Modulo vs Dify
One builds governed AI pipelines for your SDLC. The other builds LLM applications for your users. Both are self-hostable. Here is how they compare.
Comparison table
| Dimension | Modulo | Dify |
|---|---|---|
| Primary use case | SDLC agent orchestration with governance. Composes AI agents into pipelines that automate pull requests, code review, testing, and deployment, with human-in-the-loop gates at every step. | LLM application building. Visual workflow builder for creating AI agents, RAG-powered chatbots, and multi-step LLM workflows. Focused on building AI applications, not automating software delivery. |
| Hosting and data residency | Self-hosted only. Runs on your infrastructure, whether cloud, air-gapped, or behind a VPN. No telemetry by default. Every tier, including the free Community edition, runs entirely under your control. | Self-hostable (Community Edition, free) or cloud-hosted. Cloud does not guarantee EU data residency; the privacy policy lists China and the US among processing locations. Self-hosting on your own infrastructure is the GDPR-clean option. |
| Licensing model | Source-available under BUSL. The farnalabs/modulo repository is public and inspectable. The licence restricts the field of use but does not restrict internal use. Not OSI-approved open-source. | Apache 2.0 with additional conditions. The code is public and the Community Edition is free, but you cannot offer Dify as a hosted multi-tenant service without a commercial license. GitHub classifies the licence as Other. |
| Governance features | SSO/SAML, SCIM provisioning, team RBAC, immutable audit log with hash-chaining, HITL gates, eval-gated progressive autonomy, cost and budget controls, and capability scopes. All included in the Team tier. | Enterprise tier includes SSO, role management, audit logs, and advanced security. The Community Edition does not include RBAC, audit logs, or SSO. These features require an Enterprise license with custom pricing. |
| Pricing model | $8,000/year flat. Per organisation, per deployment. No per-seat, no per-run, no usage metering. The cost does not grow when you add engineers, reviewers, security stakeholders, or QA. | Cloud: Sandbox free (200 message credits), Professional $59/month (5,000 credits), Team $159/month (10,000 credits). Enterprise custom. Self-hosted Community Edition is free. LLM API costs are separate. |
| AI and agent capabilities | Agent nodes compose AI models (Claude, Copilot, Cursor, your own) into typed pipelines. Typed schemas define inputs and outputs. Evals gate progressive autonomy. Agents run on your infrastructure, your prompts, your models. | Visual workflow builder with built-in RAG engine, agent tooling, and knowledge base management. Supports multiple LLM providers via API keys. Good for building conversational AI and document Q&A applications. |
| Message and execution metering | No usage metering. Flat annual fee regardless of how many agents run, how many pipelines execute, or how many engineers use the platform. | Cloud plans meter by message credits. A single agent conversation with multiple steps can consume 5-15 credits. At 100 customer questions per day, a RAG support bot can burn 15,000 credits/month, exceeding the Team plan. |
Different paths through the same technology
Dify and Modulo both use AI agents, but they point them at different problems. Dify builds LLM applications for end users: chatbots, document Q&A tools, and conversational agents. Its visual builder, RAG engine, and knowledge base management are designed for teams shipping AI features to their customers.
Modulo builds governed AI pipelines for the SDLC: agents that handle pull requests, run code review, execute tests, and deploy to production. It does not build customer-facing chatbots. It governs the agents that write, review, and ship your code.
If you need to build an AI application for your users, Dify is purpose-built for that. If you need to govern the AI agents that automate your software delivery pipeline, Modulo is purpose-built for that. The use cases overlap only at the level of "both use AI agents."
The credit metering question
Dify's cloud plans charge by message credits. A single user interaction with a multi-step agent can consume 5 to 15 credits depending on how many model calls it makes. A RAG-backed support bot answering 100 customer questions a day at 5 credits each burns 15,000 credits per month, which exceeds the Team plan's 10,000 credit allowance.
Modulo charges a flat annual fee. Your LLM API costs are separate and under your control, but the platform itself does not meter usage. Running more agents, deploying to more environments, or adding more engineers does not change the bill.
The structural difference: Dify's cost grows with usage because every agent action consumes credits. Modulo's cost stays flat regardless of how many agents you run or how often they execute. For high-volume SDLC automation, flat pricing avoids the surprise of a metered bill at scale.
The licence fine print
Both Dify and n8n call themselves open source, but neither uses an OSI-approved licence. Dify's Community Edition runs under Apache 2.0 with additional conditions: you cannot offer Dify as a hosted multi-tenant service without a commercial license from LangGenius, and you cannot remove the Dify logo. For internal use, this is fine. For commercial hosting, you need an Enterprise license.
Modulo's BUSL licence is more restrictive on the commercial-hosting side but equally permissive for internal use. The source is public and inspectable, and internal use is unrestricted. For teams that want to run the platform on their own infrastructure, the practical difference between the two licences is small.
The key distinction: Dify restricts multi-tenant hosting. Modulo restricts competitive commercial use. Both permit internal use without restriction. If you plan to embed the platform in a product you sell, read both licences carefully before committing.
When Modulo is the better fit
- SDLC-specific agent orchestration. Pipelines compose agents that automate pull requests, code review, testing, and deployment, not build generic LLM applications.
- Governance at a predictable cost. SSO, SCIM, RBAC, audit logging, HITL gates, eval-gated autonomy, and cost controls for $8,000/year flat.
- Self-hosted or air-gapped at every tier, including the free Community edition. No telemetry by default.
- Cost that does not grow with headcount or usage. Adding engineers, running more agents, or deploying to more environments costs nothing extra.
- No message credit metering. The platform cost is flat, and your LLM API costs are separate and under your control.
When Dify is the better fit
- A polished visual workflow builder for LLM applications. Drag-and-drop agent and RAG pipeline construction with a built-in knowledge base.
- Open-source with 150k+ GitHub stars and a large community. Docker Compose self-hosting is straightforward.
- Built-in RAG engine, vector store integration, and document ingestion. Good for building conversational AI and document Q&A tools without external orchestration.
- Free Community Edition with all core features for internal use. No license fee for self-hosting.
- Cloud pricing starts at $59/month for a capable team tier. Low barrier to entry for small teams.
Try Modulo
Modulo is self-hosted. You can evaluate it without talking to anyone.
Questions? Open a discussion on GitHub.