Flowise is now a Workday company (acquired August 2025), but the open-source project remains the fastest way to drag-and-drop a LangChain workflow into existence. It is essentially a visual UI for LangChain.js, allowing you to chain LLMs, vector stores, and custom tools without writing boilerplate. The Cloud version starts at $35/month for 10,000 predictions, but the real power lies in the self-hosted version, which costs nothing but your infrastructure bill.
For a production workload processing 5,000 documents a day (approx. 150k ops/month), the Cloud plans are non-starters. The $65/month Pro plan caps out at 50,000 predictions, meaning you’d hit the limit in 10 days. You are effectively forced into the Enterprise tier or, more likely, self-hosting on a $20/month VPS. If you go the self-hosted route, the value is incredible: you get a production-grade orchestration layer that supports Docker, Kubernetes, and air-gapped deployments for zero licensing fees.
The interface is intuitive for anyone who understands the logic of RAG (Retrieval Augmented Generation). You can swap OpenAI for Anthropic or Pinecone for Weaviate by just dragging a wire. However, this visual abstraction is also its main weakness. Debugging a 50-node agent flow with loops and conditional logic turns into "spaghetti code" that is harder to read than a Python script. If you need to inspect the raw JSON passing between nodes, you'll find yourself fighting the UI.
Technically, Flowise is built on Node.js. This is a blessing for web developers but a curse for AI engineers who live in Python. Unlike LangFlow (its Python-based cousin), you cannot easily drop in a custom Python function or library; you have to wrap it in a JavaScript execution node or a separate API. This makes Flowise excellent for integrating AI into web apps but frustrating for heavy data science work.
Skip Flowise if you are a Python-heavy shop building complex, custom agents that require deep code-level debugging. Use it if you are a JavaScript/Node team or a product manager who wants to prototype a functional support bot in an afternoon without waiting for backend engineering resources.
Pricing
The Free Cloud tier is strictly for testing, offering just 100 predictions/month—enough for about 30 minutes of debugging. The paid tiers are usage-capped: $35/mo for 10k predictions and $65/mo for 50k. This pricing is aggressive for production; a modest chatbot will burn through the Starter plan in a week. The real value is the open-source self-hosted version (Docker), which removes all prediction limits and only costs you the compute (e.g., AWS EC2 or Railway).
Technical Verdict
Flowise is a stable wrapper around LangChain.js. The API is clean (REST), and the React frontend is responsive even with large graphs. However, latency is slightly higher than raw code due to the orchestration overhead. Documentation is decent but often lags behind the rapid LangChain updates. Integration is trivial for JS/TS apps but requires an HTTP bridge for Python stacks.
Quick Start
# pip install requests
import requests
API_URL = "http://localhost:3000/api/v1/prediction/<YOUR-CHATFLOW-ID>"
payload = {"question": "Summarize this context", "overrideConfig": {"sessionId": "123"}}
response = requests.post(API_URL, json=payload)
print(response.json())Watch Out
- It is Node.js only; you cannot run native Python code inside nodes without an external API call.
- Cloud plan overages are opaque; you simply hit a hard stop when you exceed prediction limits.
- Visual flows become unreadable 'spaghetti' once you exceed ~30 nodes.
- Debugging intermediate outputs between nodes requires digging through obscure logs.