# Appen vs Dataing

Appen records 665,840 monthly visits with +0.2% month-over-month growth and Contact for Pricing pricing. Dataing records 2,726 monthly visits with -53.2% growth and Freemium pricing.

| Metric | [Appen](https://openaitools.app/tool/appen-com) | [Dataing](https://openaitools.app/tool/dataing) |
| :--- | :--- | :--- |
| Website URL | https://appen.com | http://www.dataing.io |
| Categories | AI Models, Large Language Models (LLMs), AI Developer Tools | AI Dating Assistant, AI Assistant, AI Agent |
| Pricing Model | Contact for Pricing | Freemium |
| Monthly Visits | 665,840 | 2,726 |
| MoM Growth Rate | +0.2% | -53.2% |
| Primary Market | United States | United States |
| Direct Visits | 238,314 | 1,070 |
| Review Score | 4.4 | 3.9 |
| Saved Count | 2 | 1 |
| Supported Platforms | Website | Website |

## Appen

Appen is one of the oldest pure-play AI data companies, tracing its roots to 1996 when it began building human-labeled datasets for speech recognition and NLP. Over three decades it supported successive generations of model development — search relevance in the 2000s, machine translation, deep-learning vision annotation, Transformer-era semantics, GPT-3 scale corpora, RLHF for conversational AI, and now agentic and multimodal foundation models.

Today Appen positions itself around six specialised capability areas: Frontier Alignment (chain-of-thought reasoning traces, SME RLHF, supervised fine-tuning demonstrations, adversarial red teaming), Agentic AI (golden trajectories, RL environment design, failure-mode taxonomies, SWE-driven evaluation), Multimodal and Speech AI (multilingual ASR and conversational corpora, expressive TTS annotation, VLM training data, video action labelling), Physical AI (LiDAR point-cloud annotation, multi-camera sensor fusion, robotics demonstration trajectories, world-model rollouts), Model Integrity (hallucination benchmarking, bias detection, regulatory audits and continuous monitoring), and Research Services (context engineering, calibrated LLM-as-a-judge, synthetic data and custom benchmarking).

Delivery runs through Appen's AI Data Platform (ADAP), which blends automation with human oversight, and the company cites SOC 2 and ISO 27001 certification, 500+ global locales, and a client roster that includes major cloud, search, retail and aerospace organisations. Appen does not publish self-serve pricing; engagements start with a scoping conversation and are quoted per project, so cost depends on data volume, language coverage, modality and annotation complexity.

**Pricing & Plans:** Enterprise/contact-for-pricing. Appen does not publish self-serve plans; projects are scoped and quoted individually. Appen also sells off-the-shelf (OTS) datasets and crowd/contractor workforce programs with separate terms.

- **Frontier alignment data:** Chain-of-thought reasoning traces, SME-level RLHF, supervised fine-tuning demonstrations and adversarial red teaming for the most capable models.
- **Agentic AI datasets:** Golden trajectories, RL environment design, failure-mode taxonomies and software-engineering-driven deep evaluation for autonomous agents.
- **Multimodal and speech corpora:** Multilingual ASR and conversational speech data, expressive TTS annotation, vision-language model training data, video action labelling and voice-agent evaluation.
- **Physical AI annotation:** LiDAR point-cloud labeling, multi-camera sensor fusion, robot demonstration trajectories, world-model rollouts and embodied interaction logs.
- **Model integrity and evaluation:** Hallucination benchmarking, bias detection, regulatory audits and continuous monitoring, plus independent model evaluation.
- **AI Data Platform (ADAP):** A production platform that combines automation with human oversight, with SOC 2 and ISO 27001 certification and 500+ locales covered.

**Strengths:** Three decades of annotation methodology, tooling and workforce management experience; Very broad coverage: frontier alignment, agentic, speech, multimodal, physical AI and evaluation in one vendor; 500+ locales and multilingual expertise that most in-house teams cannot match; Enterprise-grade security posture (SOC 2, ISO 27001) and publicly listed governance

**Limitations:** No transparent self-serve pricing; every engagement requires a sales/scoping cycle; Enterprise-oriented contracts can be heavy for small teams or indie developers; Quality depends on workforce management, so results vary by project and locale; Overlaps with many cheaper specialist annotation vendors, making vendor selection non-trivial

[Profile (Markdown)](https://openaitools.app/tool/appen-com.md)

## Dataing

Dataing takes a deliberately non-swipe approach to online dating. Instead of a feed, the app keeps a thread of context: the songs on repeat, the small rituals, the way someone makes an ordinary Tuesday feel like a story. You ask Cupid to find a person, read a moment, plan something, or remember what matters, and it responds with a specific observation and a suggested next step rather than a verdict. You approve or correct every read, and the final choice always stays with you.

The experience is organised around three ideas. Notice brings you one person with a genuine reason to look closer. Remember keeps the small truths that make you you, in a form that is visible, editable and yours. Move helps you take a single next step, whether that is a message, a plan or an introduction, and it holds the awkward logistics such as tabs, timing and first drafts so your attention stays with the person in front of you.

Dataing also publishes an API for developers who want to build with the context people chose to share, plus a creator programme. The team is small and explicitly positions the product as support for real-world dating rather than an engagement-driven feed. Dataing is an independent product; it is not affiliated with any dating platform whose brand it is compared to.

**Pricing & Plans:** Dataing offers a consumer iOS app with a free entry point; the site does not publish detailed subscription tiers or per-feature prices. API access and creator programme terms are handled through direct contact.

- **Cupid relationship assistant:** Ask Cupid to find a person, read a text, make a plan or remember details, and get a concrete observation instead of a ranking.
- **Context you invite in:** Cupid only uses the details you actively choose to share, and every interpretation can be corrected or discarded.
- **One person, not a deck:** Instead of an infinite feed, the app surfaces a single profile with a stated reason it might fit and how strong the signal is.
- **Live, editable profile:** Your profile is shaped with Cupid and edited by you, staying closer to a living portrait than a static bio.
- **Planning and logistics help:** It drafts the message, proposes one sensible option with realistic travel time, and checks availability against your calendar.
- **Developer API and creator programme:** Builders can integrate Cupid through an API, and creators can make human-facing experiences with it.

**Strengths:** Deliberately moves away from the swipe-feed model toward one meaningful match at a time; Every interpretation is editable and the final decision stays with the user; Only uses context the user actively chooses to share; Combines matching, remembering and planning in a single assistant

**Limitations:** No published pricing table, so costs are unclear before you sign up; iOS-only app at present, with no Android or web client listed; Requires sharing personal context with an AI, which will not suit everyone

[Profile (Markdown)](https://openaitools.app/tool/dataing.md)

## Frequently Asked Questions

### How do Appen and Dataing compare in monthly traffic?

Appen has 665,840 monthly visits, while Dataing has 2,726. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Appen has a month-over-month growth rate of +0.2%; Dataing has -53.2%.

### How does pricing compare?

Appen: Contact for Pricing. Enterprise/contact-for-pricing. Appen does not publish self-serve plans; projects are scoped and quoted individually. Appen also sells off-the-shelf (OTS) datasets and crowd/contractor workforce programs with separate terms. Dataing: Freemium. Dataing offers a consumer iOS app with a free entry point; the site does not publish detailed subscription tiers or per-feature prices. API access and creator programme terms are handled through direct contact.

## 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 | Connects teams directly with verified human participants for preference tuning and studies, but lacks managed end-to-end labeling pipelines. | [.md](https://openaitools.app/tool/prolific-com.md) |
| 2 | [Invisible Technologies](https://openaitools.app/tool/invisible-technologies-inc) | 1,364,520 | +64.4% | Contact for Pricing | Blends custom software with dedicated human expert teams to handle complex operations, but operates as a bespoke service. | [.md](https://openaitools.app/tool/invisible-technologies-inc.md) |
| 3 | [What Brandon Thinks](https://openaitools.app/tool/what-brandon-thinks) | 826,572 | +437.4% | — | Upload a chat and get what Brandon really thinks | [.md](https://openaitools.app/tool/what-brandon-thinks.md) |
| 4 | [Labelbox](https://openaitools.app/tool/labelbox) | 762,803 | -22.8% | Contact for Pricing | Combines robust data labeling tools with automated workflows, but focuses more on software tooling than managed human workforces. | [.md](https://openaitools.app/tool/labelbox.md) |
| 5 | [Scale](https://openaitools.app/tool/scale) | 616,915 | +12.5% | Contact for Pricing | Provides enterprise-grade AI training data and model evaluation at massive scale, but often with higher enterprise pricing. | [.md](https://openaitools.app/tool/scale.md) |
| 6 | [SuperAnnotate](https://openaitools.app/tool/super-annotate) | 386,034 | +37.4% | Free Trial, Contact for Pricing | Delivers advanced multimodal data curation and annotation infrastructure, but requires your own annotation workforce for labeling tasks. | [.md](https://openaitools.app/tool/super-annotate.md) |
| 7 | [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) |
| 8 | [Encord](https://openaitools.app/tool/encord-com) | 244,204 | +9.4% | Contact for Pricing | The multimodal data layer for computer vision and physical AI teams | [.md](https://openaitools.app/tool/encord-com.md) |
| 9 | [Defined.ai](https://openaitools.app/tool/definedcrowd-com) | 146,251 | -21% | Contact for Pricing | Offers pre-packaged AI training datasets and custom data collection services, but with a smaller global crowd than Appen. | [.md](https://openaitools.app/tool/definedcrowd-com.md) |
| 10 | [Label Studio](https://openaitools.app/tool/label-studio) | 137,199 | -16.1% | Contact for Pricing, Free | Provides a flexible open-source annotation platform for self-hosting, but lacks Appen's managed enterprise human workforce services. | [.md](https://openaitools.app/tool/label-studio.md) |

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Source: https://openaitools.app/compare/appen-com-vs-dataing

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