# Scale vs Sieve

Scale は月間 616,915 訪問、前月比成長率 +12.5%、料金体系料金は要問い合わせを記録しています。Sieve は月間 124 訪問、成長率 +1.6%、料金体系料金は要問い合わせを記録しています。

| 指標 | [Scale](https://openaitools.app/ja/tool/scale) | [Sieve](https://openaitools.app/ja/tool/sieve) |
| :--- | :--- | :--- |
| ウェブサイト URL | https://scale.com | https://www.sieve.ai |
| カテゴリ | AI開発者ツール, Large Language Models (LLMs), AIモデル, AI Agent | AI API, AI 吹き替え, AI 動画翻訳, AI 動画編集ツール |
| 料金モデル | 料金は要問い合わせ | 料金は要問い合わせ |
| 月間訪問数 | 616,915 | 124 |
| 前月比成長率 | +12.5% | +1.6% |
| 主要市場 | アメリカ合衆国 | グローバル |
| ダイレクト訪問 | 191,408 | 54 |
| レビュースコア | 4.7 | 4.1 |
| 保存数 | 3 | 0 |
| 対応プラットフォーム | ウェブサイト | ウェブサイト |

## Scale

Scale is an enterprise AI company that supplies the data, evaluation and deployment layers behind production AI. Its Data Engine sources and labels training data for frontier models, its evaluation work benchmarks model capability, and its platform teams help enterprises and governments move AI systems from pilot into real operations.

**料金とプラン:** Contact for pricing. Scale does not publish rates; engagements are scoped through a sales conversation and depend on data volume, services and programme complexity.

- **Data Engine:** Human-sourced and expert-annotated training data, including specialist contributors with advanced degrees, at the quality bar frontier labs require.
- **Model evaluation & benchmarks:** Public leaderboards plus private benchmarks that test model capability and safety for the most ambitious AI developers.
- **Enterprise AI applications:** Platform and services teams that pick the right use case, build the system and take ownership of the outcome.
- **Agentic deployment:** Agentic systems designed around measurable business outcomes across private equity portfolios, energy and healthcare operations.
- **Domain expertise:** Programmes spanning healthcare, robotics, defence, public sector and enterprise with human oversight built in.

**強み:** Backed by a long track record in training data and model evaluation.; End-to-end coverage from data sourcing through to deployed systems.; Experienced with regulated, classified and safety-critical environments.; Benchmarks and leaderboards give an independent view of model capability.

**制約・デメリット:** No public pricing or self-serve signup - you need a sales conversation to get started.; Enterprise-scale contracting and data operations require real internal commitment.; Overkill for small teams with modest data or evaluation needs.; Some projects involve sensitive government and defence work with strict compliance overhead.

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

## Sieve

Sieve is a data lab that supplies multimodal video, audio and interaction datasets — egocentric demonstrations, multi-camera studio recordings, robotics evaluations and large-scale media — to frontier AI teams.

**料金とプラン:** 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.

**強み:** 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

**制約・デメリット:** 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/ja/tool/sieve.md)

## よくある質問

### Scale と Sieve の月間トラフィックはどのように比較されますか？

Scale の月間訪問数は 616,915、Sieve は 124 です。トラフィックの推定値はオーディエンスの規模を示すものであり、製品の品質を示すものではありません。

### 成長率はどのように比較されますか？

Scale の前月比成長率は +12.5%、Sieve は +1.6% です。

### 料金はどのように比較されますか？

Scale: 料金は要問い合わせ。Contact for pricing. Scale does not publish rates; engagements are scoped through a sales conversation and depend on data volume, services and programme complexity. Sieve: 料金は要問い合わせ。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.

## 代替ツール

| # | ツール名 | 月間訪問数 | 前月比成長率 | 料金モデル | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Roboflow](https://openaitools.app/ja/tool/roboflow) | 1,431,204 | +6.6% | フリーミアム、料金は要問い合わせ | 物理世界のためのコンピュータビジョンを構築・学習・展開 | [.md](https://openaitools.app/ja/tool/roboflow.md) |
| 2 | [ImageKit](https://openaitools.app/ja/tool/imagekit) | 947,721 | -13.7% | フリーミアム、有料、料金は要問い合わせ | リアルタイム画像・動画 API、エージェント型 DAM、クリエイティブ自動化を 1 つに | [.md](https://openaitools.app/ja/tool/imagekit.md) |
| 3 | [Labelbox](https://openaitools.app/ja/tool/labelbox) | 762,803 | -22.8% | 料金は要問い合わせ | Scaleの主要なアノテーション事業と同様に、企業向けのデータラベリングと学習データのキュレーションに特化しています。 | [.md](https://openaitools.app/ja/tool/labelbox.md) |
| 4 | [SuperAnnotate](https://openaitools.app/ja/tool/super-annotate) | 386,034 | +37.4% | 無料トライアル、料金は要問い合わせ | SuperAnnotate is an AI data platform that combines a collaborative multimodal annotation editor, an expert data-operations team, and the orchestration layer needed to move curated datasets into training, evaluation, and | [.md](https://openaitools.app/ja/tool/super-annotate.md) |
| 5 | [AI or Not](https://openaitools.app/ja/tool/ai-or-not) | 375,863 | +12.3% | フリーミアム、料金は要問い合わせ | 画像・テキスト・音声・動画・ディープフェイクを判定するマルチモーダルAI検出API | [.md](https://openaitools.app/ja/tool/ai-or-not.md) |
| 6 | [Weights & Biases（CoreWeave Forge）](https://openaitools.app/ja/tool/prompts) | 315,624 | -80.2% | フリーミアム、料金は要問い合わせ、無料 | 一つの接続環境で AI モデルとエージェントを追跡・評価・出荷。 | [.md](https://openaitools.app/ja/tool/prompts.md) |
| 7 | [Arize AI](https://openaitools.app/ja/tool/arize-com) | 249,028 | +3.2% | 有料、料金は要問い合わせ | Focuses on post-deployment LLM observability and evaluation rather than training data generation or annotation. | [.md](https://openaitools.app/ja/tool/arize-com.md) |
| 8 | [Encord](https://openaitools.app/ja/tool/encord-com) | 244,204 | +9.4% | 料金は要問い合わせ | コンピュータビジョンとフィジカル AI チームのためのマルチモーダルデータレイヤー | [.md](https://openaitools.app/ja/tool/encord-com.md) |
| 9 | [AI/ML API](https://openaitools.app/ja/tool/ai-ml-api) | 232,946 | -20.1% | Pay-as-you-go credits (free to start); enterprise custom pricing on request | 500以上のAIモデルと1,000以上のエージェントツールを1つのAPIで | [.md](https://openaitools.app/ja/tool/ai-ml-api.md) |
| 10 | [Memories.ai](https://openaitools.app/ja/tool/memories-ai) | 188,127 | -14.6% | フリーミアム、料金は要問い合わせ | 物理 AI とロボット学習のための動画データ基盤 | [.md](https://openaitools.app/ja/tool/memories-ai.md) |

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出典: https://openaitools.app/ja/compare/scale-vs-sieve

More Markdown: [カテゴリ](https://openaitools.app/ja/categories.md) · [トラフィックランキング](https://openaitools.app/ja/rankings.md) · [llms.txt](https://openaitools.app/ja/llms.txt)