- 月访问
- <5K
- 2026 年 9 月
- 公开收入
- 未公开
- 暂无公开收入证据
- 入门价
- 免费
- 没有月付订阅价
- 首次发布
- 5 个月前
- 2026-06-21 · 估算
访问
近 12 个月
营销动作
发布、搜索与内容、引荐、促销X 账号@maxnghello
在 Fazier 发布（75 票 · 22 评论）发布
DataMoat — Protect, export, back up, analyze, search, and reuse AI data
6 月 21 日 · 5 个月前发布页
第三方评测与案例拆解
获客渠道拆解、工程师评测与开源档案Fazier
2026-06-21查看原文
- 核心亮点评测：
- Raw records first Original source records, ChatGPT export files, session JSONL, logs, metadata, skills snapshots, and attachments are preserved when the source provides them, then stored inside the encrypted memory archive. Prompt-to-output trail Normalized records keep prompts, responses, tool use, tool results, timestamps, model metadata, source path, and stable attachment links together so the surrounding work can be reviewed later. Local private custody The memory archive, search data, skills, attachments, and memory encryption keys stay on-device. DataMoat is not a platform-account personalization layer or transcript cloud. Cross-provider layer Supported ChatGPT exports, Claude, Codex, Cursor, DeepSeek/Qwen local workflows, OpenClaw, skills, and attachments land in the same local memory layer instead of staying trapped inside one vendor view. Company AI work asset Work history becomes a protected company or personal asset for review, incident analysis, onboarding, handoff, private memory, and future model evaluation under your own rules. Portable memory ownership If the team changes tools or models, the memory layer can move with the encrypted DataMoat folder. Vendor choice changes; custody of the work record stays yours. Black box Reconstruct what happened when an AI-assisted task mattered. Knowledge base Keep the process, not only the final answer or commit. Handoff layer Give future teammates and agents the decisions behind the work. Private memory Build reusable context without sending a memory archive to DataMoat.
- 适用人群：
- 01 / problem Who solved what? Keep the work record around incidents, migrations, bugs, product decisions, and repeated workflows so the next person can find the path, not just the outcome. 02 / context What did the AI see? Preserve supported file context, attachments, skills, metadata, local source records, and the surrounding session that shaped the answer. 03 / prompts How did employees prompt? Save the prompt trail, corrections, constraints, clarifications, and decisions that turned a vague task into a usable result. 04 / tools Which tools ran? Capture supported tool calls, command output, errors, timestamps, source metadata, and stable attachments when the source writes them locally. 05 / decision Which solution shipped? Keep the evidence around the adopted approach: alternatives discussed, failed paths, final commands, review notes, and the reason the team moved forward. 06 / reuse Can it be reused later? Make AI work searchable for reuse, audit, incident review, onboarding, project handoff, and private AI memory across ChatGPT, Claude, Codex, Cursor, DeepSeek, Qwen, and OpenClaw workflows.
开源仓库与 Agent 生态
GitHub 开源与智能体接入
Agent 智能体、MCP 与开发者接入
Agent Skill流量来源
2026 年 9 月
访问构成
渠道
- 直接访问40%
- 自然搜索32%
- 外部引荐11%
- 社交媒体8%
- 其他9%
国家/地区
- 印度尼西亚97%
- 波兰3%
搜索
站点权重 —域名注册 2026-04-27
| 关键词 | 月搜索量 |
|---|---|
| cache ram llama cpp | 290 |
| --cache-ram parameter llama.cpp | 250 |
| llama.cpp kv cache settings | 150 |
| llama cpp cursor | 110 |
流量数据：Vaneform
定价
定价页没有列出套餐价格。
试用：未提供 · 观察于 2026-11-03 · 原定价页
页面与变化
开始记录于 2026-07-05
关键页面
每周快照- 首页datamoat.org/
- 定价datamoat.org/pricing 快照 2026-11-03
- 功能datamoat.org/zh-cn/ 快照 2026-11-03
问题与证据
- DataMoat 的网站访问量是多少？
- Vaneform 估算 2026-09 网站访问量为 <5K。访问量包含重复访问，付费客户数与收入需分别查看对应证据。来源
- DataMoat 是什么产品，支持哪些平台？
- DataMoat 归属「笔记与知识管理」赛道：DataMoat 是面向个人和团队的本地 AI 工作空间，可加密保存、搜索、备份和复用多种 AI 工具的工作记录。。 支持平台包括：网页。
- DataMoat 是开源项目吗？采用什么协议？
- DataMoat 拥有公开代码仓库（https://github.com/max-ng/datamoat），已公开源代码仓库，记录有 30 个 GitHub Stars。来源
同分类研究对象
效率与协作 / 笔记与知识管理
这些产品属于同一目录分类。选型时请进一步核对客户任务、功能与部署要求。