Flowise vs GitHub - Kong/kong: 🦍 The API and AI Gateway

## Flowise vs Kong: Which Tool Fits Your Workflow?

Flowise and Kong serve different purposes in the AI and API ecosystem. Flowise focuses on visual prototyping of AI agents and workflows, while Kong is a gateway for managing API traffic, including AI model requests. Understanding their strengths helps you decide which to use.

### What is Flowise?

Flowise is a visual orchestration platform that enables builders to prototype agent systems, retrieval flows, and tool-using assistants without writing raw code. It uses a drag-and-drop interface to design agent flows and internal copilots quickly. Key features include visual orchestration, agent prototyping, multi-step workflow automation, and Model Context Protocol (MCP) experimentation.

### What is Kong?

Kong is an open-source API and AI gateway designed for cloud-native architectures, microservices, and Kubernetes environments. It acts as a reverse proxy and ingress controller, managing API traffic with over 200 plugins for authentication, rate limiting, logging, and AI-specific features like LLM gateway and MCP gateway. Kong is built on OpenResty and Lua for high performance.

### Key Differences

- **Primary Function**: Flowise is for building and prototyping AI agent workflows visually; Kong is for managing and securing API traffic, including AI model endpoints.

- **Target Users**: Flowise suits teams prototyping AI agents and retrieval flows; Kong suits DevOps and platform teams managing microservices and API gateways.

- **Deployment**: Flowise can start from open deployment paths; Kong is open-source with a commercial enterprise version (Kong Konnect).

- **AI Features**: Flowise focuses on agent orchestration and MCP experimentation; Kong provides AI gateway capabilities like LLM proxy and MCP gateway.

- **Extensibility**: Flowise uses visual drag-and-drop; Kong uses a plugin ecosystem with over 200 plugins.

### When to Use Flowise

- You need to quickly prototype AI agent behaviors and orchestration logic.

- You want to build multi-step workflows connecting multiple tools.

- You are experimenting with Model Context Protocol (MCP) for advanced agent interactions.

### When to Use Kong

- You need to manage API traffic for microservices or Kubernetes.

- You require an AI gateway to proxy requests to LLMs or manage MCP endpoints.

- You need robust API security, rate limiting, and monitoring.

### Conclusion

Flowise and Kong are complementary rather than directly competitive. Flowise is ideal for early-stage prototyping of AI agents and workflows, while Kong is essential for production-grade API management and AI model traffic control. Teams may use both: Flowise for prototyping and Kong for deployment.

### Pricing

Flowise can start from open deployment paths; specific pricing details are not provided. Kong is open-source under Apache 2.0 license; enterprise version (Kong Konnect) requires subscription.