# Defined.ai vs Labelbox

Defined.ai records 146,251 monthly visits with -21% month-over-month growth and Contact for Pricing pricing. Labelbox records 762,803 monthly visits with -22.8% growth and Contact for Pricing pricing.

| Metric | [Defined.ai](https://openaitools.app/tool/definedcrowd-com) | [Labelbox](https://openaitools.app/tool/labelbox) |
| :--- | :--- | :--- |
| Website URL | https://defined.ai | https://labelbox.com |
| Categories | Other | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 146,251 | 762,803 |
| MoM Growth Rate | -21% | -22.8% |
| Primary Market | United States | United States |
| Direct Visits | 84,423 | 370,024 |
| Review Score | 4.4 | 4.4 |
| Saved Count | 1 | 0 |
| Supported Platforms | Website | Website |

## Defined.ai

Defined.ai supports the full AI data lifecycle, from sourcing and collection through annotation, machine translation, conversational data and model evaluation. Its AI Data Marketplace offers off-the-shelf datasets spanning audio, image, video, text and multimodal formats, with advanced filters so teams can find data matching specific technical requirements such as content type, file format, sample rate and bit depth, then request free samples and quotes.

Beyond off-the-shelf data, Defined.ai delivers custom services: model-in-the-loop data annotation for text, audio, image and video, global data collection, multilingual machine translation and conversational AI datasets designed to be natural and bias-free. Work is delivered via file transfer or API so teams can begin training quickly, and services are positioned as enterprise-grade with respect to privacy, security and compliance.

The company emphasizes ethical sourcing and governance. It states ISO 27001, 27701 and 42001 accreditation, GDPR and HIPAA compliance, and fair working conditions, and works with a crowd of over 1.6 million experts across 150+ countries and 500+ languages, dialects and locales. It also offers Accelerat.ai for multilingual AI agent support.

**Pricing & Plans:** Defined.ai does not publish standard list prices; datasets and services are quoted per project. Teams can browse the marketplace, access free dataset samples and request quotes, with pricing depending on dataset type, customization, volume and delivery method.

- **AI Data Marketplace:** A large selection of ready-to-use, ethically sourced datasets across audio, image, video, text and multimodal formats, with advanced filtering and free samples.
- **Data annotation & model-in-the-loop:** Expert-verified labeling for text, audio, image and video using model-first, human-in-the-loop workflows for accuracy and fairness.
- **Machine translation & multilingual data:** High-quality multilingual content for global AI systems, drawing on 500+ languages, dialects and locales.
- **Custom data collection:** Global, diverse data collection at scale for training, with privacy and security vetting on every dataset.
- **Data & model evaluation:** Rigorous testing of datasets and models to ensure accuracy, fairness and quality before deployment.

**Strengths:** One of the largest marketplaces of ready-to-use AI training datasets; Full lifecycle services from collection to annotation and evaluation; Strong compliance posture (ISO 27001/27701/42001, GDPR, HIPAA); Global crowd of 1.6M+ experts across 150+ countries and 500+ languages

**Limitations:** No public list pricing; quotes required for datasets and services; Enterprise-oriented, which may be heavy for small hobby projects; The legacy definedcrowd.com domain now routes to a restricted access gateway rather than a public product page

[Profile (Markdown)](https://openaitools.app/tool/definedcrowd-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 Defined.ai and Labelbox compare in monthly traffic?

Defined.ai has 146,251 monthly visits, while Labelbox has 762,803. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Defined.ai has a month-over-month growth rate of -21%; Labelbox has -22.8%.

### How does pricing compare?

Defined.ai: Contact for Pricing. Defined.ai does not publish standard list prices; datasets and services are quoted per project. Teams can browse the marketplace, access free dataset samples and request quotes, with pricing depending on dataset type, customization, volume and delivery method. 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 | [ModelScope](https://openaitools.app/tool/modelscope-community) | 3,085,423 | +5.1% | Free | Open-source model and dataset hub with one-click online demos | [.md](https://openaitools.app/tool/modelscope-community.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 | [Bright Data](https://openaitools.app/tool/bright-data) | 802,752 | -5.9% | Free Trial, Paid | Web data infrastructure for scraping, proxies, and AI-ready datasets | [.md](https://openaitools.app/tool/bright-data.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 | [Arize AI](https://openaitools.app/tool/arize-com) | 249,028 | +3.2% | Paid, Contact for Pricing | Agent observability, evaluation, and continuous improvement for production AI | [.md](https://openaitools.app/tool/arize-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 | [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/definedcrowd-com-vs-labelbox

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