AnythingLLM vs Flowise vs Langflow

AnythingLLM

Freemium · From Free (desktop/self-hosted); Cloud from $50/month

Best for: Individuals and small teams who want an out-of-the-box, privacy-first app for chatting with their own documents and running simple AI agents without assembling a custom pipeline.

Flowise

Freemium (free open-source self-hosting plus a paid Flowise Cloud with free, Starter, Pro, and Enterprise tiers) · From Free (self-hosted or Cloud Free plan); paid Cloud plans start at $35/month

Best for: Developers and technical teams who want a drag-and-drop, Apache 2.0-licensed canvas for building custom RAG chatbots and multi-agent workflows, backed by over 100 LLM and vector store integrations and Workday's enterprise resources.

Langflow

Open Source · From Free (self-hosted, MIT license); Langflow Cloud from about $25/month

Best for: Teams that want the largest open-source community around a LangChain-style visual builder, an MIT-licensed core, and the ability to export any visual flow to raw Python code for production hardening.

AnythingLLM

Flowise

Langflow

AnythingLLM

Pros

• Strong privacy and local-first design with no forced data sharing
• Free desktop app requiring no account
• Flexible support for multiple LLM providers, including local models
• Open source under the MIT license

Cons

• Cloud plans are priced per user-tier rather than pure usage-based pricing
• Enterprise pricing isn't publicly listed and requires contacting sales
• Self-hosting via Docker requires some technical setup

Flowise

Pros

• Free and open source under the permissive Apache 2.0 license, with no-cost self-hosting available on any infrastructure.
• Visual, drag-and-drop interface lowers the barrier to building RAG chatbots and multi-agent systems compared to writing LangChain code by hand.
• Broad integration coverage across more than 100 LLMs, embedding models, and vector databases.
• Supports both simple chatbots and more complex, coordinated multi-agent workflows with human-in-the-loop review.

Cons

• Flowise Cloud subscription prices do not include LLM token usage, so real production costs can run well above the listed plan price once model API bills are added.
• Self-hosting requires comfort with Docker and cloud infrastructure, which adds setup and maintenance overhead for non-technical teams.
• Enterprise pricing is not published and must be negotiated directly with sales, making budgeting harder for larger deployments.
• As a very small, now Workday-owned team historically, long-term open-source roadmap priorities may increasingly reflect Workday's enterprise product needs rather than the broader community's.

Langflow

Pros

• Fully open source and free under the MIT license, with no forced paid tier for core functionality
• Visual canvas makes RAG and agent prototyping accessible to non-expert developers
• Ability to export to raw Python code bridges the gap between prototyping and production
• Large and fast-growing GitHub community with over 150,000 stars
• Backed by IBM's resources following the DataStax acquisition, aiding long-term maintenance

Cons

• Real-world costs still come from LLM API usage, vector databases, and infrastructure, which are not included in the free software
• Self-hosted deployments require managing servers, scaling, and monitoring without a managed cloud plan
• Visual flows can become difficult to manage for very complex, large-scale agent architectures
• Ownership changes (Logspace to DataStax to IBM) may raise questions about long-term product direction for some adopters
• Langflow Cloud does not publish a detailed public pricing page beyond a reported starting price

AnythingLLM

• Private document Q&A — Chat with personal or company documents without sending data to a third-party cloud AI service.
• Building AI agents on proprietary data — Create AI agents that operate over your own documents and knowledge base.
• On-premise enterprise AI deployment — Deploy a self-hosted AI chat and agent platform within enterprise infrastructure.

Flowise

• Rapid RAG chatbot prototyping — Developers connect a document loader, vector store, and chat model on the Flowise canvas to stand up a retrieval-augmented chatbot over internal knowledge bases in minutes rather than writing custom LangChain code.
• Internal enterprise AI assistants — Product and IT teams use Flowise's Chatflows and Agentflows to build customer-support bots, internal knowledge assistants, and multi-step business process agents that plug into existing enterprise data sources.
• Governed, large-scale agent deployment — Large organizations use Flowise's Enterprise tier, with SSO/SAML, RBAC, audit logging, and on-premises deployment, to design, launch, and govern AI agents at scale across regulated business units.

Langflow

• Rapid RAG chatbot prototyping — Developers visually assemble retrieval-augmented generation pipelines to quickly prototype chatbots grounded in custom data.
• Multi-agent workflow design — Teams build coordinated multi-agent systems where specialized agents handle different parts of a complex task.
• Low-code to production handoff — Organizations prototype AI workflows visually, then export the generated Python code for hardening into a production application.