
FlowiseAI
AI orchestration software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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$35 per month
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What is FlowiseAI
FlowiseAI is an open-source, visual builder for creating and orchestrating LLM-based workflows such as chatbots, retrieval-augmented generation (RAG) pipelines, and agent-like chains. It targets developers and technical teams that want a node-based UI to assemble prompts, tools, memory, and data connectors without writing all orchestration logic from scratch. The product commonly runs self-hosted and is used to prototype and deploy LLM applications that integrate with model providers and vector databases. Its differentiator is a low-code, drag-and-drop canvas that maps to common LLM orchestration patterns and can be extended through custom nodes.
Visual low-code workflow builder
FlowiseAI provides a node-based canvas that makes it easier to assemble LLM pipelines than writing orchestration code end-to-end. Teams can iterate on prompts, chaining logic, and RAG components with faster feedback loops. This is useful for prototyping internal assistants and proof-of-concept automations where requirements change frequently.
Open-source and self-hostable
FlowiseAI is available as open source and is commonly deployed in a customer-controlled environment. This can help organizations that need to keep prompts, documents, and logs within their own infrastructure. It also enables customization of nodes and integrations without waiting for a vendor roadmap.
Ecosystem-style integrations
The product supports common building blocks used in LLM applications, such as model provider connectors and vector database/RAG components. This reduces the amount of glue code needed to connect models to enterprise data sources and tools. It also helps standardize how teams compose and reuse workflow patterns across projects.
Governance features vary by deployment
Compared with more enterprise-oriented orchestration platforms, FlowiseAI’s governance capabilities (role-based access control, auditability, environment promotion, and policy controls) may require additional setup or third-party tooling. Organizations with strict SDLC and compliance requirements may need to build supporting processes around it. The maturity of these controls can depend on the specific version and hosting approach used.
Operationalization requires engineering effort
Running FlowiseAI reliably in production typically involves work around scaling, monitoring, secrets management, and CI/CD. Teams may need to add observability for latency, token usage, and failure modes across nodes and external services. This can increase total implementation effort versus fully managed offerings.
Workflow complexity can be hard to manage
As flows grow, visual graphs can become difficult to version, review, and test compared with code-first approaches. Reproducibility and automated testing of complex chains may require conventions and additional tooling. Teams with large numbers of workflows may need governance and modularization practices to avoid sprawl.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Free | $0/month | 2 Flows & Assistants; 100 Predictions/month; 5MB Storage; Evaluations & Metrics; Custom Embedded Chatbot Branding; Community Support. |
| Starter | $35/month | Everything in Free; Unlimited Flows & Assistants; 10,000 Predictions/month; 1GB Storage; Community Support. (Sign-up note on site: "Get Started - First Month Free"). |
| Pro | $65/month | Everything in Starter; 50,000 Predictions/month; 10GB Storage; Unlimited Workspaces; Includes 5 users (additional users $15/user/month); Admin roles & permissions; Priority Support. |
| Enterprise | Custom pricing | "Looking for specific use cases and support? Let's Chat" (contact sales). Platform is described as Enterprise Ready with support for cloud & on-prem deployment and scaling features. |
Seller details
FlowiseAI (open-source project)
Open Source
https://flowiseai.com/
https://x.com/FlowiseAI
https://www.linkedin.com/company/flowiseai/