# Grably vs Labelbox

Grably records 1,923 monthly visits with -13% 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 | [Grably](https://openaitools.app/tool/grably) | [Labelbox](https://openaitools.app/tool/labelbox) |
| :--- | :--- | :--- |
| Website URL | https://grably.us | https://labelbox.com |
| Categories | AI Developer Tools, AI Image Recognition, Large Language Models (LLMs) | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 1,923 | 762,803 |
| MoM Growth Rate | -13% | -22.8% |
| Primary Market | Global | United States |
| Direct Visits | 683 | 370,024 |
| Review Score | 3.8 | 4.4 |
| Saved Count | 1 | 0 |
| Supported Platforms | Website | Website |

## Grably

Grably is a multi-modal human interaction data research company. Rather than selling generic web scrapes, it positions itself as a lab that captures and models how people act, sense, decide, and communicate, then packages that into structured datasets for AI developers. Its site highlights backing from Techstars and partnerships with leading AI labs and technology companies.

The company organizes its offering into suites for image, video, audio, code, and text, alongside a catalogue of multi-modal conversational data. Publicly described assets include billions of sequential messages in more than 100 languages, thousands of hours of labelled sports footage, and tens of millions of medical studies. Each dataset is aimed at a specific modelling need, from LLM pretraining and multilingual coverage to embodied AI, temporal video understanding, speech, and document intelligence.

Grably also builds custom datasets for clients with niche goals, aligning anonymization, filtering, and delivery formats to internal research, legal, and infrastructure requirements. Delivery timelines vary by modality, and pricing is scoped per engagement, so prospective buyers discuss requirements directly with the team. The overall pitch is data quality and human-centred signal, not volume alone.

**Pricing & Plans:** Pricing is contact-based. Each dataset or custom project is quoted according to scope, modalities, and delivery requirements.

- **Multi-modal data suites:** Datasets covering image, video, audio, code, text, and conversational interaction for varied model-training needs.
- **Custom dataset design:** Buyer-scoped datasets with configurable anonymization, filtering, modalities, and delivery formats.
- **Multilingual coverage:** Conversational and text data spanning 100+ languages, including low-resource and underrepresented regions.
- **Structured, annotated content:** Signals that are aligned, annotated, and validated for cross-modality and scientific consistency.
- **Domain-specific corpora:** Specialized datasets for sports, medical records, documents, scientific literature, and code.

**Strengths:** Broad multi-modal and multilingual dataset catalogue; Custom dataset design with configurable anonymization; Enterprise and research-lab oriented delivery; Backed by a recognised accelerator program

**Limitations:** No self-serve pricing or instant purchase flow; Dataset access requires a sales conversation; Delivery timelines for rich modalities can span weeks

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

Grably has 1,923 monthly visits, while Labelbox has 762,803. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Grably has a month-over-month growth rate of -13%; Labelbox has -22.8%.

### How does pricing compare?

Grably: Contact for Pricing. Pricing is contact-based. Each dataset or custom project is quoted according to scope, modalities, and delivery requirements. 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 | [clickworker](https://openaitools.app/tool/clickworker-com) | 1,861,149 | +0.5% | Contact for Pricing | Crowdsourcing data services for AI training and market data | [.md](https://openaitools.app/tool/clickworker-com.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 | [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 | [Surge AI](https://openaitools.app/tool/surgehq-ai) | 245,142 | +6.6% | Contact for Pricing | Expert human data and evaluation for training frontier AI | [.md](https://openaitools.app/tool/surgehq-ai.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 | [Wirestock](https://openaitools.app/tool/wirestock) | 167,156 | +12.9% | Freemium | Where creators get paid and AI teams get premium training data | [.md](https://openaitools.app/tool/wirestock.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/grably-vs-labelbox

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