I’m working on deploying LangGraph and wondering about serverless limitations. The official docs mention that LangGraph shouldn’t be used in serverless setups, but I don’t understand the technical reasons behind this recommendation.
I was thinking about using AWS Lambda or similar serverless platforms for my LangGraph application. Even though I plan to use external PostgreSQL and Redis instances (not serverless ones), I’m still unsure if this would work properly.
What specific problems occur when you try to run LangGraph on serverless platforms? Is it related to cold starts, memory limitations, or something else? Would love to hear from anyone who has experience with this.
The docs warn against serverless for good reason - LangGraph needs stateful execution but serverless is stateless by design. I tried this on Vercel functions and it was a complete mess. Graph nodes lose context between calls, so workflows just break. Cold starts are brutal too since LangGraph has to rebuild the entire graph structure every time.
It’s all about LangGraph’s architecture - it’s built for persistent environments, not serverless. Found this out the hard way when I tried moving a complex agent workflow from a regular server to Google Cloud Functions. LangGraph keeps execution graphs running across multiple function calls. Each node triggers a separate serverless function, which means you get hit with cascading cold starts. The overhead goes exponential, not linear. Checkpointing is another killer. LangGraph auto-saves state constantly, expecting fast local storage. In serverless, that means expensive database writes for every checkpoint - even simple workflows get costly. The threading model breaks too. LangGraph uses background threads for streaming and parallel execution, but serverless platforms have strict threading limits and wonky execution contexts. I switched to Cloud Run instead. You get the serverless scaling without the execution time and state management nightmares. The persistent containers handle LangGraph’s needs way better and still scale to zero when idle.
Been down this road multiple times with different projects. The core problem is LangGraph’s graph execution doesn’t match serverless lifecycle at all.
LangGraph compiles your workflow into an execution graph that expects to run continuously. When a node waits for another node or handles conditional branching, it keeps everything alive in memory. Serverless functions hate this pattern.
I’ve watched teams try breaking graphs into tiny pieces - you get a distributed system nightmare. Each function call becomes a network hop with serialization overhead. Your simple 5-node graph turns into a mess of API calls between functions.
The interrupt and resume feature? Useless in serverless. LangGraph lets you pause execution for human input or external validation, then pick up exactly where you left off. Good luck doing that when your function dies after every request.
Another pain point - the visualization and debugging tools. LangGraph’s studio and graph inspection features expect persistent processes. Try tracing execution flow when your functions are scattered across different invocations.
Even with external PostgreSQL and Redis, you’re still fighting the execution model. The graph runner needs to stay alive to coordinate between nodes properly.
Stick with containers or traditional servers for LangGraph. The stateful nature just doesn’t work with serverless patterns.
Fought with this for months before ditching serverless completely for LangGraph. The core problem? LangGraph needs persistent connections and background processes, but serverless kills these off constantly. Even with external PostgreSQL and Redis, the function containers get recycled nonstop, creating expensive reconnection overhead. Your database bills will explode from connection pooling issues. LangGraph’s dependency graph is a nightmare in serverless too - each node can spawn new function instances, making execution paths unpredictable and debugging hell. The streaming features that make LangGraph great break when your execution environment vanishes mid-stream. I moved to containerized with auto-scaling and haven’t looked back. Consistent performance and predictable costs beat the hassle of serverless deployment every time.
Hit this exact problem when I tried cramming LangGraph into Lambda. Most people focus on cold starts, but that’s not the real issue.
The killer is state management. LangGraph tracks conversation state and graph execution state between steps. Normal servers keep this in memory or write it to disk fast.
Serverless functions start fresh every time. LangGraph constantly serializes and deserializes state, hammering your database way more than necessary. Massive latency spikes.
I tested a simple LangGraph workflow on Lambda with RDS PostgreSQL. Each graph step meant separate database trips just to figure out where it was. Milliseconds turned into seconds.
Memory limits make it brutal. LangGraph loads the entire graph structure plus intermediate results. Complex graphs blow past Lambda’s memory ceiling fast, especially with chunky LLM responses.
Timeout limits suck too. LangGraph workflows run long - chaining multiple LLM calls, waiting for external APIs. Lambda’s 15-minute max becomes a real problem.
For production LangGraph, I’ve had way better results with containerized deployments on ECS or plain EC2 instances. Persistent memory and longer processes make everything smoother.
