An AI-powered tool by Segmed for the de-identification of medical data. It uses NLP and language models to automatically detect and remove Protected Health Information (PHI) from clinical texts, ensuring privacy and compliance for medical research and data sharing.
Lakera is an AI-native security platform designed to protect Generative AI applications from threats like prompt injection, data leakage, and compliance violations. It offers real-time runtime protection, continuous threat intelligence powered by the world's largest AI red team, and easy integration with a single line of code. Trusted by enterprises like Dropbox, Lakera secures AI agents and applications across all major models and languages with ultra-low latency.
An AI-powered tool by Segmed for the de-identification of medical data. It uses NLP and language models to automatically detect and remove Protected Health Information (PHI) from clinical texts, ensuring privacy and compliance for medical research and data sharing.
Lakera is an AI-native security platform designed to protect Generative AI applications from threats like prompt injection, data leakage, and compliance violations. It offers real-time runtime protection, continuous threat intelligence powered by the world's largest AI red team, and easy integration with a single line of code. Trusted by enterprises like Dropbox, Lakera secures AI agents and applications across all major models and languages with ultra-low latency.
| Feature | deid | Lakera |
|---|---|---|
| Primary category | Privacy | Api |
| Added | 2025-08-06 | 2025-08-13 |
| Pricing | Freemium | Freemium |
| Official website | deid.segmed.ai | www.lakera.ai |
| Product type | Website | Website |
| Performance data | ||
| User rating | Not verified | Not verified |
| Comments | 0 | 0 |
| Monthly visits | 4.1K | 305.9K |
| Monthly growth | Not verified | 11.3% |
| Favorites | 145 | 150 |
| Details | View details | View details |
Compare deid and Lakera by monthly reach, traffic trend, visit depth, top regions, and acquisition sources.
In the deid vs Lakera monthly traffic comparison, deid currently shows 4.1K visits and Lakera shows 305.9K; Lakera has about 73.8 times the visible traffic of deid, an absolute difference of about 301.7K visits. This reflects visible reach, not feature quality or paid users.
Only Lakera has complete third-party traffic details; deid 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.
| Country/region | Percentage | Traffic |
|---|---|---|
| 🇺🇸United States | 35.26% | 107.8K |
| 🇫🇷France | 23.89% | 73.1K |
| 🇮🇳India | 20.37% | 62.3K |
| 🇰🇪Kenya | 11.01% | 33.7K |
| 🇳🇴Norway | 9.47% | 29K |
| Source type | Percentage | Traffic |
|---|---|---|
| Direct | 79.26% | 242.4K |
| Referral | 19.73% | 60.3K |
| 1.01% | 3.1K |
This in-depth deid vs Lakera comparison uses only the product records, taxonomy, audience, traffic, and community signals available on this page. deid is primarily listed under “Privacy”, while Lakera is primarily listed under “Api”, so the first decision is whether your actual task matches their recorded scope.
The structured fields currently show these decision-relevant differences: Primary category (deid: Privacy; Lakera: Api); Monthly visits (deid: 4.1K; Lakera: 305.9K); Favorites (deid: 145; Lakera: 150); Website (deid: deid.segmed.ai; Lakera: www.lakera.ai); Added (deid: 2025-08-06; Lakera: 2025-08-13). These facts are more useful for selection than brand visibility alone.
In the deid vs Lakera monthly traffic comparison, deid currently shows 4.1K visits and Lakera shows 305.9K; Lakera has about 73.8 times the visible traffic of deid, an absolute difference of about 301.7K visits. This reflects visible reach, not feature quality or paid users.
Only Lakera has complete third-party traffic details; deid 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.
deid and Lakera currently overlap in shared categories: Compliance. This can place both on the same shortlist, but it does not prove equal implementation, depth, or cost.
deid's unique categories/tags are Privacy, Data Anonymization, clinical notes, data anonymization, data de-identification, GDPR, healthcare, and hipaa compliance; Lakera's are Api, Ai Security, AI security, API security, compliance, data leakage, developer tools, and enterprise AI. These unique fields are the strongest differentiators: validate the product whose recorded scope matches the task instead of following traffic alone.
deid has no verified rating, 0 comments, 145 favorites, and 121 likes；Lakera has no verified rating, 0 comments, 150 favorites, and 140 likes。
Neither product has enough rating or comment samples for a credible reputation ranking.
Put deid on the priority trial list when the task aligns with “Privacy” and especially Privacy, Data Anonymization, clinical notes, data anonymization, data de-identification, and GDPR. This follows recorded positioning and does not imply unlisted capabilities are absent.
deid also currently records: pricing is freemium, product type is website, 4.1K 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.
Put Lakera on the priority trial list when the task aligns with “Api” and especially Api, Ai Security, AI security, API security, compliance, and data leakage. This follows recorded positioning and does not imply unlisted capabilities are absent.
Lakera also currently records: pricing is freemium, product type is website, 305.9K 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.
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 deid and Lakera, 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.
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Customer Support
