Flowise makes LLM application development accessible to people who think visually. Drag a document loader here, connect a vector store there, wire up a chat model — you can build a RAG pipeline or an agent without writing code. But the visual simplicity hides a cost problem: every node that talks to an LLM is a separate API call, and a typical Flowise pipeline makes 3-5 calls per user query. At GPT-4o prices, each query costs $0.02-0.10. Scale that to hundreds of queries a day and the costs get uncomfortable.
Antbase transforms Flowise from an expensive prototyping tool into a production-ready platform. When you configure the ChatOpenAI node to use Antbase, every LLM call in your visual pipeline gets independently routed. The retrieval synthesis step (simple) goes to a fast free model. The response refinement step (moderate) goes to a mid-tier model. The final answer generation (complex, if needed) goes to a premium model. Same pipeline, same output quality, dramatically lower per-query costs.
Flowise pipelines also benefit from Antbase failover in a unique way. If you are building pipelines for non-technical users or clients, reliability matters more than cost. A pipeline that fails intermittently because of provider outages makes the whole system look unreliable. Antbase provides the invisible resilience layer that makes your Flowise applications production-grade without adding complexity to the visual canvas.
Flowise is an open-source visual tool for building LLM applications. You drag and drop nodes to create chains, agents, and RAG pipelines. By configuring the ChatOpenAI node to point at Antbase, every LLM call in your visual pipeline gets intelligent model routing.
Run Flowise
npx flowise startConfigure the ChatOpenAI Node
In the Flowise canvas, drag a ChatOpenAI node and configure it:
- •Base Path: https://antbase.ai/v1
- •OpenAI API Key: ant_your-api-key (add via Credentials)
- •Model Name: auto
- •Temperature: adjust per use case (0.1 for extraction, 0.7 for creative)
Build a RAG Pipeline
Connect nodes visually: Document Loader (PDF, text, web) > Text Splitter > Vector Store (Chroma, Pinecone) > Retrieval QA Chain > ChatOpenAI (Antbase). Each query goes through the chain, and Antbase picks the best model for the synthesis step.
Tips
- •Flowise exposes every chain as an API endpoint automatically. Your visual pipeline becomes a REST API you can call from any app.
- •Use the Prediction API to test your chains programmatically before integrating.
- •For complex agent flows, Flowise makes multiple LLM calls. Antbase routes each independently for optimal cost.
- •Export your flow as JSON for version control and share it with your team.
