# Appen vs Sieve

Appen records 665,840 monthly visits with +0.2% month-over-month growth and Contact for Pricing pricing. Sieve records 124 monthly visits with +1.6% growth and Contact for Pricing pricing.

| Metric | [Appen](https://openaitools.app/tool/appen-com) | [Sieve](https://openaitools.app/tool/sieve) |
| :--- | :--- | :--- |
| Website URL | https://appen.com | https://www.sieve.ai |
| Categories | AI Models, Large Language Models (LLMs), AI Developer Tools | AI API, AI Dubbing, AI Video Translator, AI Video Editor |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 665,840 | 124 |
| MoM Growth Rate | +0.2% | +1.6% |
| Primary Market | United States | Global |
| Direct Visits | 238,314 | 54 |
| Review Score | 4.4 | 4.1 |
| Saved Count | 2 | 0 |
| 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)

## Sieve

Sieve began life as a video API company offering endpoints for translation, dubbing, background removal, lipsync, autocrop and transcription. That product line has since been superseded: the company published an announcement titled "Reintroducing Sieve" describing its transition from video APIs to supplying multimodal data at petabyte scale, and the original sievedata.com domain now resolves to sieve.ai.

Today Sieve operates as a data lab for what it calls real-world intelligence. Instead of selling individual API calls, it works with frontier AI companies to design, capture, annotate and deliver high-quality training and evaluation data. Its public offerings include egocentric first-person video of economically valuable work with hand-pose and sensor annotations, custom multi-camera studio recordings, simulated and real-world robotics evaluations, virtual-world capture with synchronised control inputs and camera metadata, and datasets targeting motion, causality and spatial reasoning.

Underneath the data products sits serious infrastructure. Sieve has published engineering work on embedding one billion videos and querying tens of billions of vectors, which is the kind of stack you need to make petabyte-scale audiovisual archives searchable for model training. The practical route in is a conversation rather than a signup: visitors are pointed at "Talk to us" and "Request data" flows, and the company is actively hiring researchers and engineers.

**Pricing & Plans:** No public price list. Sieve is positioned as a custom engagement led by a "Talk to us" / "Request data" contact flow, so commercial terms are scoped per project, data volume and capture complexity. Earlier self-serve API pricing is no longer published.

- **Egocentric data capture:** First-person recordings of economically valuable physical work, paired with hand-pose, motion and synchronised sensor annotations.
- **Studio and custom rig recordings:** Multi-camera video and audio collection with bespoke rigs and controlled environments built to a client's specification.
- **Robotics evaluations:** Both simulated and real-world test suites that measure how well robot policies perform across tasks and conditions.
- **Large-scale media infrastructure:** Petabyte-scale synchronised video and audio datasets, including published engineering work on embedding a billion videos for retrieval.
- **Real-world reasoning datasets:** Datasets and benchmarks targeting motion, causality and spatial relationships for models that must reason about the physical world.

**Strengths:** Deep specialisation in hard-to-collect embodied and egocentric data; In-house research and infrastructure proven at billion-video scale; Offers evaluations alongside datasets, not just raw media; Works directly with frontier labs, which signals high data quality standards

**Limitations:** Pricing is quote-based, so there is no self-serve cost transparency; Most offerings require an engagement rather than instant API signup; Documentation for the older video API suite is no longer reachable at the previous docs domain; Overkill for teams that only need a single simple video-processing endpoint

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

## Frequently Asked Questions

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

Appen has 665,840 monthly visits, while Sieve has 124. 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%; Sieve has +1.6%.

### 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. Sieve: Contact for Pricing. No public price list. Sieve is positioned as a custom engagement led by a "Talk to us" / "Request data" contact flow, so commercial terms are scoped per project, data volume and capture complexity. Earlier self-serve API pricing is no longer published.

## 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 | [Roboflow](https://openaitools.app/tool/roboflow) | 1,431,204 | +6.6% | Freemium, Contact for Pricing | Build, train, and deploy computer vision for the physical world | [.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 | 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) |
| 4 | [ImageKit](https://openaitools.app/tool/imagekit) | 947,721 | -13.7% | Freemium, Paid, Contact for Pricing | Real-time image and video APIs, agentic DAM and creative automation in one platform | [.md](https://openaitools.app/tool/imagekit.md) |
| 5 | [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) |
| 6 | [Scale](https://openaitools.app/tool/scale) | 616,915 | +12.5% | Contact for Pricing | The data, evaluations and systems behind AI that has to be right. | [.md](https://openaitools.app/tool/scale.md) |
| 7 | [SuperAnnotate](https://openaitools.app/tool/super-annotate) | 386,034 | +37.4% | Free Trial, Contact for Pricing | AI data infrastructure for building production-grade models | [.md](https://openaitools.app/tool/super-annotate.md) |
| 8 | [AI or Not](https://openaitools.app/tool/ai-or-not) | 375,863 | +12.3% | Freemium, Contact for Pricing | Multi-modal AI detection API that flags synthetic images, text, audio, video and deepfakes | [.md](https://openaitools.app/tool/ai-or-not.md) |
| 9 | [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) |
| 10 | [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) |

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

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