadsheet, and it produces a diagram that already follows sensible layout rules.

Beyond static images, Eraser supports interactive maps that zoom from a company-wide view down into a single service, guided explainers that walk teammates through a system, and AI documents for design docs and technical documentation. Team governance features such as shared style presets, naming rules, and reference architectures keep output consistent as more people create diagrams. An open-source agent toolkit and MCP server let tools like Claude, Cursor, and ChatGPT create, search, and export diagrams programmatically.

Prices are organized in tiers that include a free entry point, paid plans, and enterprise agreements, with multi-tenant, single-tenant, and self-hosted deployment options for larger organizations. Security posture includes SOC 2 Type 2 certification and SAML SSO.

**Pricing & Plans:** Freemium with paid plans and custom enterprise pricing. A free tier and free tools such as DiagramGPT are available, while paid individual and team plans plus enterprise agreements cover advanced governance, security, and deployment options; check the pricing page for current rates.

- **AI diagram generation:** Generate architecture diagrams, flowcharts, sequence diagrams, and BPMN from a text prompt, code, Terraform, or an uploaded PDF, Word, Excel, PowerPoint, Draw.io, or Visio file.
- **Maps from live systems:** Build interactive maps that sync with cloud accounts, repositories, and internal catalogs so the picture stays close to the real environment, with drill-down from org level to a single container.
- **Diagram-as-code integrations:** Paste code or connect private and public Git repositories, Confluence, Notion, VS Code, and GitHub to keep diagrams grounded in actual implementation.
- **Open-source agent toolkit and MCP server:** Run the agent toolkit in your own infrastructure under an MIT license and let MCP-compatible clients create, search, update, and export diagrams with OAuth or API tokens.
- **Team style governance:** Apply presets, naming and notation rules, and reference architectures so every teammate's diagram ships on-brand and consistent.

**Strengths:** Turns prompts, code, and files into properly laid-out diagrams instead of raw box-and-arrow output; Interactive maps and explainers make complex systems understandable to non-architects; MCP server and open-source toolkit fit agent-driven and self-hosted workflows; Enterprise controls such as SSO, SAML, and SOC 2 Type 2 certification

**Limitations:** Heaviest value comes with team and governance features that need setup; Advanced mapping and self-hosted deployment are aimed at larger organizations rather than solo users

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

Eraser has 534,396 monthly visits, while Labelbox has 762,803. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Eraser has a month-over-month growth rate of -23.6%; Labelbox has -22.8%.

### How does pricing compare?

Eraser: Freemium, Paid, Contact for Pricing. Freemium with paid plans and custom enterprise pricing. A free tier and free tools such as DiagramGPT are available, while paid individual and team plans plus enterprise agreements cover advanced governance, security, and deployment options; check the pricing page for current rates. 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 | [Miro](https://openaitools.app/tool/miro) | 30,749,206 | +1.7% | Freemium, Contact for Pricing | Offers an expansive infinite canvas for team brainstorming and visual workshops, but lacks Eraser's code-to-diagram automation. | [.md](https://openaitools.app/tool/miro.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 | [Napkin AI](https://openaitools.app/tool/napkin) | 1,029,978 | -17.5% | Freemium | Automatically embeds visual diagrams directly inside text documents, though it lacks Eraser's specialized cloud architecture focus. | [.md](https://openaitools.app/tool/napkin.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 | [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 | [EdrawMax](https://openaitools.app/tool/edrawmax-com) | 301,707 | -6.5% | Paid | Provides extensive drag-and-drop shapes and templates for general diagrams, but weaker automated code-to-diagram capabilities than Eraser. | [.md](https://openaitools.app/tool/edrawmax-com.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 | [Cloudairy](https://openaitools.app/tool/cloudairy) | 179,162 | -13% | Freemium, Contact for Pricing | Combines team whiteboarding, documentation, and diagramming in one platform, but less tailored for automated codebase architecture. | [.md](https://openaitools.app/tool/cloudairy.md) |

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

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