Consensusは、科学研究のために設計されたAI搭載の検索エンジンです。2億件以上の査読付き論文を検索し、ユーザーの質問に対して証拠に基づいた統合的な回答を提供します。科学文献から主要な知見を直接抽出することで、研究者、学生、専門家の時間を節約します。
製品概要
Consensus 製品概要
Consensusは、科学研究のために設計されたAI搭載の検索エンジンです。2億件以上の査読付き論文を検索し、ユーザーの質問に対して証拠に基づいた統合的な回答を提供します。科学文献から主要な知見を直接抽出することで、研究者、学生、専門家の時間を節約します。
STORM 製品概要
STORMは、スタンフォード大学が開発したAI研究プロトタイプで、あらゆるトピックに関するWikipediaのような包括的なレポートを自動生成します。対話的な知識キュレーションをサポートし、ユーザーが研究プロセスを導き、アウトラインを編集し、構造化され引用付きの記事を効率的に作成できます。
Detailed feature comparison
| Feature | Consensus | STORM |
|---|---|---|
| 主要カテゴリー | 学習 | 学習 |
| 追加日 | 2025-08-10 | 2025-08-15 |
| 価格 | フリーミアム | 無料 |
| 公式サイト | consensus.app | storm.genie.stanford.edu |
| 製品タイプ | ウェブサイト | ウェブサイト |
| Performance data | ||
| ユーザー評価 | 未確認 | 未確認 |
| コメント | 0 | 0 |
| 月間訪問数 | 5.7M | 4.2K |
| 月間成長率 | -2.5% | 未確認 |
| お気に入り | 98 | 128 |
| Details | 詳細を見る | 詳細を見る |
Consensus vs STORM monthly traffic
Compare Consensus and STORM by monthly reach, traffic trend, visit depth, top regions, and acquisition sources.
How to interpret the traffic data
In the Consensus vs STORM monthly traffic comparison, Consensus currently shows 5.7M visits and STORM shows 4.2K; Consensus has about 1,375.7 times the visible traffic of STORM, an absolute difference of about 5.7M visits. This reflects visible reach, not feature quality or paid users.
Only Consensus has complete third-party traffic details; STORM uses visits recorded inside ToolMage. These scopes cannot estimate market share directly, and on-site views should not be treated as the product’s total website traffic.
Consensus monthly traffic:
Latest traffic
Monthly traffic trend
- 2025/9: 3.5M 月間訪問数
- 2026/1: 3.9M 月間訪問数
- 2026/2: 4M 月間訪問数
- 2026/3: 5.1M 月間訪問数
- 2026/4: 5.9M 月間訪問数
- 2026/5: 5.7M 月間訪問数
主要地域
Top 5 countries/regions
| Country/region | Percentage | Traffic |
|---|---|---|
| 🇮🇩Indonesia | 35.36% | 2M |
| 🇺🇸United States | 29.41% | 1.7M |
| 🇵🇪Peru | 12.81% | 735.4K |
| 🇮🇳India | 11.61% | 666.5K |
| 🇩🇪Germany | 10.81% | 620.6K |
流入元
| Source type | Percentage | Traffic |
|---|---|---|
| ダイレクト | 85.92% | 4.9M |
| 参照元 | 12.3% | 706.1K |
| Eメール | 1.78% | 102.2K |
検索キーワード
STORM monthly traffic:
Latest traffic
Usage comparison
Compare the core capabilities of Consensus and STORM
Consensus Core features
STORM Core features
Use cases
Consensus Use cases
STORM Use cases
Consensus vs STORM:In-depth comparison and selection guidance
First decide whether the products solve the same kind of need
This in-depth Consensus vs STORM comparison uses only the product records, taxonomy, audience, traffic, and community signals available on this page. Consensus is primarily listed under “学習”, while STORM is primarily listed under “学習”, so the first decision is whether your actual task matches their recorded scope.
The structured fields currently show these decision-relevant differences: Pricing (Consensus: Freemium; STORM: Free); Monthly visits (Consensus: 5.7M; STORM: 4.2K); Favorites (Consensus: 98; STORM: 128); Website (Consensus: consensus.app; STORM: storm.genie.stanford.edu); Added (Consensus: 2025-08-10; STORM: 2025-08-15). These facts are more useful for selection than brand visibility alone.
What market visibility and monthly traffic mean
In the Consensus vs STORM monthly traffic comparison, Consensus currently shows 5.7M visits and STORM shows 4.2K; Consensus has about 1,375.7 times the visible traffic of STORM, an absolute difference of about 5.7M visits. This reflects visible reach, not feature quality or paid users.
Only Consensus has complete third-party traffic details; STORM uses visits recorded inside ToolMage. These scopes cannot estimate market share directly, and on-site views should not be treated as the product’s total website traffic.
The current traffic scope is not sufficient for a reliable product ranking. Treat monthly visits as a market-interest signal, then decide using taxonomy, use cases, pricing, and a like-for-like trial rather than reading exposure as product capability.
Product positioning, use cases, and roles
Consensus and STORM currently overlap in shared categories: 学習; shared tags: 文献レビュー、研究. This can place both on the same shortlist, but it does not prove equal implementation, depth, or cost.
Consensus's unique categories/tags are 医療、ライティング、文献レビュー、学術検索、AI検索エンジン、データ分析、根拠に基づいた、ファクトチェック; STORM's are ライティングアシスタント、知識管理、アカデミックライティング、自動化された研究、コンテンツ作成、知識キュレーション、知識統合、レポート生成. These unique fields are the strongest differentiators: validate the product whose recorded scope matches the task instead of following traffic alone.
What ratings, comments, and favorites can tell you
Consensus has no verified rating, 0 comments, 98 favorites, and 90 likes;STORM has no verified rating, 0 comments, 128 favorites, and 120 likes。
Neither product has enough rating or comment samples for a credible reputation ranking.
Selection guidance by actual need
When to evaluate Consensus first
Put Consensus on the priority trial list when the task aligns with “学習” and especially 医療、ライティング、文献レビュー、学術検索、AI検索エンジン、データ分析. This follows recorded positioning and does not imply unlisted capabilities are absent.
Consensus also currently records: pricing is freemium, product type is website, 5.7M verified monthly visits, no verified user rating. Verify any hard requirement around price, platform, or reach before trial, and do not let sparse review data substitute for testing.
When to evaluate STORM first
Put STORM on the priority trial list when the task aligns with “学習” and especially ライティングアシスタント、知識管理、アカデミックライティング、自動化された研究、コンテンツ作成、知識キュレーション. This follows recorded positioning and does not imply unlisted capabilities are absent.
STORM also currently records: pricing is free, product type is website, 4.2K on-site monthly views, no verified user rating. Verify any hard requirement around price, platform, or reach before trial, and do not let sparse review data substitute for testing.
How to validate the recommendation before deciding
The available data describes positioning, public visibility, and community signals, but it cannot prove output quality, speed, integration effort, privacy, or long-term cost in your workflow. Before deciding, run the same representative tasks in Consensus and STORM, then record completion time, accuracy, manual corrections, and the real paid threshold. A like-for-like trial turns this comparison into a defensible adoption decision.




