I’m working on setting up a RAG system with agents for my small business and I’ve been exploring LangGraph CLI and Platform options. I understand the basics of creating a LangGraph server using langgraph dev or langgraph build commands and testing it with LangGraph Studio plus LangSmith.
What I can’t figure out is the licensing model. When I create a Docker container using langgraph-cli, can I deploy it on my own servers without restrictions? Is this open source or does it require the expensive Enterprise license that costs $25k?
I’m also wondering if I should skip the server approach entirely and just integrate the library with FastAPI instead. What are the main advantages of using LangGraph server compared to a custom FastAPI implementation, other than being able to use their hosted infrastructure and studio interface?
Any guidance on the licensing terms and deployment options would be really helpful.
I’ve deployed both in production, so here’s what I’ve learned. Yeah, Docker containers from langgraph-cli are free to self-host - no enterprise license needed for basic deployment. But here’s what everyone misses: you’re stuck handling auth and security yourself. When you self-host LangGraph server, you need to build proper API auth, rate limiting, and security headers. FastAPI gives you more control since you’re building these from scratch. With LangGraph server, you’ll probably end up reverse-proxying through nginx anyway to get these features. It really depends on your team’s backend skills. If you’re strong there, FastAPI lets you optimize exactly what you need without bloat. But if you just want to focus on AI/RAG logic instead of infrastructure, LangGraph server’s built-in conversation management and agent orchestration will save you tons of dev time. One approach that works well: start with LangGraph server for prototyping and agent development, then migrate the critical stuff to custom FastAPI endpoints once you know your performance and scaling needs.
yup, langgraph is open source! i run my own containers too, it’s free. been on aws and it works well. the enterprise license is really for extra features and managed stuff, not needed for basic setups.
Yeah, it’s open source, but here’s what everyone misses about deployment.
Sure, you can self-host LangGraph containers for free. But the real pain isn’t licensing - it’s all the operational stuff. Docker management, scaling, monitoring… it adds up quick.
Been there with RAG systems at work. Started thinking “we’ll just deploy it ourselves.” Three months later we’re doing more DevOps than AI work.
FastAPI seems easier but you lose tons of stuff. LangGraph server handles state management, conversation persistence, agent orchestration - all built in. Go custom and you’re rebuilding everything from scratch.
Game changer for me was finding platforms that handle deployment automatically. You get LangGraph benefits without the infrastructure headaches.
For smaller budgets, this makes way more sense. Keep control of your RAG system, let someone else deal with the boring server stuff.
Latenode’s great for this - bridges the gap between DIY complexity and expensive enterprise licenses.
I’ve used LangGraph for six months - here’s what I’ve learned. The core library is MIT licensed, so you can self-host freely. Just know there’s a difference between the library and platform features. FastAPI vs LangGraph server? I tried FastAPI first thinking it’d be easier. Wrong move. You’ll rebuild state persistence, conversation threading, and agent checkpointing from scratch. LangGraph server handles all that, plus streaming responses and proper error handling for multi-agent setups. The real issue isn’t licensing cost - it’s operational overhead. Self-hosting means managing Redis/PostgreSQL for state, setting up logging, handling concurrent conversations, and fixing memory leaks from long-running agents. These problems don’t show up in dev but hit hard in production. For a small business RAG system? Start with LangGraph server locally, then pick hosting based on your traffic. The studio interface alone saves hours debugging agent flows.
MIT license covers the basics, but production is a different beast. I’ve been running LangGraph containers in prod for over a year.
What surprised me: resource management. Agent workflows eat memory, especially with complex RAG pipelines. Set up monitoring and auto-scaling from the start.
FastAPI vs LangGraph server isn’t just features - it’s debugging complexity. When agents go sideways (they will), LangGraph’s built-in observability saves you. FastAPI means rolling your own telemetry.
Something nobody talks about: backup and recovery. LangGraph server’s state persistence works great until you need to migrate or restore conversations. Plan ahead.
If you’re self-hosting, this video covers security basics you’ll need:
For small business, start with LangGraph server locally, then move to managed hosting after proving concept. The operational headache isn’t worth it unless you have compliance requirements.