Farna Control vs Dify

Evidence in this comparison uses four labels: observed facts come from a connector or runtime, derived values are calculated from observed data, reported values are claims made by an agent or external system, and attested values carry a verifiable human or cryptographic assertion.

Farna Control is a Delivery Governance Platform for the software lifecycle. Dify is a visual platform for building LLM applications. Both can be self-hosted, and Dify can compose with a governed delivery pipeline when an application team needs that boundary.

Dimension	Farna Control	Dify
Primary use case	Agentic Delivery Governance for the software lifecycle. Composes agents, connectors and human gates into governed delivery pipelines across pull requests, code review, testing and deployment.	LLM application building. Its official materials describe a visual workflow builder for AI agents, RAG-powered applications and multi-step LLM workflows, aimed at general-purpose AI applications.
Hosting and data residency	Self-hosted only. Runs on your infrastructure, in your cloud or behind a VPN. It is air-gap capable. No telemetry by default, opt-in only. 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/farna-control repository is public and inspectable. The license 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 license as Other.
Governance features	SSO/SAML, SCIM provisioning, team RBAC, append-only audit log with hash-chaining, HITL gates, bounded autonomy with per-pipeline autonomy levels, and capability scopes, all in the Team tier. Token budgets, the pipeline spend circuit breaker, and the org-wide pause are in every tier; Team adds organization and team spend limits, a budget overview, and a cost breakdown.	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	One annual license per organization, 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. Current price on /control/pricing.	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. As published at dify.ai/pricing, September 2026.
AI and agent capabilities	Agent nodes compose AI models (Claude, Copilot, Cursor, your own) into typed pipelines. Typed schemas define inputs and outputs. You set each pipeline's autonomy level, with eval scores as the evidence for raising it. 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.
Evidence and provenance	The governed object is the delivery run. Its record distinguishes observed facts, derived values, reported claims, and attested decisions across the systems it touches.	Application runs and traces support debugging. We found no public documentation for a delivery-wide evidence record with those four provenance classes.
Controls and autonomy	Schemas, credentials, budgets, HITL gates and earned autonomy are enforced outside the model and can progress from approval to notification to autonomous execution.	Dify provides application workflow and model controls. The reviewed sources do not describe eval-backed autonomy progression for delivery agents.
How products compose	Farna Control governs delivery workflows; Dify can remain the application or agent-building layer for customer-facing AI.	Dify-built applications can be connected to a Farna Control-governed delivery process when the application team needs that boundary.

Different paths through the same technology

Dify and Farna Control 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.

Farna Control 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, Farna Control 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.

Farna Control 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. Farna Control's cost stays flat regardless of how many agents you run or how often they execute. For high-volume SDLC automation, Dify's cost follows usage; Farna Control's license cost does not.

The license fine print

Both Dify and n8n call themselves open source, but neither uses an OSI-approved license. 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.

Farna Control's BUSL license 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 licenses is small.

The key distinction: Dify restricts multi-tenant hosting. Farna Control restricts competitive commercial use. Both permit internal use without restriction. If you plan to embed the platform in a product you sell, read both licenses carefully before committing.

When Farna Control is the better fit

SDLC-specific agent orchestration. Pipelines compose agents that automate pull requests, code review, testing, and deployment.
Governance at a predictable cost. SSO, SCIM, RBAC, audit logging, HITL gates, bounded autonomy, and cost controls under one flat annual license.
Self-hosted at every tier, including the free Community edition. No telemetry by default, opt-in only.
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 Farna Control

Farna Control is self-hosted. You can evaluate it without talking to anyone.

Community edition (free) Team tier (see /control/pricing)

Questions? Open a discussion on GitHub.