FlowiseAI vs New API
Side-by-side comparison · Updated October 2026
| Description | FlowiseAI is a tool for buyers evaluating whether it fits a specific AI workflow. FlowiseAI stands out as an open-source low-code tool that simplifies the process of building customized Large Language Model (LLM) orchestration flows and AI agents. With over 21K stars on GitHub, FlowiseAI is a trusted choice for developers worldwide, offering quick iterations from testing to production. It enables developers to create powerful LLM applications with a low-code approach, significantly enhancing their development velocity. Whether you're looking to build sophisticated AI agents or intricate LLM flows, FlowiseAI provides the flexibility and efficiency needed to bring your ideas to life. One of FlowiseAI's key strengths lies in its developer-friendly tools. It offers a myriad of APIs, SDKs, and embedded options that allow seamless integration into existing applications. Developers can extend FlowiseAI's capabilities with these tools and create autonomous agents that can execute various tasks. Additionally, FlowiseAI supports multiple open-source LLMs and functions effortlessly in air-gapped environments. This means you can run local LLMs, embeddings, and vector databases without depending on external cloud services, making it a versatile tool for a wide range of applications. FlowiseAI also offers support for self-hosting on major cloud platforms like AWS, Azure, and GCP, further enhancing its deployment flexibility. The platform is particularly useful for a variety of use cases, such as creating product catalog chatbots, generating detailed product descriptions, executing SQL database queries, and providing automated customer support. Community engagement is another strong suit of FlowiseAI, with a vibrant open-source community sharing experiences and innovations. This community-driven approach not only accelerates development but also provides developers with invaluable insights and support, fostering a collaborative environment that continually pushes the boundaries of what is possible with LLM technology. The capabilities to test first are Open-source low-code tool, Support for self-hosting on AWS, Azure, and GCP, Over 100 integrations including Langchain and LlamaIndex, Chatflow and LLM Orchestration, APIs, SDKs, and Embedded Chat functionalities. Those details matter because they determine whether FlowiseAI can reduce manual work, replace tool switching, or produce reliable output without constant cleanup. Best-fit users include e-commerce businesses, content creators, database administrators, customer support teams. A useful pilot should include a normal task, an edge case, and a recovery test so the team can see what happens when the first attempt is incomplete. Pricing is listed as Free, with plan information currently shown as Free. Confirm current limits, credits, seats, cancellation rules, and commercial terms on the official website before relying on this listing for budget decisions. Before adopting FlowiseAI, compare it with adjacent tools in the same category. Measure setup time, output quality, data handling, collaboration controls, exports, and whether non-technical users can repeat the workflow without heavy prompting. The strongest buying signal is not feature count; it is whether FlowiseAI consistently completes the exact job the buyer needs with fewer manual handoffs. If sensitive customer, financial, or internal data is involved, review privacy and retention policies before production use. | New API is a self-hosted AI gateway and model-management project maintained in the QuantumNous repository. It brings provider channels, access tokens, model restrictions, usage statistics and cost accounting into one interface. Teams supply their own authorized model-provider access and operate the gateway in their chosen environment. The gateway supports several API formats, including OpenAI-compatible requests, Claude Messages and Google Gemini. Compatibility has boundaries: the README marks Gemini-to-OpenAI conversion as text-only without function calling, and OpenAI-compatible to Responses conversion as in development. Test the exact endpoints, streaming behavior and tool calls your application uses before switching traffic. Routing features include weighted channel selection, automatic retry after failures and user-level model rate limits. The dashboard supports request-based, usage-based and cache-hit cost accounting, with token grouping and model-access controls. These controls can help organize usage, but retries and a common API format do not guarantee uninterrupted service or identical behavior across providers. The current repository license is AGPLv3. Docker Compose is the recommended quick-start path in the README, which also documents Docker commands with SQLite or MySQL. Running the software still involves infrastructure, operations and upstream model charges. Review the current license, secure your deployment and validate accounting against provider bills. Compare LiteLLM if you also want a Python SDK or a documented gateway for MCP servers and agents. |
| Category | AI Assistant | Developer Tools |
| Rating | No reviews | No reviews |
| Pricing | Free | Open Source |
| Starting Price | Free | N/A |
| Plans |
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| Tags | low-codedeveloperscustomized LLM orchestration flowsAI agentsAPIs | AI gatewayLLM routingmodel managementrate limitingusage accounting |
| Features | ||
| Open-source low-code tool | ||
| Support for self-hosting on AWS, Azure, and GCP | ||
| Over 100 integrations including Langchain and LlamaIndex | ||
| Chatflow and LLM Orchestration | ||
| APIs, SDKs, and Embedded Chat functionalities | ||
| Support for air-gapped environments with local LLMs | ||
| Developer-friendly with easy extensions | ||
| Strong open-source community | ||
| Autonomous agent creation | ||
| Rapid development and deployment capabilities | ||
| Self-hosted AI gateway and model management | ||
| Provider channels, token groups and model restrictions | ||
| OpenAI-compatible, Claude Messages and Gemini format support | ||
| Documented limits on protocol conversion | ||
| Weighted channel selection and failure retries | ||
| User-level model rate limiting | ||
| Request, usage and cache-hit cost accounting | ||
| Docker Compose deployment documentation | ||
| View FlowiseAI | View New API | |
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