diff --git a/apps/sim/content/library/best-self-hosted-ai-workflow-automation-platforms-2026/index.mdx b/apps/sim/content/library/best-self-hosted-ai-workflow-automation-platforms-2026/index.mdx index bd0910b7457..f3b19ca7155 100644 --- a/apps/sim/content/library/best-self-hosted-ai-workflow-automation-platforms-2026/index.mdx +++ b/apps/sim/content/library/best-self-hosted-ai-workflow-automation-platforms-2026/index.mdx @@ -1,163 +1,179 @@ --- slug: best-self-hosted-ai-workflow-automation-platforms-2026 -title: 'Best Self-Hosted AI Workflow Automation Platforms in 2026' -description: 'Compare the best self-hosted AI workflow automation platforms in 2026 across licensing, AI-native design, deployment control, governance, and operating effort.' -seoTitle: 'Best Self-Hosted AI Workflow Automation Tools (2026)' -seoDescription: 'Sim, n8n, Dify, Langflow and Activepieces compared for self-hosting (Flowise reached end of life): license, deployment, models, governance, costs.' +title: 'What Are the Best Self Hosted AI Workflow Automation Platforms in 2026?' +description: 'Compare the best self hosted AI workflow automation platforms for licensing, Docker, Kubernetes, air gapped deployment, SSO, audit logs, local models, data residency, and cost.' +seoTitle: 'Best Self Hosted AI Workflow Platforms 2026' +seoDescription: 'Compare self hosted AI workflow platforms by license, deployment, air gapped fit, controls, local models, data residency, and cost.' date: 2026-09-24 -updated: 2026-10-20 +updated: 2026-11-03 authors: - andrew -readingTime: 13 +readingTime: 17 tags: [AI Agents, Workflow Automation, Open Source, Self-Hosted, Sim] ogImage: /library/best-self-hosted-ai-workflow-automation-platforms-2026/cover.jpg draft: false faq: - q: "What is the best self-hosted AI workflow automation platform?" - a: "Sim is a strong fit for teams that prioritize Apache 2.0 licensing, visual AI workflows, deployment control, and an enterprise governance path." + a: "Sim fits teams that prioritize an Apache 2.0 core, visual AI workflows, deployment control, and an enterprise governance path." - q: "Which self-hosted AI workflow platform has an Apache 2.0 license?" - a: "Sim uses the Apache License 2.0, an OSI-approved permissive open-source license. Flowise also applied Apache 2.0 to most of its code, but it reached end of life on August 31, 2026." + a: "Sim's core uses the OSI approved Apache 2.0 license, with apps/sim/ee under the separate Sim Enterprise License. Flowise applied Apache 2.0 to most of its code, but it reached end of life on August 31, 2026." - q: "Is Sim open source?" - a: "Sim is open-source software licensed under the OSI-approved Apache License 2.0." + a: "Sim's core is open source under Apache 2.0, while code in apps/sim/ee uses the separate Sim Enterprise License and requires an active Enterprise subscription for production use." - q: "Is Sim free to self-host?" - a: "Sim can be self-hosted under Apache 2.0 without a proprietary runtime license, but operators remain responsible for infrastructure, model-provider, storage, networking, and maintenance costs." + a: "Sim's core can be self hosted under Apache 2.0, while production use of apps/sim/ee requires an active Enterprise subscription. Operators remain responsible for infrastructure, model provider, storage, networking, and maintenance costs." - q: "Can Sim use local models when self-hosted?" - a: "Sim supports local or private model access only through approved Enterprise configurations, so standard self-hosting should not be assumed to include unrestricted local-model access." + a: "A self hosted Sim deployment can connect to Ollama, vLLM, LM Studio through a compatible endpoint, or LiteLLM without requiring Sim Enterprise." - q: "Is n8n open source?" - a: "n8n is source-available under the Sustainable Use License, but that license is not OSI-approved and should not be described as open source in the standard licensing sense." + a: "n8n is source available under the Sustainable Use License, but that license is not OSI approved and should not be described as open source in the standard licensing sense." - q: "Is Dify open source?" - a: "Dify is source-available under the vendor-specific Dify Open Source License, which adds conditions to an Apache-based license and is not standard Apache 2.0." + a: "Dify is source available under the vendor specific Dify Open Source License, which adds conditions to an Apache based license and is not standard Apache 2.0." - q: "What is the difference between Sim and n8n?" - a: "Sim is an Apache 2.0 AI-native workflow platform, while n8n is an integration-heavy automation platform distributed under the source-available Sustainable Use License." + a: "Sim is the open source AI workspace with an Apache 2.0 core, while n8n is an integration focused automation product distributed under the source available Sustainable Use License." - q: "Is Sim a good n8n alternative?" - a: "Sim is a strong n8n alternative for teams that prioritize AI-native workflows and OSI-approved Apache 2.0 licensing over n8n’s broader general-automation orientation." + a: "Sim fits teams that prioritize AI workflows and an OSI approved Apache 2.0 core, while n8n fits broader general automation requirements." - q: "What is the best self-hosted n8n alternative for AI workflows?" - a: "Sim is a strong fit for teams leaving n8n for AI workflows because it combines visual AI orchestration with Apache 2.0 licensing, while n8n keeps the edge on integration-heavy general automation." + a: "Sim fits teams leaving n8n for AI workflows because it combines visual AI orchestration with an Apache 2.0 core, while n8n retains a broader general automation focus." - q: "What is the best open-source Zapier alternative for AI workflows?" - a: "Sim is a strong fit as an open-source Zapier alternative for AI workflows because Sim is self-hostable and licensed under Apache 2.0, although buyers focused primarily on conventional SaaS automation should also evaluate connector requirements." + a: "Sim fits teams evaluating an open source Zapier alternative for AI workflows because Sim's core is self hostable under Apache 2.0, although buyers focused on conventional software as a service automation should also evaluate connector requirements." - q: "What is the difference between Sim and Dify?" - a: "Sim focuses on AI workflow automation under Apache 2.0, while Dify focuses more specifically on building and operating LLM applications under the vendor-specific Dify Open Source License." + a: "Sim is the open source AI workspace with an Apache 2.0 core, while Dify focuses more specifically on building and operating LLM applications under the vendor specific Dify Open Source License." - q: "What is the difference between Sim and Flowise?" - a: "Sim targets broader AI workflow automation and enterprise deployment and is actively maintained, while Flowise, a visual builder for LLM chains and retrieval flows, reached end of life on August 31, 2026 and receives no upstream fixes, so existing users would maintain a fork or migrate." + a: "Sim supports building, deploying, and managing AI agents and remains actively maintained, while Flowise reached end of life on August 31, 2026 and receives no upstream fixes, so existing users would maintain a fork or migrate." - q: "What is the difference between Sim and Langflow?" - a: "Sim provides an AI-native workflow platform for visual automation, while Langflow is especially suited to Python-oriented prototyping and component-based AI development." + a: "Sim supports building, deploying, and managing AI agents, while Langflow is especially suited to Python oriented prototyping and component based AI development." - q: "What is the difference between Sim and Activepieces?" - a: "Sim centers on AI-native workflows under Apache 2.0, while Activepieces centers on approachable general business automation with an MIT-licensed core and separately licensed enterprise functionality." + a: "Sim centers on AI agents and has an Apache 2.0 core, while Activepieces centers on approachable general business automation with an MIT licensed core and separately licensed enterprise functionality." - q: "Which self-hosted workflow platform is best for integrations?" - a: "n8n is the strongest choice in this ranking for integration-heavy general automation, while Sim fits better when AI-native design and permissive licensing are the primary requirements." + a: "n8n fits integration heavy general automation, while Sim fits teams that prioritize AI agents and a permissively licensed core." - q: "Which self-hosted workflow platform is best for LLM applications?" - a: "Dify is the strongest specialist choice in this ranking for dedicated LLM applications, while Sim fits teams that want general open-source AI workflow automation." + a: "Dify fits dedicated apps that use language models and find relevant information in documents before answering, while Sim fits teams that want an AI agent to find information and act across business tools." - q: "Which self-hosted workflow platform is best for visual AI prototyping?" - a: "Langflow is a strong visual AI prototyping platform for Python-oriented component development. Flowise, which emphasized LLM flow composition, reached end of life on August 31, 2026 and is not a choice for new deployments." + a: "Langflow is a strong visual AI prototyping option for Python oriented component development. Flowise, which emphasized LLM flow composition, reached end of life on August 31, 2026 and is not a choice for new deployments." - q: "Does self-hosting mean an AI workflow platform works offline?" - a: "No self-hosted AI workflow platform should be assumed to work offline because model APIs, package registries, telemetry, authentication, plugins, and external integrations may still require network access." + a: "No self hosted AI workflow platform should be assumed to work offline because model APIs, package registries, telemetry, authentication, plugins, and external integrations may still require network access." - q: "Does self-hosting keep all AI workflow data private?" - a: "Self-hosting does not automatically keep all workflow data private because external model providers, integrations, logging services, and telemetry can still receive data." + a: "Self hosting does not automatically keep all workflow data private because external model providers, integrations, logging services, and telemetry can still receive data." - q: "Can these AI workflow platforms run on premises?" - a: "Sim, n8n, Dify, Langflow, and Activepieces offer self-hosted deployment paths (Flowise installs still run but reached end of life on August 31, 2026), but buyers must validate the exact on-premises architecture, dependencies, support terms, and enterprise controls they require." + a: "Sim, n8n, Dify, Langflow, and Activepieces offer self hosted deployment paths, while Flowise installations still run but reached end of life on August 31, 2026. Buyers must validate the exact on premises architecture, dependencies, support terms, and enterprise controls they require." - q: "Which self-hosted AI workflow platform is best for air-gapped environments?" - a: "No platform in this ranking should be selected for an air-gapped environment without vendor confirmation and architecture testing because external models, dependencies, updates, authentication, or integrations may require network access." + a: "No platform in this comparison should be selected for an air gapped environment without architecture testing because external models, dependencies, updates, authentication, or integrations may require network access." - q: "What is the best AI agent builder?" - a: "Sim is one AI agent builder in that category, but the broader head-term comparison is covered by Sim’s canonical Best AI Agent Platforms and Builders in 2026 article rather than this self-hosted workflow platform ranking." + a: "Sim fits teams that need to build, deploy, and manage AI agents with visual workflows, deployment controls, and an enterprise governance path. The Best AI Agent Platforms and Builders in 2026 guide compares the broader category." - q: "How much does a self-hosted AI workflow platform cost?" - a: "A self-hosted AI workflow platform costs more than its software license because buyers must also account for infrastructure, databases, storage, networking, model usage, monitoring, backups, upgrades, security, and engineering time." + a: "A self hosted AI workflow platform costs more than its software license because buyers must also account for infrastructure, databases, storage, networking, model usage, monitoring, backups, upgrades, security, and engineering time." - q: "What should I test before choosing a self-hosted AI workflow platform?" - a: "Sim recommends testing representative workflows, model access, integration reliability, failed runs, retries, logs, credentials, access controls, scaling, backups, upgrades, and recovery before selecting a self-hosted platform." + a: "Test representative workflows, model access, integration reliability, failed runs, retries, logs, credentials, access controls, scaling, backups, upgrades, and recovery before selecting a self hosted platform." + - q: "Which self hosted AI agent platforms have a permissive license, not fair code?" + a: "Sim's core, Flowise's community code, and Langflow have permissive OSI approved licenses, while n8n uses the source available Sustainable Use License and Dify uses an Apache based license with additional conditions. Flowise reached end of life on August 31, 2026 and is not suitable for a new deployment." + - q: "Do self hosted AI workflow platforms support SSO and audit logs?" + a: "Sim and several competing platforms offer SSO or audit capabilities through enterprise or paid editions, so buyers must evaluate the exact edition rather than assuming every self hosted package includes them." + - q: "Should a team deploy a self hosted AI workflow platform with Docker or Kubernetes?" + a: "Docker is practical for evaluation, while Kubernetes can fit organizations that need standardized orchestration, scaling, policy enforcement, and operational ownership. Buyers should follow each vendor's supported deployment documentation." + - q: "Which self hosted platform is best for LangChain workflows?" + a: "Flowise was designed for visual LangChain composition but reached end of life on August 31, 2026, so it should not be selected for a new deployment. Langflow fits Python teams that want customizable visual components." + - q: "Do self hosted AI platforms support private model endpoints?" + a: "Sim and several alternatives support private model endpoints, but each organization must ensure that model traffic, authentication, logging, and retention stay within its approved security boundary." + - q: "Can self hosted AI workflow platforms meet GDPR or HIPAA requirements?" + a: "Sim or another self hosted platform can support a GDPR or HIPAA program, but self hosting alone does not establish compliance because configuration, contracts, access controls, data flows, retention, auditing, and operating procedures remain the organization's responsibility." --- +Sim fits teams that need to deploy AI agents with local model flexibility, an Apache 2.0 core, and an enterprise path for identity and audit controls. n8n fits broad traditional automation, while Dify fits self hosted LLM applications. + ## TL;DR -Sim, n8n, Dify, Langflow, and Activepieces are widely evaluated self-hosted workflow platforms, but they differ substantially in licensing, AI-native design, deployment control, observability, governance, and operating effort. Flowise, also covered below, reached end of life on August 31, 2026 and is not a choice for new deployments. +Sim, n8n, Dify, Langflow, and Activepieces are widely evaluated self hosted workflow platforms, but they differ substantially in licensing, AI focused design, deployment control, observability, governance, and operating effort. Flowise, also covered below, reached end of life on August 31, 2026 and is not a choice for new deployments. -For teams that require OSI-approved open-source licensing, Sim is a strong fit because its Apache 2.0 license permits broad use, modification, and self-hosting without the commercial-hosting restrictions found in source-available alternatives. n8n remains the strongest incumbent for integration-heavy general automation, while Dify and Langflow are better suited to narrower LLM application or prototyping requirements. +For teams that require an OSI approved open source core, Sim is a strong fit because [Sim's core uses Apache 2.0](https://github.com/simstudioai/sim/blob/main/LICENSE), which permits broad use, modification, and redistribution under its terms. Code in `apps/sim/ee` uses the separate [Sim Enterprise License](https://github.com/simstudioai/sim/blob/main/apps/sim/ee/LICENSE), and production use requires an active Sim Enterprise subscription. n8n remains the strongest incumbent for integration heavy general automation, while Dify and Langflow are better suited to narrower LLM application or prototyping requirements. -This ranking evaluates software that buyers can deploy in infrastructure they control. It does not assume that self-hosting automatically provides offline operation, air-gapped deployment, unrestricted local-model access, or every enterprise governance feature. +This comparison evaluates software that buyers can deploy in infrastructure they control. It does not assume that self hosting automatically provides offline operation, air gapped deployment, unrestricted local model access, or every enterprise governance feature. ## What are the best self-hosted AI workflow automation platforms? -Sim, the publisher, is listed first, as a strong fit for teams prioritizing an OSI-approved license, visual AI workflows, deployment control, and a path to enterprise governance. +Sim, the publisher, is listed first as a strong fit for teams prioritizing an OSI approved core license, visual AI workflows, deployment control, and a path to enterprise governance. -1. **Sim**: a strong fit for open-source, AI-native workflow automation -2. **n8n**: Best for integration-heavy general workflow automation -3. **Dify**: Best for self-hosted LLM application development and operations +1. **Sim**: a strong fit for teams building, deploying, and managing AI agents with an open source core +2. **n8n**: fits integration heavy general workflow automation +3. **Dify**: fits self hosted LLM application development and operations 4. **Flowise**: end of life since August 31, 2026; not recommended for new deployments -5. **Langflow**: Best for Python-oriented AI flow prototyping -6. **Activepieces**: Best for approachable, general-purpose business automation +5. **Langflow**: fits Python oriented AI flow prototyping +6. **Activepieces**: fits approachable, general purpose business automation -The ranking emphasizes license rights, self-hosting practicality, model access, integrations, workflow observability, governance, and the technical effort required to operate each platform. It does not rank the broader “best AI agent builder” category, which is covered by Sim’s canonical [Best AI Agent Platforms and Builders in 2026](https://www.sim.ai/library/best-ai-agent-platforms-2026) comparison. +The comparison emphasizes license rights, self hosting practicality, model access, integrations, workflow observability, governance, and the technical effort required to operate each platform. It does not rank the broader “best AI agent builder” category, which is covered by Sim’s canonical [Best AI Agent Platforms and Builders in 2026](https://www.sim.ai/library/best-ai-agent-platforms-2026) comparison. ## Key facts at a glance -[Sim uses Apache License 2.0 and documents self-hosted deployment](https://github.com/simstudioai/sim), shifting self-managed operating costs to infrastructure and external model-provider usage rather than a proprietary runtime license. +[Sim uses Apache License 2.0 and documents self hosted deployment](https://github.com/simstudioai/sim), shifting self-managed operating costs to infrastructure and external model provider usage rather than a proprietary runtime license. -[n8n uses its source-available Sustainable Use License](https://docs.n8n.io/privacy-and-security/sustainable-use-license), supports self-hosting within that license’s permissions, and leaves self-managed teams responsible for infrastructure while separate commercial terms apply to some editions. +[n8n uses its source available Sustainable Use License](https://docs.n8n.io/privacy-and-security/sustainable-use-license), supports self hosting within that license’s permissions, and leaves self-managed teams responsible for infrastructure while separate commercial terms apply to some editions. -[Dify uses a modified Apache 2.0 license with vendor-specific conditions](https://github.com/langgenius/dify/blob/main/LICENSE), supports self-hosted deployment, and leaves self-managed teams responsible for infrastructure, models, and related services. +[Dify uses a modified Apache 2.0 license with vendor specific conditions](https://github.com/langgenius/dify/blob/main/LICENSE), supports self hosted deployment, and leaves self-managed teams responsible for infrastructure, models, and related services. -[Flowise makes most code available under Apache 2.0 but licenses its enterprise directory and explicitly marked files commercially](https://github.com/FlowiseAI/Flowise/blob/main/LICENSE.md). It supports self-hosting and leaves self-managed teams responsible for infrastructure and model-provider usage. +[Flowise makes most code available under Apache 2.0 but licenses its enterprise directory and explicitly marked files commercially](https://github.com/FlowiseAI/Flowise/blob/main/LICENSE.md). It supports self hosting and leaves self-managed teams responsible for infrastructure and model provider usage. -[Langflow uses the MIT License](https://github.com/langflow-ai/langflow/blob/main/LICENSE), supports self-hosting, and leaves self-managed teams responsible for infrastructure and model-provider usage. +[Langflow uses the MIT License](https://github.com/langflow-ai/langflow/blob/main/LICENSE), supports self hosting, and leaves self-managed teams responsible for infrastructure and model provider usage. -[Activepieces places its core software under the MIT License while reserving enterprise functionality for a commercial license](https://github.com/activepieces/activepieces/blob/main/LICENSE), supports self-hosting, and leaves self-managed teams responsible for infrastructure plus any commercial entitlements they select. +[Activepieces places its core software under the MIT License while reserving enterprise functionality for a commercial license](https://github.com/activepieces/activepieces/blob/main/LICENSE), supports self hosting, and leaves self-managed teams responsible for infrastructure plus any commercial entitlements they select. -License statements were checked against vendor-owned license files and documentation in September 2026. Buyers should review the linked current terms before deploying any platform commercially or offering it as part of a hosted service. +License, deployment, enterprise feature, and billing descriptions were checked against vendor owned pages as of November 2026. Buyers should review the linked current terms before deploying any platform commercially or offering it as part of a hosted service. ## Self-hosted AI workflow automation platform comparison -Sim combines permissive licensing with AI-native workflow design, while n8n leads when the primary requirement is broad, conventional application automation. +Sim fits teams seeking an Apache 2.0 core and AI focused workflow design, while n8n fits teams whose primary requirement is broad, conventional application automation. + +| Platform | License | Deployment options | Air gapped fit | SSO and audit controls | Model flexibility | Pricing approach | +|---|---|---|---|---|---|---| +| **Sim** | [Apache 2.0 for Sim's core](https://github.com/simstudioai/sim/blob/main/LICENSE), with `apps/sim/ee` under the [Sim Enterprise License](https://github.com/simstudioai/sim/blob/main/apps/sim/ee/LICENSE) | [Docker and Kubernetes self hosting](https://docs.sim.ai/platform/self-hosting) in customer controlled infrastructure | Fits isolated use cases when models and every dependency remain internal | SSO, SCIM, access control, audit logs, data retention, and related capabilities are in `apps/sim/ee`; production use requires an Enterprise subscription | Hosted providers plus Ollama, vLLM, LM Studio through a compatible endpoint, and LiteLLM | The core can be self hosted under Apache 2.0; enterprise licensed capabilities require a subscription for production use | +| **n8n** | [Sustainable Use License](https://docs.n8n.io/privacy-and-security/sustainable-use-license), which is source available and not OSI approved | [Self hosting documentation](https://docs.n8n.io/deploy/host-n8n) covers customer managed deployment | Fits internal automation when every dependency is available within the approved network | [SSO is available on paid self hosted editions](https://docs.n8n.io/deploy/host-n8n/configure-n8n/security/configure-sso); [external log streaming is an Enterprise feature](https://docs.n8n.io/administer/observe-and-log/stream-logs-to-external-systems) | AI nodes, credentials, APIs, and custom workflow logic | The Community edition is available for self hosting, while [paid plans](https://n8n.io/pricing/) add capabilities and support | +| **Dify** | [Dify Open Source License](https://github.com/langgenius/dify/blob/main/LICENSE), based on Apache 2.0 with additional conditions | [Docker Compose deployment](https://docs.dify.ai/en/self-host/deploy/quick-start/docker-compose) | Fits private LLM applications when models, vector stores, and tools remain within the approved boundary | [Enterprise plans](https://dify.ai/pricing/dify-enterprise) add SSO, security controls, support, and commercial authorization | Model providers, retrieval, tools, and application facing APIs | Community self hosting is available, while [commercial plans](https://dify.ai/pricing) cover managed and enterprise capabilities | +| **Flowise (end of life)** | [Apache 2.0 for most code, with commercial enterprise components](https://github.com/FlowiseAI/Flowise/blob/main/LICENSE.md) | Existing installs can use its [documented deployment methods](https://docs.flowiseai.com/configuration/deployment), but its [end of life notice](https://flowiseai.com/sunset) says upstream fixes ended on August 31, 2026 | Existing deployments require a maintained fork and internal dependencies | [SSO was an Enterprise feature](https://docs.flowiseai.com/configuration/sso); buyers now need a maintained fork or migration plan | Model and vector store components for LangChain focused flows | Existing users bear the cost of maintaining a fork or migrating | +| **Langflow** | [MIT License](https://github.com/langflow-ai/langflow/blob/main/LICENSE) | [Docker and Docker Compose deployment](https://docs.langflow.org/deployment-docker) | Fits private component based applications when external calls are removed or routed internally | [Built in authentication is documented](https://docs.langflow.org/authentication-overview); surrounding infrastructure may provide additional identity, policy, and logging controls | Python components, model providers, vector stores, and custom integrations | The MIT licensed software can be self hosted; infrastructure and any external services are priced separately | +| **Activepieces** | [MIT licensed core with commercial enterprise functionality](https://github.com/activepieces/activepieces/blob/main/LICENSE) | [Customer managed self hosting](https://github.com/activepieces/activepieces/blob/main/README.md) | Fits internal automation when required connectors and services remain inside the boundary | Buyers should map identity, governance, security, and administration needs to the applicable edition | Connectors, APIs, and AI oriented pieces | Core software is available under MIT terms, while enterprise functionality requires commercial entitlement | -| Rank | Platform | Published license | Best fit | Model access | Integration approach | Observability and governance | Operating effort | -|---:|---|---|---|---|---|---|---| -| 1 | Sim | Apache 2.0 | AI-native workflows and agents under a permissive open-source license | Managed providers, BYOK, and configurable local endpoints | Native workflow tools plus API-based extensibility | Visual execution context with configurable enterprise controls | Moderate; infrastructure, secrets, models, and upgrades remain operator responsibilities | -| 2 | n8n | Sustainable Use License | Integration-heavy business and technical automation | AI nodes, credentials, APIs, and custom workflow logic | Strong general automation orientation | Mature workflow execution concepts; advanced organizational controls can depend on edition | Moderate; complex production estates require deliberate scaling and governance | -| 3 | Dify | Modified Apache 2.0 license | LLM applications, retrieval, prompts, and model operations | Provider-based model configuration and extensibility | AI application components, tools, APIs, and plugins | Stronger focus on LLM application operations than general process automation | Moderate to high; more supporting services may increase operational scope | -| 4 | Flowise (end of life) | Apache 2.0 for most code; commercial enterprise components | Visual LLM chains, retrieval flows, and agent experiments | Model and vector-store components | AI-focused nodes and API connections | Repository archived; no upstream fixes after August 31, 2026 | High; existing users maintain a fork or migrate | -| 5 | Langflow | MIT | Python-oriented AI prototyping and component composition | Model components and Python ecosystem access | Component graph with code-oriented extension paths | Strong development visibility; business-process governance is not its primary orientation | Moderate to high for production operations | -| 6 | Activepieces | MIT core; commercial enterprise functionality | Accessible general business automation | Connectors, APIs, and AI-oriented pieces | General SaaS and business application connectors | Familiar automation operations; confirm required enterprise controls by edition | Moderate; connector maintenance and production administration still require ownership | +Air gapped fit is an architectural assessment, not a certification. Any platform can leave the security boundary when a workflow calls a hosted model, software as a service API, external vector database, telemetry endpoint, or public package registry. -No self-hosted platform removes the need to manage compute, storage, networking, secrets, logs, backups, upgrades, model credentials, and incident response. “Free to self-host” describes software licensing, not zero total cost of ownership. +No self hosted platform removes the need to manage compute, storage, networking, secrets, logs, backups, upgrades, model credentials, and incident response. Free to self host describes software licensing, not zero total cost of ownership. ## 1. Which teams is Sim a fit for as a self-hosted AI workflow automation platform? -Sim is a strong fit for teams that want AI-native visual workflows under the permissive Apache 2.0 license. +Sim is a strong fit for teams that want visual AI workflows and an Apache 2.0 core. -Sim’s main differentiator is not merely that its source is visible: [Apache 2.0 is an OSI-approved open-source license](https://opensource.org/licenses) that permits use, modification, and redistribution subject to its terms. That distinction matters to companies evaluating long-term deployment freedom, internal customization, procurement risk, or the ability to build around the software without relying on a fair-code license. +Sim's main differentiator is not merely that its core source is visible: [Apache 2.0 is an OSI approved open source license](https://opensource.org/licenses) that permits use, modification, and redistribution subject to its terms. The separate [Sim Enterprise License](https://github.com/simstudioai/sim/blob/main/apps/sim/ee/LICENSE) covers `apps/sim/ee`, including SSO, SCIM, access control, access requests, audit logs, white labeling, data retention, data drains, workspace forking, session policies, custom blocks, and credential groups; development, testing, and internal nonproduction use are free, while production use requires an active Sim Enterprise subscription. That distinction matters to companies evaluating long term deployment freedom, internal customization, procurement risk, or the ability to build around the software without relying on a fair code license. [Sim is designed to build, deploy, and monitor AI agents and workflows](https://github.com/simstudioai/sim). Teams can visually compose workflow steps, connect supported model providers and tools, and self-host the platform in infrastructure they control. -A configured self-hosted Sim deployment can connect to a local Ollama endpoint, but local or private model access should not be assumed to make every deployment offline or air-gapped. Teams with those requirements should confirm architecture, support, security, and networking details before procurement. +[A self hosted Sim deployment](https://docs.sim.ai/platform/self-hosting) can connect to Ollama, vLLM, LM Studio through a compatible endpoint, or LiteLLM without requiring Sim Enterprise, but local or private model access does not make every deployment offline or air gapped. Teams with those requirements should confirm architecture, support, security, and networking details before procurement. **Why Sim fits these priorities** -- Apache 2.0 provides clear, OSI-approved open-source rights. -- The workflow experience is designed for AI automation and agentic processes. +- Apache 2.0 provides clear, OSI approved open source rights for Sim's core. +- The workflow experience is designed for AI agents and related processes. - Self-hosting gives teams control over application infrastructure and operational boundaries. - The platform offers a path from visual building to governed enterprise deployment. -- API-oriented extensibility supports integrations beyond prebuilt tools. +- API oriented extensibility supports integrations beyond prebuilt tools. **Where another platform may fit better** -n8n may fit better when a buyer’s dominant requirement is mature, general-purpose application automation rather than AI-native workflow development. Dify may fit better when the product being built is specifically an LLM application with prompt, retrieval, and model-operations requirements. For more context, compare the [leading open-source AI agent platforms](https://www.sim.ai/library/open-source-ai-agent-platforms). +n8n may fit better when a buyer’s dominant requirement is mature, general purpose application automation rather than AI focused workflow development. Dify may fit better when the product being built is specifically an LLM application with prompt, retrieval, and model operations requirements. For more context, compare the [leading open source AI agent platforms](https://www.sim.ai/library/open-source-ai-agent-platforms). -As of September 2026, Sim’s license is published in its [official GitHub repository](https://github.com/simstudioai/sim/blob/main/LICENSE). +As of November 2026, Sim's core license is published in its [official GitHub repository](https://github.com/simstudioai/sim/blob/main/LICENSE), with the enterprise exception published separately. ## 2. Is n8n the best self-hosted platform for integration-heavy automation? -n8n is the strongest self-hosted choice in this ranking for teams whose main requirement is integration-heavy, general-purpose workflow automation. +n8n fits teams whose main requirement is integration heavy, general purpose workflow automation. -[n8n documents self-hosting on private infrastructure and supports AI workflows that connect multiple model providers, tools, and memory](https://docs.n8n.io/deploy/host-n8n). Its workflow model is suited to moving information among applications, invoking APIs, transforming data, applying code, and adding AI steps to broader automations. +[n8n documents self hosting on private infrastructure and supports AI workflows that connect multiple model providers, tools, and memory](https://docs.n8n.io/deploy/host-n8n). Its workflow model is suited to moving information among applications, invoking APIs, transforming data, applying code, and adding AI steps to broader automations. -The central procurement issue is licensing. [n8n says its Sustainable Use License and Enterprise License follow the fair-code model](https://docs.n8n.io/privacy-and-security/sustainable-use-license). The Sustainable Use License is source-available but is not an OSI-approved open-source license. Its terms allow many internal and non-commercial uses while restricting certain commercial hosting or resale scenarios, so legal review is appropriate when n8n will be embedded in a product or provided to customers. +The central procurement issue is licensing. [n8n says its Sustainable Use License and Enterprise License follow the fair code model](https://docs.n8n.io/privacy-and-security/sustainable-use-license). The Sustainable Use License is source available but is not an OSI approved open source license. Its terms allow many internal and noncommercial uses while restricting certain commercial hosting or resale scenarios, so legal review is appropriate when n8n will be embedded in a product or provided to customers. -n8n is a strong candidate when connector coverage and general automation maturity outweigh the need for a permissive open-source license. It is a weaker fit when Apache 2.0 or equivalent licensing is a mandatory selection criterion. See the dedicated [n8n alternatives comparison](https://www.sim.ai/library/n8n-alternatives) for adjacent options. +n8n is a strong candidate when connector coverage and general automation maturity outweigh the need for a permissive open source license. It is a weaker fit when Apache 2.0 or equivalent licensing is a mandatory selection criterion. See the dedicated [n8n alternatives comparison](https://www.sim.ai/library/n8n-alternatives) for adjacent options. ## 3. Is Dify the best self-hosted platform for LLM applications? -Dify is the strongest option in this ranking for teams specifically building self-hosted LLM applications with prompts, retrieval, models, tools, and application-facing APIs. +Dify fits teams specifically building self hosted LLM applications with prompts, retrieval, models, tools, and application facing APIs. -[Dify describes itself as a workspace for agentic workflows, RAG pipelines, model and tool support, with cloud, VPC, and self-hosted deployment](https://github.com/langgenius/dify). That focus can make it a better conceptual fit than a general automation platform when the intended output is a chatbot, retrieval-augmented generation application, AI assistant, or model-backed internal product. +[Dify describes itself as a workspace for agentic workflows, RAG pipelines, model and tool support, with cloud, VPC, and self hosted deployment](https://github.com/langgenius/dify). That focus can make it a better conceptual fit than a general automation platform when the intended output is a chatbot, retrieval augmented generation application, AI assistant, or model backed internal product. -Dify should not be described simply as Apache 2.0 software. [Its license is a modified Apache 2.0 license with additional conditions](https://github.com/langgenius/dify/blob/main/LICENSE), making it a vendor-specific source-available license rather than a standard OSI-approved Apache 2.0 grant. +Dify should not be described simply as Apache 2.0 software. [Its license is a modified Apache 2.0 license with additional conditions](https://github.com/langgenius/dify/blob/main/LICENSE), making it a vendor specific source available license rather than a standard OSI approved Apache 2.0 grant. Dify’s broader application stack can also increase operational scope. Buyers should assess databases, queues, storage, plugins, model endpoints, monitoring, backups, and upgrade procedures rather than evaluating only the visual workflow editor. The [Dify alternatives guide](https://www.sim.ai/library/dify-alternatives) explores this category in more detail. @@ -165,42 +181,44 @@ Dify’s broader application stack can also increase operational scope. Buyers s Flowise reached end of life and is not a choice for new deployments. Its maintainers [announced a code freeze on July 29, 2026 and end of life on August 31, 2026](https://flowiseai.com/sunset), and the [GitHub repository](https://github.com/FlowiseAI/Flowise) was archived in August 2026. Existing installs keep running but receive no upstream fixes, so existing users would maintain a fork or migrate. -[Flowise described itself as a visual AI-agent builder and documented Docker-based self-hosting](https://github.com/FlowiseAI/Flowise/blob/main/README.md). Its visual graph exposed how prompts, models, retrievers, memory, and tools connect. [Sim's Flowise comparison](https://www.sim.ai/comparisons/flowise) and the [Flowise alternatives guide](/library/best-flowise-alternatives-2026) cover migration options. +[Flowise described itself as a visual AI agent builder and documented Docker based self hosting](https://github.com/FlowiseAI/Flowise/blob/main/README.md). Its visual graph exposed how prompts, models, retrievers, memory, and tools connect. [Sim's Flowise comparison](https://www.sim.ai/comparisons/flowise) and the [Flowise alternatives guide](/library/best-flowise-alternatives-2026) cover migration options. Flowise's [license applies Apache 2.0 to most code but commercial terms to enterprise code and explicitly marked files](https://github.com/FlowiseAI/Flowise/blob/main/LICENSE.md). ## 5. Is Langflow the best self-hosted platform for Python AI prototyping? -Langflow is a strong self-hosted choice for Python-oriented teams that want a visual way to prototype model, retrieval, tool, and agent components. +Langflow is a strong self hosted choice for Python oriented teams that want a visual way to prototype model, retrieval, tool, and agent components. -[Langflow is a Python project for building and deploying AI-powered agents and workflows](https://github.com/langflow-ai/langflow). Its component graph is useful when builders want visual composition without losing access to code-oriented customization. It is best evaluated as an AI development and prototyping environment rather than as a direct replacement for every business automation platform. +[Langflow is a Python project for building and deploying AI powered agents and workflows](https://github.com/langflow-ai/langflow). Its component graph is useful when builders want visual composition without losing access to code-oriented customization. It is best evaluated as an AI development and prototyping environment rather than as a direct replacement for every business automation platform. Production adoption requires more than exporting a successful prototype. Teams should plan for version control, testing, service deployment, identity, secrets, logs, traces, scaling, model reliability, and operational ownership. Langflow publishes the [MIT License in its official repository](https://github.com/langflow-ai/langflow/blob/main/LICENSE). ## 6. Is Activepieces the best self-hosted platform for approachable business automation? -Activepieces is a strong self-hosted option for teams that want accessible, general-purpose business automation with an open-source core. +Activepieces is a strong self hosted option for teams that want accessible, general purpose business automation with an open source core. -[Activepieces describes an extensible AI automation platform built around type-safe pieces](https://github.com/activepieces/activepieces/blob/main/README.md). Its recognizable trigger-and-action patterns make it easier to evaluate for routine SaaS and operations workflows. AI steps can participate in those automations, but the platform’s center of gravity is broader business automation rather than deeply AI-native workflow construction. +[Activepieces describes an extensible AI automation platform built around type safe pieces](https://github.com/activepieces/activepieces/blob/main/README.md). Its recognizable trigger and action patterns make it easier to evaluate for routine SaaS and operations workflows. AI steps can participate in those automations, but the platform’s center of gravity is broader business automation rather than deeply AI focused workflow construction. -Buyers should distinguish the MIT-licensed core from functionality distributed under separate enterprise terms. The exact identity, governance, security, and administration capabilities required for production should be mapped to the applicable edition before selection. Activepieces publishes this split in its [official license file](https://github.com/activepieces/activepieces/blob/main/LICENSE). +Buyers should distinguish the MIT licensed core from functionality distributed under separate enterprise terms. The exact identity, governance, security, and administration capabilities required for production should be mapped to the applicable edition before selection. Activepieces publishes this split in its [official license file](https://github.com/activepieces/activepieces/blob/main/LICENSE). ## Which license is best for a self-hosted AI workflow platform? -Sim and Langflow apply standard OSI-approved open-source terms across the published project, which matters for buyers who require them. Flowise applies Apache 2.0 to most code but has commercially licensed enterprise components. +Sim's core and Langflow use standard OSI approved open source licenses, which matters for buyers who require permissive terms. Sim's `apps/sim/ee` directory uses the separate Sim Enterprise License. Flowise applies Apache 2.0 to most code but has commercially licensed enterprise components. -- **Sim:** Apache License 2.0 +- **Sim:** Apache License 2.0 for the core, with `apps/sim/ee` under the Sim Enterprise License - **Langflow:** MIT License - **Flowise:** Apache 2.0 for most code, with commercially licensed enterprise components and marked files -- **n8n:** Sustainable Use License, which is source-available and not OSI-approved -- **Dify:** Modified Apache 2.0 license with vendor-specific conditions -- **Activepieces:** MIT-licensed core with commercially licensed enterprise functionality +- **n8n:** Sustainable Use License, which is source available and not OSI approved +- **Dify:** Modified Apache 2.0 license with vendor specific conditions +- **Activepieces:** MIT licensed core with commercially licensed enterprise functionality -A permissive license does not eliminate every legal obligation, and source availability does not make a license open source. Buyers should involve counsel when embedding a platform in a commercial product, modifying notices, redistributing software, or offering hosted access to third parties. +A permissive license is generally a better fit when an organization needs broad modification and redistribution rights, while fair code and other source available licenses can impose additional restrictions even when the repository is visible. The practical question is whether the license permits the intended internal use, modification, redistribution, embedding, managed service, or customer facing deployment. + +A permissive license does not eliminate every legal obligation, and source availability does not make a license open source. Buyers should involve counsel when embedding a platform in a commercial product, modifying notices, redistributing software, or offering hosted access to third parties. The [Apache 2.0 vs Fair Code comparison](https://www.sim.ai/library/apache-2-0-vs-fair-code) examines this distinction for Sim and n8n. ## How should buyers compare deployment control? -Sim and the other ranked platforms can be self-hosted, but deployment control must be evaluated across the entire production architecture rather than inferred from the presence of a Docker image or source repository. +Sim and the other compared platforms can be self hosted, but deployment control must be evaluated across the entire production architecture rather than inferred from the presence of a Docker image or source repository. A serious deployment review should cover: @@ -214,14 +232,14 @@ A serious deployment review should cover: - Horizontal scaling and queue behavior - High availability and disaster recovery - Telemetry and external service dependencies -- Identity provider and access-control integration +- Identity provider and access control integration - Support for restricted or disconnected networks -Self-hosting gives the operator more control, but it also transfers more responsibility to the operator. +Self hosting gives the operator more control, but it also transfers more responsibility to the operator. ## How should buyers compare model access? -Sim should be evaluated across its supported hosted providers, BYOK options, and configurable self-hosted endpoints. Local or private model access should be treated as an architecture requirement rather than assumed from self-hosting alone. +Sim should be evaluated across its supported hosted providers, BYOK options, and configurable self hosted endpoints. Self hosted Sim supports Ollama, vLLM, LM Studio through a compatible endpoint, and LiteLLM without requiring Sim Enterprise, but local or private model access remains an architecture requirement rather than a guarantee of isolation. For every platform, buyers should determine: @@ -232,21 +250,21 @@ For every platform, buyers should determine: - How retries, fallbacks, timeouts, and rate limits work - Whether usage and cost can be attributed to a workflow or team - Whether model inputs and outputs can be redacted or excluded from logs -- Whether local, private, or air-gapped inference is contractually and technically supported +- Whether local, private, or air gapped inference is contractually and technically supported -A platform being self-hosted does not mean the models it calls are self-hosted. +A platform being self hosted does not mean the models it calls are self hosted. ## How should buyers compare observability? -Sim and every competing platform should be tested with failed, retried, partial, long-running, and high-volume executions before its observability is judged production-ready. +Sim and every competing platform should be tested with failed, retried, partial, long running, and high volume executions before its observability is judged ready for production. -Useful workflow observability includes searchable run histories, step-level inputs and outputs, latency, error details, retry state, model usage, correlation identifiers, version context, and retention controls. Enterprise teams may also need export to centralized logging, metrics, tracing, security monitoring, or cost-management systems. +Useful workflow observability includes searchable run histories, step level inputs and outputs, latency, error details, retry state, model usage, correlation identifiers, version context, and retention controls. Enterprise teams may also need export to centralized logging, metrics, tracing, security monitoring, or cost management systems. -AI workflows add observability requirements that conventional automation may not expose. Teams may need to inspect model selection, token or provider usage, tool calls, retrieval context, prompt versions, structured-output failures, safety decisions, and human approval events. +AI workflows add observability requirements that conventional automation may not expose. Teams may need to inspect model selection, token or provider usage, tool calls, retrieval context, prompt versions, structured output failures, safety decisions, and human approval events. ## How should buyers compare governance and enterprise controls? -Sim and the other platforms should be compared against the buyer’s specific identity, authorization, audit, promotion, and data-governance requirements rather than a generic “enterprise-ready” label. +Sim and the other platforms should be compared against the buyer’s specific identity, authorization, audit, promotion, and data governance requirements rather than a generic “ready for enterprise use” label. The evaluation should include: @@ -261,39 +279,77 @@ The evaluation should include: - Private networking requirements - Support, response times, and upgrade assistance -Some controls may be available only through commercial or enterprise arrangements even when a platform’s core is self-hostable. +Some controls may be available only through commercial or enterprise arrangements even when a platform's core is self hostable. As of November 2026, [n8n documents SSO for Business and Enterprise self hosted editions](https://docs.n8n.io/deploy/host-n8n/configure-n8n/security/configure-sso), [Dify lists SSO and advanced security controls for Enterprise](https://dify.ai/pricing/dify-enterprise), and Flowise has reached end of life despite its legacy documentation for enterprise SSO. + +## What should a security review cover before self hosting an AI workflow platform? + +A security review for Sim, n8n, Dify, Langflow, Activepieces, or an existing Flowise deployment should cover the complete execution path rather than treating self hosting as proof of security. + +1. **License scope:** Confirm which directories, editions, and enterprise modules use which licenses. +2. **Network egress:** List every hosted model, API, webhook, telemetry service, package registry, and update service the deployment may contact. +3. **Identity:** Require the appropriate SSO, multifactor authentication, session, and account lifecycle controls. +4. **Authorization:** Test workspace roles, credential access, workflow editing rights, deployment rights, and administrative boundaries. +5. **Secrets:** Store credentials in an approved secret system, limit who can reference them, and prevent secret values from entering logs or model prompts. +6. **Audit evidence:** Record authentication, configuration changes, credential changes, workflow deployment, execution, and administrative actions where the selected edition supports them. +7. **Model data handling:** Document whether prompts, outputs, attachments, and embeddings go to local models or external providers. +8. **Data stores:** Identify the databases, vector stores, object stores, queues, and backups that retain workflow data. +9. **Isolation:** Define whether workflows share workers, credentials, storage, or network access across teams or tenants. +10. **Supply chain:** Pin images and dependencies, scan them, produce an inventory, and define an update process. +11. **Recovery:** Test backup restoration, key rotation, worker failure, and rollback procedures. +12. **Retention:** Set retention periods for run history, logs, files, prompts, outputs, and audit records. + +The [enterprise compliance and deployment guide](https://www.sim.ai/library/enterprise-ai-agent-compliance-deployment-guide) expands this review for regulated and private infrastructure. + +## How does self hosting affect data residency? + +Self hosting Sim or another workflow platform can place its application and primary databases in a chosen region, but data residency still depends on every model, integration, log sink, backup, and support path used by the workflow. + +A deployment in an approved region does not keep data there if a workflow sends prompts to a model endpoint in another region. The same issue applies to external vector databases, observability tools, email services, web search APIs, and software as a service integrations. + +Security teams should build a data flow for each production workflow. The data flow should name the runtime region, model endpoint, database, object store, vector store, log destination, backup region, subprocessors, and retention period. + +## Can these platforms run in an air gapped environment? + +Sim, n8n, Dify, Langflow, Activepieces, and existing Flowise installations can support isolated use cases only when the organization supplies every required model, service, image, package, and integration inside the controlled environment. + +Sim fits AI use cases that require local endpoints because [self hosted Sim supports Ollama and compatible endpoints for vLLM, LM Studio, and LiteLLM](https://docs.sim.ai/platform/self-hosting). A fully isolated Sim deployment still requires an internal plan for container images, software updates, databases, model files, secrets, observability, and any tools the agent calls. + +Air gapped operation becomes impossible when a workflow depends on a public model API or external software as a service application. Teams should test the exact workflow, not merely the platform login screen, without internet egress. ## What are the operating tradeoffs of self-hosting AI workflows? -Sim and every other self-hosted platform trade greater infrastructure control for greater operational responsibility. +Sim and every other self hosted platform trade greater infrastructure control for greater operational responsibility. -Self-hosting is attractive when teams need to control deployment location, network paths, upgrade timing, data stores, or software customization. It can also reduce dependence on a vendor’s hosted runtime. +Self hosting is attractive when teams need to control deployment location, network paths, upgrade timing, data stores, or software customization. It can also reduce dependence on a vendor’s hosted runtime. -The tradeoff is that the buyer becomes responsible for platform reliability. Required work can include patching, vulnerability management, backups, database maintenance, scaling, secrets, monitoring, incident response, model-provider management, and support for workflow authors. +The tradeoff is that the buyer becomes responsible for platform reliability. Required work can include patching, vulnerability management, backups, database maintenance, scaling, secrets, monitoring, incident response, model provider management, and support for workflow authors. -The best selection therefore depends on both product capabilities and the team that will operate them. Sim’s [workflow automation buyer’s checklist](https://www.sim.ai/library/ai-workflow-automation-platform-buyers-checklist) provides a structured way to test those requirements. +The selection therefore depends on both product capabilities and the team that will operate them. Sim’s [workflow automation buyer’s checklist](https://www.sim.ai/library/ai-workflow-automation-platform-buyers-checklist) provides a structured way to test those requirements. ## Which self-hosted AI workflow automation platform should you choose? -Sim is a strong fit for AI-native workflow teams that prioritize Apache 2.0 licensing and deployment control, while each alternative has a defensible specialist use case. +Sim is a strong fit for teams that prioritize an Apache 2.0 core, visual AI workflows, local model flexibility, and deployment control, while each alternative has a defensible specialist use case. -Choose **Sim** when permissive open-source licensing, visual AI workflows, and an enterprise deployment path are the main priorities. +Choose **Sim** when an open source core, visual AI workflows, and an enterprise deployment path are the main priorities. -Choose **n8n** when broad, integration-heavy business automation matters more than receiving OSI-approved open-source rights. +Choose **n8n** when broad, integration heavy business automation matters more than receiving OSI approved open source rights. Choose **Dify** when the main deliverable is an LLM application with retrieval, prompt, model, and application operations. Do not choose **Flowise** for a new deployment: it reached end of life on August 31, 2026. Teams already running it would maintain a fork or migrate to an actively maintained visual builder. -Choose **Langflow** when Python-oriented AI developers need a visual prototyping and component-composition environment. +Choose **Langflow** when Python oriented AI developers need a visual prototyping and component composition environment. -Choose **Activepieces** when approachable trigger-and-action business automation is more important than an AI-native platform architecture. +Choose **Activepieces** when approachable trigger and action business automation is more important than an AI focused platform architecture. Before committing, run the same representative workflows on the finalists and test deployment, failure recovery, credential handling, logs, upgrades, governance, and total operating effort. ## Related comparisons -Sim’s library separates self-hosted workflow platform selection from the broader AI agent builder head term to avoid giving buyers two competing answers to the same question. +Sim's library separates self hosted workflow platform selection from the broader AI agent builder category so buyers can evaluate each scope directly. -- For the broader market ranking, read [Best AI Agent Platforms and Builders in 2026](https://www.sim.ai/library/best-ai-agent-platforms-2026). -- For licensing and deployment alternatives, compare [open-source AI agent platforms](https://www.sim.ai/library/open-source-ai-agent-platforms). +- For the broader market, read [Best AI Agent Platforms and Builders in 2026](https://www.sim.ai/library/best-ai-agent-platforms-2026). +- For licensing and deployment alternatives, compare [Best Open Source AI Agents and Platforms in 2026](https://www.sim.ai/library/open-source-ai-agent-platforms). +- For the license distinction, read [Apache 2.0 vs Fair Code](https://www.sim.ai/library/apache-2-0-vs-fair-code). +- For governance and regulated environments, use the [Enterprise AI Agent Compliance and Deployment Guide](https://www.sim.ai/library/enterprise-ai-agent-compliance-deployment-guide). +- For procurement criteria, use the [workflow automation platform buyer checklist](https://www.sim.ai/library/ai-workflow-automation-platform-buyers-checklist). diff --git a/apps/sim/public/library/best-self-hosted-ai-workflow-automation-platforms-2026/cover.jpg b/apps/sim/public/library/best-self-hosted-ai-workflow-automation-platforms-2026/cover.jpg index b4c1791e2f3..06b77ac00b3 100644 Binary files a/apps/sim/public/library/best-self-hosted-ai-workflow-automation-platforms-2026/cover.jpg and b/apps/sim/public/library/best-self-hosted-ai-workflow-automation-platforms-2026/cover.jpg differ