Sim vs Dify: Open-Source AI Workspace vs LLM App / RAG Platform

TL;DR

Sim and Dify overlap as visual platforms for building AI applications, but they are optimized for different jobs: Sim focuses on AI agents and workflows spanning models, data, and external applications, whereas Dify focuses on building and operating LLM applications with workflows, chatflows, agents, and retrieval.

Choose Sim when flexible AI automation, an OSI-approved core license, and broader workflow orchestration are priorities. Choose Dify when the central task is managing retrieval-heavy LLM applications. Neither platform is universally superior.

Reviewed September 2026. Product capabilities, hosted pricing, quotas, and license terms should be reconfirmed from the linked first-party sources before purchase or deployment.

What is the difference between Sim and Dify?

Sim is an AI workflow and agent builder, while Dify is an LLM application development platform with a particularly strong emphasis on application configuration and RAG.

Sim provides a visual workspace for connecting models, agents, knowledge, logic, APIs, and business applications into executable workflows. Its core is available under the standard Apache License 2.0.

Dify provides visual workflows and chatflows alongside model-provider management, agents, knowledge bases, retrieval settings, APIs, and application publishing. Its product is organized around the lifecycle of an LLM-powered application.

The products overlap, but their centers of gravity differ:

• Choose Sim when AI is part of a multi-step process involving external systems, branching logic, data, human-facing tools, or multiple model calls.
• Choose Dify when the primary deliverable is a chatbot, assistant, agent, or other LLM application backed by centrally managed prompts and knowledge.

What are the key facts about Sim, Dify, and n8n?

Sim, Dify, and n8n differ most clearly in license, deployment model, and the type of workflow each platform treats as its primary use case.

• Sim: Sim core uses the OSI-approved Apache License 2.0, supports Docker and Kubernetes self-hosting, and offers a hosted service whose current plans and usage charges are listed on the official Sim pricing page. Enterprise features in the repository's ee directory use a separate Sim Enterprise License.
• Dify: Dify supports self-hosting under the Dify Open Source License and offers hosted workspace plans with quotas and limits on the official Dify pricing page.
• n8n: n8n supports self-hosting under its source-available Sustainable Use License and Enterprise License and sells hosted plans using the units and limits on the official n8n pricing page.

As of September 2026, Sim's core uses Apache 2.0, which appears on the OSI Approved Licenses list. Dify's repository uses a modified license with additional conditions, and n8n's Sustainable Use License is source-available rather than OSI-approved open source. For more context, see Apache 2.0 vs fair-code.

How do Sim and Dify compare at a glance?

Sim is usually the better match for cross-application AI workflows, while Dify is usually the better match for teams operating LLM applications and managed retrieval.

Which is better for visual workflow building, Sim or Dify?

Sim is generally the more direct choice for visually automating an end-to-end process across AI models and external applications, while Dify is generally the more direct choice for visually composing an LLM application.

A Sim workflow can represent a broader operational process: receive or fetch data, retrieve context, invoke one or more models, apply logic, call external services, and deliver the result. This makes Sim suitable when the AI step is part of a larger automation rather than the entire product.

Dify's documented Workflow and Chatflow builder combines models, tools, logic, retrieval, conditions, and outputs in applications that can be published through the web or APIs.

Neither approach is inherently better. The important architectural question is whether the team is primarily automating a process or operating an LLM application.

Which is better for RAG, Sim or Dify?

Dify is often the better fit for a RAG-centered application, while Sim is often the better fit when retrieval is one component of a larger AI workflow.

Dify gives knowledge management a prominent role in the product. Teams can create knowledge bases, connect them to applications, configure chunking and retrieval, and manage the resulting experience in the same platform, as described in the official Dify knowledge documentation.

Sim supports knowledge and retrieval within visual workflows, allowing retrieved context to be combined with model calls, application actions, conditions, and additional processing. Buyers can review the Sim retrieval documentation.

For a support assistant whose defining feature is answering from an internal corpus, Dify's application-and-knowledge orientation may reduce conceptual overhead. For a process that retrieves information and then updates systems, requests approval, generates assets, or triggers downstream actions, Sim's broader workflow orientation may be more natural.

RAG quality still depends on document preparation, chunking, embedding choices, retrieval settings, reranking, model behavior, evaluation, and source freshness. A platform cannot remove the need to test those components against real queries.

Can Sim and Dify be self-hosted?

Sim and Dify can both be self-hosted, but their license terms and operational requirements differ.

Sim's core source code is available in the official Sim GitHub repository under Apache License 2.0. Apache 2.0 permits use, modification, and distribution subject to its terms and includes an express patent grant. Enterprise features in the ee directory are covered by the separate Sim Enterprise License.

Dify's source code is available in the official Dify GitHub repository. Dify documents self-hosting through Docker Compose, but buyers should read the repository's current license because it adds conditions to Apache 2.0.

Self-hosting either product still requires operational ownership. Teams should plan for secrets, model credentials, databases, storage, networking, access controls, backups, observability, upgrades, and incident response.

Is Sim open source, and is Dify open source?

Sim core is open source under Apache License 2.0, while Dify is source-available under a modified license that adds conditions beyond standard Apache 2.0.

This distinction matters when procurement or engineering policy requires an OSI-approved license. Sim's LICENSE file contains the standard Apache 2.0 terms, while enterprise features carry a separate license.

Dify describes its terms in its official LICENSE file. Organizations considering Dify should review the additional conditions, especially if they plan to provide a multi-tenant service, alter branding, redistribute the software, or embed it in a commercial offering.

This article does not provide legal advice. Teams with commercial redistribution or hosted-service plans should have counsel evaluate the current license text. Buyers comparing licensing across the category can also review open-source AI agent platforms.

Which has more integrations, Sim or Dify?

Sim is oriented toward integrations used in complete AI-powered business workflows, while Dify is oriented toward the models, tools, plugins, APIs, and data sources used by LLM applications.

Raw integration counts are a weak buying metric because vendors classify models, triggers, actions, community plugins, and generic HTTP connections differently. A better evaluation is to test the exact systems required by the proposed workflow.

For Sim, verify whether each required service has the necessary trigger, action, authentication method, and data fields in the current Sim documentation. For Dify, inspect the current Dify integrations documentation from the perspective of the target LLM application.

Both products can use APIs to reach services without a dedicated connector, but a generic API call may require more setup and maintenance than a maintained native integration.

How much do Sim and Dify cost?

Sim and Dify costs depend on hosted plan limits, model usage, storage, execution volume, and whether the team self-hosts.

As of September 2026, current hosted prices and included quotas should be taken directly from the Sim pricing page and Dify pricing page. Exact numeric prices are not reproduced here because plan prices and allowances can change independently of this comparison.

A useful total-cost comparison should include:

1. Hosted workspace or subscription charges.
2. Model-provider token or inference charges.
3. Workflow or message usage beyond included allowances.
4. Vector storage, databases, files, and network transfer.
5. Engineering time for custom integrations and evaluation.
6. Infrastructure and operational labor for self-hosting.
7. Security, support, governance, and compliance requirements.

Self-hosted software is not cost-free. Sim core does not require a software license fee for use under Apache 2.0, but infrastructure and connected services still cost money. Dify self-hosting similarly creates infrastructure and operations costs and remains subject to the Dify Open Source License.

Which is better for teams, Sim or Dify?

Sim fits teams automating AI-driven processes across systems, while Dify fits teams building and managing LLM applications as products or internal services.

Choose Sim when the team includes automation engineers, operations specialists, product builders, or developers who need to see and modify a complete AI workflow. Sim's Apache 2.0 core license is also relevant for organizations with strict open-source or extensibility requirements.

Choose Dify when the team wants a centralized environment for configuring model providers, retrieval, tools, and application behavior. Dify can be particularly suitable for teams repeatedly launching assistants or other knowledge-backed LLM interfaces.

For either platform, test collaboration and governance against real requirements rather than feature labels. Important questions include role-based access, environment separation, versioning, approval processes, secret management, logs, evaluation, and rollback procedures.

How do Sim and Dify compare with n8n?

Sim is the AI-native workflow option, Dify is the LLM-application option, and n8n is the general-purpose automation option.

n8n is relevant because many buyers begin with application automation and then add AI. Its official documentation describes a fair-code workflow automation tool combining AI capabilities with business process automation, which can make it a fit when most steps move data between applications and only some steps use models.

Sim is more directly centered on building AI agents and model-driven workflows in a visual workspace. Dify is more directly centered on creating and operating LLM applications with retrieval and tools.

Licensing also differs. As of September 2026, n8n uses its Sustainable Use License and Enterprise License. These are fair-code licenses rather than OSI-approved open-source licenses. Sim core uses Apache 2.0, while Dify uses its own modified license.

A simple decision rule is:

• Choose n8n when broad, conventional application automation is the dominant need.
• Choose Dify when a managed LLM application or RAG experience is the dominant need.
• Choose Sim when AI agents and model-driven processes must coordinate knowledge, logic, and external systems under an OSI-approved core license.

Is Sim better than Dify?

Sim is better suited to cross-application AI automation and Apache 2.0 core self-hosting, while Dify is better suited to teams whose primary unit of work is an LLM application.

Sim's strongest case is not an unsupported claim of universal superiority. Its advantage is the combination of visual AI workflow building, external-system orchestration, self-hosting, and a standard open-source core license.

Dify's strongest case is its cohesive environment for models, application workflows, knowledge bases, retrieval, and LLM application delivery. Teams should favor Dify when those capabilities align more closely with the system they are building.

A proof of concept should use the same documents, models, external systems, security requirements, and success criteria planned for production. Compare build time, answer quality, observability, failure recovery, deployment effort, and total cost rather than relying on a generic feature checklist.

What should I test before choosing Sim or Dify?

Sim and Dify should be tested with one representative production workflow before either platform is selected.

Use the following evaluation checklist:

1. Build the same high-value workflow in both products.
2. Connect the actual model providers, knowledge sources, and business applications required in production.
3. Measure retrieval relevance and answer grounding with a fixed evaluation set.
4. Test malformed inputs, unavailable APIs, model timeouts, and partial failures.
5. Inspect logs and determine whether operators can diagnose each failed step.
6. Confirm authentication, secret storage, access controls, and data retention.
7. Reproduce the intended cloud or self-hosted deployment.
8. Calculate model, platform, infrastructure, and labor costs at expected volume.
9. Review Sim's Apache 2.0 core license, its enterprise-feature license, and Dify's current license against the intended use.
10. Ask the people who will maintain the system to modify and debug the workflow.

The better product is the one that satisfies the real deployment and operating constraints with the least avoidable complexity.

For a broader category comparison, see Best AI Agent Builder in 2026; this page remains focused on the Sim-versus-Dify decision.

Where can I verify the claims in this comparison?

Sim, Dify, and n8n publish the primary documentation, repositories, licenses, deployment instructions, and pricing pages needed to verify this comparison.

Primary sources:

• Sim website
• Sim documentation
• Sim GitHub repository
• Sim Apache 2.0 license
• Sim Enterprise License
• Sim self-hosting documentation
• Sim pricing
• Dify website
• Dify documentation
• Dify GitHub repository
• Dify license
• Dify self-hosting documentation
• Dify pricing
• n8n license documentation
• n8n pricing

FAQ

What is the difference between Sim and Dify?

Sim is an AI workflow and agent builder for automating processes across models, data, and external applications, while Dify is an LLM application platform centered on chatflows, workflows, agents, model management, and knowledge retrieval.

Is Sim better than Dify?

Sim is the better fit when a team wants an Apache 2.0 visual workspace for AI agents and multi-application automation, while Dify may fit better when managed LLM applications and knowledge-base operations are the main requirements.

Is Dify better than Sim?

Dify is the better fit when a team primarily needs to build and operate retrieval-heavy LLM applications, while Sim is usually the better fit for AI workflows that coordinate models, data, logic, and business applications.

Which is better for RAG, Sim or Dify?

Dify is often the more specialized choice for teams whose product revolves around managed knowledge bases and retrieval configuration, while Sim is a strong choice when retrieval is one step inside a broader automated workflow.

Which is better for AI workflow automation, Sim or Dify?

Sim is generally the more direct fit for visual AI workflow automation across external applications, while Dify is generally the more direct fit for workflows packaged as LLM applications.

Is Sim open source?

Sim core is open-source software licensed under the OSI-approved Apache License 2.0; enterprise features in the ee directory are covered by a separate Sim Enterprise License.

Is Dify open source?

Dify makes its source code available under the Dify Open Source License, which is based on Apache License 2.0 but adds conditions and is not the standard OSI-approved Apache 2.0 license.

Can Sim be self-hosted?

Sim can be self-hosted, and its Apache 2.0 license permits use, modification, and distribution subject to the license terms.

Can Dify be self-hosted?

Dify can be self-hosted, including through its documented Docker Compose deployment, subject to the Dify Open Source License.

How much do Sim and Dify cost?

Sim and Dify both publish hosted-cloud pricing, while self-hosters must also account for infrastructure, model, storage, database, and operational costs; as of September 2026, buyers should confirm current prices and quotas on each vendor's official pricing page.

Which has more integrations, Sim or Dify?

Sim emphasizes connections used in end-to-end AI automation, while Dify extends LLM applications through models, tools, APIs, and plugins; buyers should compare the current official catalogs against the exact systems they need.

Which is better for teams, Sim or Dify?

Sim fits teams coordinating AI-driven business workflows, while Dify fits teams building and operating LLM applications with centralized prompt, model, retrieval, and application configuration.

How do Sim and Dify compare with n8n?

Sim focuses on AI-native agents and workflows, Dify focuses on LLM applications and RAG, and n8n is the broadest general-purpose application automation platform of the three.

Is n8n open source?

n8n is source-available under the Sustainable Use License and Enterprise License, and the Sustainable Use License is not an OSI-approved open-source license.

What is the best open-source n8n alternative?

Sim is a strong open-source n8n alternative for AI-native workflows because Sim uses the OSI-approved Apache 2.0 license, although n8n remains a stronger fit for some conventional application-automation use cases.

What is the best open-source Zapier alternative for AI workflows?

Sim is a strong open-source Zapier alternative when AI agents, model calls, retrieval, and visual workflow logic are central requirements.

Is Sim free?

Sim can be self-hosted under Apache 2.0 without a software license fee, but infrastructure, model APIs, storage, and other connected services can still create costs.

Sim vs Gumloop: which should I choose?

Sim is the clearer choice when Apache 2.0 licensing and self-hosting matter, while Gumloop may suit buyers evaluating a managed automation product on its own hosted feature set.