# Isahit vs Labelbox

Isahit records 13,136 monthly visits with +80.2% 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 | [Isahit](https://openaitools.app/tool/isahit) | [Labelbox](https://openaitools.app/tool/labelbox) |
| :--- | :--- | :--- |
| Website URL | https://www.isahit.com | https://labelbox.com |
| Categories | AI Workflow, Large Language Models (LLMs), AI For Data Analytics, AI Developer Tools | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 13,136 | 762,803 |
| MoM Growth Rate | +80.2% | -22.8% |
| Primary Market | France | United States |
| Direct Visits | 5,482 | 370,024 |
| Review Score | 4.1 | 4.4 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Isahit

Isahit positions itself as an ethical outsourcing partner for organisations that need human judgment inside their AI pipeline. Rather than selling software alone, it assembles a managed community of digital workers — claimed at over 1,000 annotators and 18,000 people impacted — and pairs them with project managers, quality assurance layers and its Human-by-API platform. Customers can label on Isahit's own tool, on their existing stack, or on a partner tool.

The service scope spans the classic data-labeling categories (image and video annotation, object detection, NLP extraction, speech and audio) plus newer work created by generative AI: reinforcement learning from human feedback, red-teaming, prompt monitoring, human evaluation of model output, and independent testing for AI Act readiness. On the operations side, Isahit takes on repetitive back-office jobs such as data entry, KYC/KYB checks, product catalog enrichment and review moderation, mostly for e-commerce, retail and services teams.

Its differentiators are social impact and governance. The company is a certified B-Corp, promises part-time flexibility, fair pay and a ban on violent-content labelling, and applies bias-correction and localisation reviews so model output fits regional cultures. Because it works with customer tools and APIs, security-conscious or regulated buyers can keep data within preferred environments while still tapping a scalable workforce.

**Pricing & Plans:** Isahit does not publish public rates; pricing is quoted per project after a scoping call, with offers for one-off labels, ongoing data-processing outsourcing and managed workforce engagements. A free small labelling project is offered through its self-service annotation tool.

- **Image, video and computer vision labeling:** Annotation services for object detection, classification and segmentation across still images and frame-by-frame video, aimed at autonomous driving, drones and industrial inspection models.
- **NLP, speech and audio annotation:** Text extraction, intent tagging, transcription and speech dataset work to train and evaluate language, conversational and voice models.
- **Generative AI and RLHF workflows:** Human feedback collection, prompt monitoring, A/B testing and output evaluation to fine-tune LLMs and correct biases in generative and agentic systems.
- **AI Act trust and validation:** Independent human testing, risk evaluation and traceable evidence to help teams document and validate AI systems against regulatory requirements.
- **Back-office and catalog operations:** Outsourced data entry, cleaning, KYC/KYB verification, PIM and product-catalog management, and review moderation handled by trained operational teams.

**Strengths:** Certified B-Corp model with fair-pay and part-time commitments; Wide modality coverage from computer vision to speech and text; Works on the customer's own tools and APIs; Combines labeling with governance, evaluation and AI Act support

**Limitations:** No transparent public pricing; quotes require a sales conversation; Rates are engagement-specific, so cost forecasting takes more effort; Project-based onboarding means deployment is not instant self-serve

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

Isahit has 13,136 monthly visits, while Labelbox has 762,803. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Isahit has a month-over-month growth rate of +80.2%; Labelbox has -22.8%.

### How does pricing compare?

Isahit: Contact for Pricing. Isahit does not publish public rates; pricing is quoted per project after a scoping call, with offers for one-off labels, ongoing data-processing outsourcing and managed workforce engagements. A free small labelling project is offered through its self-service annotation tool. 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 | [Prolific](https://openaitools.app/tool/prolific-com) | 18,697,671 | -1.8% | Paid | High-quality human data for AI training, evaluation, and research | [.md](https://openaitools.app/tool/prolific-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 | [Invisible Technologies](https://openaitools.app/tool/invisible-technologies-inc) | 1,364,520 | +64.4% | Contact for Pricing | Enterprise AI infrastructure where software and expert humans work together | [.md](https://openaitools.app/tool/invisible-technologies-inc.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 | [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 | [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) |
| 10 | [Confident AI](https://openaitools.app/tool/confident-ai) | 83,400 | -3.3% | Freemium, Paid, Contact for Pricing | Focuses strictly on LLM evaluation, testing, and observability rather than raw data annotation and training dataset curation. | [.md](https://openaitools.app/tool/confident-ai.md) |

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

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