产品比较
JieGou vs Make
从视觉化场景到 AI 原生自动化
Make（前身为 Integromat）提供强大的视觉化场景建构器，用于连接应用程式和转换资料。JieGou 专为 AI 驱动的工作流程而设计，结构化 LLM 推理是核心——而非 API 之间的资料转换。如果您的工作流程需要 AI 在每个步骤中阅读、撰写、评分和决策，JieGou 提供专门为此设计的原语。
最后更新: 2026年3月
学习回圈优势
其他平台执行您的指令。JieGou 从每次执行中学习并变得更好。
Make 的场景每次执行都相同。JieGou 会学习——撷取知识、优化提示词，并呈现让每次后续执行更好的洞察。
探索智能平台 →主要差异
| JieGou | Make | |
|---|---|---|
| 核心设计 | AI 原生，具有结构化提示/回应架构 | 视觉化资料转换和 API 编排 |
| LLM 整合 | 每步骤一流的多供应商支援 | Maia AI 代理建构器，具即时推理透明度（早期存取） |
| 结构化 I/O | 每个配方都有型别化输入/输出架构 | 模组之间自由格式的资料对应 |
| 审核关卡 | 原生暂停与恢复，附带电子邮件通知 | 需要外部 Webhook 解决方案 |
| 知识库 | 内建 RAG 提供配方上下文 | 无原生文件知识功能 |
| AI 评估 | 多评审评分的比较测试 | 无内建 AI 品质测试 |
| 组织全域可见性 | 部门级分析和营运中心 | Make Grid：组织全域自动化景观视觉化 |
| 定价 | 免费方案 + $49/月 Pro（BYOK LLM 费用另计） | 从 $9/月（Core）到 $29/月（Pro）；所有付费方案含自订 AI |
| 品质保证 | 持续性 LLM 评审评分 + 统计 AI Bakeoff + 对抗性输入的夜间模拟测试 | 场景层级的测试运行 |
| 整合方式 | MCP 原生：AI 透过开放协定发现和使用工具 | 2,000+ 应用程式模组搭配视觉化资料对应；早期 MCP 支援 |
| LLM Integration | 9 providers with BYOM bakeoffs — structured A/B testing to prove which model works best per workflow | Multi-model: GPT-4, Claude, Gemini, Grok + OpenAI-compatible endpoints via BYOK (all paid plans); 350+ AI app connectors — but no bakeoffs or model comparison tooling |
| Structured I/O | Typed input/output schemas on every recipe | Free-form data mapping between modules |
| Approval Gates | Native pause-and-resume with email notifications | Requires external webhook workarounds |
| Knowledge Sources | 12 enterprise knowledge sources (Coveo, Glean, Elasticsearch, Algolia, Pinecone, Vectara, Confluence, Notion, Google Drive, OneDrive/SharePoint, Zendesk, Guru) + built-in RAG | No enterprise knowledge integration; 3,000+ app connectors for data syncing only |
| AI Evaluation | AI Bakeoffs with multi-judge scoring | No built-in AI quality testing |
| Org-Wide Visibility | Operations Hub: Automation Landscape Map, Governance Dashboard, Org Analytics with executive summaries — all organized by department | Make Grid: org-wide automation landscape visualization + Enterprise Grid centralized control |
| Pricing | Free tier + $49/mo Pro (BYOK LLM costs separate) | Credit-based: from $10.59/mo; AI-intensive actions cost multiple credits; BYOK on all paid plans; Enterprise Grid for large deployments |
| Quality Assurance | Continuous LLM-judge scoring + statistical AI Bakeoffs + nightly simulation testing with adversarial inputs | Enterprise Grid AI-assisted debugging for scenario logic |
| Integrations | MCP-native: 250+ integrations where AI discovers and uses tools via open protocol | 3,000+ app modules with visual data mapping + 350+ AI app connectors; early MCP support |
| Multi-Agent Safety | Delegation cycle detection, shared memory isolation, auto role inference — built-in guardrails | AI Agents with reasoning transparency and real-time decision-making; no delegation safety primitives, no cycle detection, no memory isolation |
| Visual Canvas | DAG builder with agent-aware nodes, memory overlays, cycle detection | Best-in-class visual scenario builder for data mapping |
| Test Coverage | 13,320+ tests with 99.1% code coverage; nightly regression suites | No published test suite or coverage metrics |
| Hybrid Deployment | VPC execution agents + Docker Compose air-gapped option (Enterprise) | Cloud-only SaaS; no on-premise option |
| Data Residency | Configurable data residency with compliance presets | EU and US data centers available |
| Evidence Export | 17 TSC controls, 8 evidence categories, auditor-ready PDF/JSON export | Enhanced audit logs (Enterprise Grid) |
| A2A Protocol | Agent-to-Agent protocol for cross-platform interoperability | No A2A; early MCP support for tool discovery |
| Agent Threat Detection | 4 inline detectors: prompt injection, data exfiltration, privilege escalation, resource abuse — runs during execution | No agent-level threat detection |
| AI Agent Architecture | Department-scoped agents with graduated autonomy (4 levels), GovernanceScore, tool approval gates, and compliance framework mapping | Make AI Agents (April 2025): autonomous decision-making with NL goals, context-aware adaptation, reasoning panel — but zero governance layers, no autonomy controls, no compliance mapping |
| Natural Language Interface | Conversational AI agent builds workflows from plain English + 20 department packs for instant start | Maia: NL-to-scenario builder that generates full automations from descriptions — available on all plans including free tier |
| AI Agent Governance | 10-layer governance on every agent: identity, encryption, data residency, RBAC, escalation, tool approval, audit, compliance timeline, evidence export, regulatory | No agent governance — AI Agents run with full autonomy, no tool approval gates, no compliance controls, no audit evidence export |
为什么团队选择 JieGou
结构化 AI 输出
每个配方都强制执行型别化输入和输出架构，因此下游步骤始终从 LLM 接收一致的、机器可读的资料。
提供上下文的知识库
上传文件并建立 RAG 驱动的知识库，为配方提供特定领域的上下文——无需外部向量资料库。
AI 评估比较测试
透过多评审评分、合成输入和信赖区间，以统计严谨性比较模型效能。
品牌语调治理
设定全组织的品牌语调准则，自动套用于每个 AI 生成的输出。
Governed AI agents vs. ungoverned AI agents
Make launched AI Agents — but with zero governance. JieGou wraps every agent in 10 governance layers, 4 autonomy levels, tool approval gates, and compliance framework mapping. Same capability, fundamentally different trust posture.
何时选择
选择 JieGou，当您需要
- 以 AI 为核心的结构化 LLM 推理工作流程
- 需要内建知识库为 AI 提供上下文的团队
- 需要人工审核关卡的流程
- 评估和比较 AI 模型品质的组织
选择 Make，当您需要
- API 之间的复杂资料转换
- 具有进阶资料对应的视觉化场景建构
- 需要广泛 API 连接器库的团队
- 具有进阶路由和重试逻辑的错误处理
Make 的优势
同类最佳的视觉化场景建构器
拖放式视觉化编辑器，搭配进阶资料对应、分支和错误处理，树立视觉化自动化设计的标准。
Maia AI 代理建构器
AI 驱动的代理建构器，具即时推理透明度，让使用者即时查看和理解代理的决策过程。
Make Grid 组织全域自动化可见性
组织全域视觉化工具，映射您整个自动化景观，展示场景如何在部门之间相互连接——为企业监督提供独特的能力。
MCP 支援标准化工具发现
率先采用 Model Context Protocol，实现 AI 驱动场景中的标准化工具发现和整合。
3,000+ 应用程式连接器
广泛的整合库，搭配热门应用程式的深度模组支援，对 API 操作提供精细控制。
具竞争力的定价，所有付费方案含自订 AI
付费方案从 $9/月起，所有方案都包含自订 AI 功能——让预算有限的团队也能轻松探索 AI 自动化。
3,000+ app connectors with 350+ AI apps
Extensive integration library with deep module support for popular apps, plus 350+ dedicated AI app connectors for specialized AI workflows.
MCP support for standardized tool discovery
Early adoption of Model Context Protocol enabling standardized tool discovery and integration across AI-powered scenarios.
Credit-based pricing with custom AI on all paid plans
Plans start at $10.59/mo with credit-based billing. Custom AI provider connections available on all paid tiers. AI-intensive actions cost multiple credits.
常见问题
JieGou 可以取代 Make 的所有自动化吗？
JieGou 擅长 AI 驱动的工作流程。对于不涉及 AI 的纯 API 资料转换，Make 可能仍然更合适。许多团队两者都用——Make 处理资料管道，JieGou 处理 AI 逻辑。
JieGou 有像 Make 一样的视觉化建构器吗？
JieGou 有具备步骤排序功能的工作流程建构器，但其重点在于 AI 配方配置而非视觉化资料对应。对话式 AI 助手也能从自然语言建构工作流程。
JieGou 的知识库如何运作？
上传 PDF、Markdown 或文字档。JieGou 将它们处理成可搜寻的知识库，配方可透过 RAG 自动引用，为 AI 回应提供特定领域的上下文。
我可以同时使用 Make 和 JieGou 吗？
可以。使用 Webhook 从 Make 场景触发 JieGou 工作流程，或将 JieGou 输出发送回 Make 进行下游资料路由。
Make 比 JieGou 便宜吗？
Make 的付费方案从 $9/月（Core）和 $29/月（Pro）起，所有付费方案都包含自订 AI 功能。JieGou 是 $49/月 Pro，BYOK LLM 费用另计。对于非 AI 场景 Make 通常更便宜；对于 AI 密集的工作流程搭配 AI Bakeoff、知识库和部门套件，JieGou 提供更多价值。
Make now has AI Agents — how is JieGou different?
Make AI Agents (April 2025) bring autonomous decision-making into Make's visual builder with multi-model support and 3,000+ app connections. Maia lowers the barrier further with natural-language scenario creation. However, Make AI Agents have zero governance — no tool approval gates, no compliance frameworks, no delegation safety, no audit evidence export. JieGou wraps every agent in 10 governance layers, graduated autonomy (4 levels), GovernanceScore quantification, and three regulatory framework mappings (EU AI Act, NIST AI RMF, ISO 42001). Make built the agent. JieGou built the trust infrastructure around the agent.
What is Maia and how does it compare to JieGou's conversational agent?
Maia is Make's natural-language interface that generates full scenarios from plain English descriptions — available on all plans including free. JieGou's conversational AI agent also builds workflows from natural language. The difference: JieGou pairs NL building with 20 department packs, so teams get pre-built, governance-wrapped workflows instead of starting from scratch. Maia builds scenarios; JieGou gives you a curated starting point with governance built in.
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