# Fillout vs Labelbox

Fillout records 3,923,917 monthly visits with -9.7% month-over-month growth and Freemium pricing. Labelbox records 762,803 monthly visits with -22.8% growth and Contact for Pricing pricing.

| Metric | [Fillout](https://openaitools.app/tool/fillout-com) | [Labelbox](https://openaitools.app/tool/labelbox) |
| :--- | :--- | :--- |
| Website URL | https://www.fillout.com | https://labelbox.com |
| Categories | AI Forms, AI Quiz Generator, AI Signature Generator, AI PDF | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools |
| Pricing Model | Freemium | Contact for Pricing |
| Monthly Visits | 3,923,917 | 762,803 |
| MoM Growth Rate | -9.7% | -22.8% |
| Primary Market | United States | United States |
| Direct Visits | 2,306,833 | 370,024 |
| Review Score | 4.4 | 4.4 |
| Saved Count | 12 | 0 |
| Supported Platforms | Website | Website |

## Fillout

Fillout is a no-code platform for building forms, surveys, quizzes, and payment or scheduling pages. You can start from a description and let the built-in Zite AI assemble a ready-to-publish form in your brand, or drag and drop fields manually. Every submission is stored in a structured database you can filter, analyze, and connect to other tools.

Beyond basic data collection, Fillout adds intelligent routing, login-based forms that let respondents edit their own answers, digital signatures, and native integrations with 50+ apps. Branding is fully controllable, including custom domains, white-labeling, and embeds as popups, sliders, or full-screen pages.

A large number of organizations use Fillout for secure data intake, and the service advertises SOC II Type 2 compliance plus AES-256 encryption. A free tier covers casual use, while paid plans unlock advanced logic, higher limits, and team features.

**Pricing & Plans:** Freemium. A free plan covers basic forms and submissions, with paid tiers for advanced logic, higher limits, file uploads, payments, and team collaboration.

- **AI form builder (Zite):** Describe the form you need in plain language and Zite generates a branded form with logic, file uploads, and payments.
- **Logic and workflows:** Add conditional logic, intelligent routing, and multi-step workflows to automate what happens after each response.
- **Editable and secure submissions:** Respondents can log in to update their answers, and forms can be restricted to internal users only.
- **Branding and embeds:** Use custom themes, fonts, logos, custom CSS, your own domain, and embed forms as popups, sliders, or full-screen pages.
- **50+ integrations and analytics:** Send responses to spreadsheets, CRMs, and other tools, then review and collaborate on results with flexible views.

**Strengths:** AI-assisted building makes first drafts fast; Deep logic, workflows, and 50+ integrations; Strong branding controls including white-label and custom domains; SOC II Type 2 compliance and AES-256 encryption

**Limitations:** Advanced features require paid plans; Feature depth can be overwhelming for simple one-off forms; Popular templates see heavy competition for the same look

[Profile (Markdown)](https://openaitools.app/tool/fillout-com.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 Fillout and Labelbox compare in monthly traffic?

Fillout has 3,923,917 monthly visits, while Labelbox has 762,803. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Fillout has a month-over-month growth rate of -9.7%; Labelbox has -22.8%.

### How does pricing compare?

Fillout: Freemium. Freemium. A free plan covers basic forms and submissions, with paid tiers for advanced logic, higher limits, file uploads, payments, and team collaboration. 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 | [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 | [forms.app](https://openaitools.app/tool/forms-app) | 987,157 | -11.2% | Freemium, Paid | Broader template library and multi-channel sharing features, but less flexible multi-page layout customization than Fillout. | [.md](https://openaitools.app/tool/forms-app.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 | [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) |
| 6 | [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) |
| 7 | [BlockSurvey](https://openaitools.app/tool/block-survey) | 302,480 | -14.9% | Freemium, Paid | Superior privacy-first encryption and secure data handling, but more restrictive workflow automation and styling capabilities. | [.md](https://openaitools.app/tool/block-survey.md) |
| 8 | [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) |
| 9 | [Makeform](https://openaitools.app/tool/makeform) | 160,692 | -10.8% | Freemium | Faster AI form generation with built-in logic, but fewer advanced database integration and multi-page routing features. | [.md](https://openaitools.app/tool/makeform.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/fillout-com-vs-labelbox

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