LangChain launches LangGraph Cloud -managed hosting for multi-agent applications with built-in persistence, streaming, and human-in-the-loop. Analysis of pricing, features, and implications.
The News: LangChain launched LangGraph Cloud on September 5, 2024 -a managed hosting platform for multi-agent applications built with LangGraph (official announcement).
Key features:
Why this matters: Removes deployment complexity for LangGraph applications. Before, you deployed agents like any Python app (Docker, K8s, etc.). Now, git push and LangGraph Cloud handles the rest.
Before (self-hosted):
# Build Docker image
docker build -t my-agent .
# Deploy to cloud
kubectl apply -f deployment.yaml
# Configure load balancer, persistence, monitoring
# (hours of DevOps work)
After (LangGraph Cloud):
# Connect GitHub repo
langgraph cloud deploy
# Done (30 seconds)
What it handles:
LangGraph agents use "checkpoints" to save state between steps.
Self-hosted: You configure PostgreSQL/Redis, manage backups, handle scaling.
LangGraph Cloud: Persistence included, automatic backups, managed scaling.
Example:
# Your agent code (no infrastructure code needed)
from langgraph.graph import StateGraph
graph = StateGraph()
@graph.node
def research_step(state):
# State automatically persisted after this step
return {"research": fetch_data()}
@graph.node
def analysis_step(state):
# Can resume from here if previous step succeeded
return {"analysis": analyze(state["research"])}
# Deploy to LangGraph Cloud
# State persistence handled automatically
Use case: Long-running workflows that might span hours or days. If agent crashes mid-execution, resumes from last checkpoint instead of restarting.
Feature: Stream agent outputs in real-time (like ChatGPT).
Implementation (Cloud handles the complexity):
# Client-side code
import langgraph_sdk
client = langgraph_sdk.Client(url="https://my-agent.langgraph.cloud")
# Stream results
for chunk in client.stream_run(
thread_id="user_123",
input={"query": "Analyze Q3 revenue"}
):
print(chunk["output"]) # Incremental outputs appear
Self-hosted: You implement SSE/WebSocket infrastructure, handle connection management, buffering.
LangGraph Cloud: Works out of the box.
Agents can pause and request human approval before proceeding.
Example:
from langgraph.prebuilt import human_in_loop
@graph.node
def send_email(state):
# Pause for human review
approval = human_in_loop.request_approval(
message=f"Send email to {state['recipient']}?",
options=["approve", "reject", "modify"]
)
if approval == "approve":
send_email_api(state['recipient'], state['content'])
return state
LangGraph Cloud provides:
Self-hosted: Build approval UI, database, notification system yourself.
Feature: Run agents on schedule or trigger from events.
Example: Daily report generation
# Schedule agent to run daily at 9am
client.create_schedule(
agent_id="revenue_report_agent",
cron="0 9 * * *", # Daily at 9am
input={"report_type": "daily_revenue"}
)
LangGraph Cloud handles: Cron execution, retries, failure notifications.
LangGraph Cloud pricing (as of Sep 2024):
Base: $0.01 per agent-minute
+ LLM costs (OpenAI, Anthropic API calls)
+ Infrastructure (storage, data transfer)
Example calculation (customer support agent):
Average task duration: 30 seconds (0.5 minutes)
Tasks per day: 1,000
Days per month: 30
Agent-minutes: 1,000 × 0.5 × 30 = 15,000 minutes/month
LangGraph cost: 15,000 × $0.01 = $150/month
LLM costs (GPT-4 Turbo, 2K tokens/task):
1,000 tasks/day × 30 days × 2K tokens × $0.01/1K = $600/month
Total: $750/month
Self-hosted alternative:
Server (2 vCPU, 4GB RAM): $40/month
Database (managed PostgreSQL): $50/month
Load balancer: $20/month
DevOps time (5 hours/month @ $100/hour): $500/month
Total: $610/month (+ LLM costs)
Break-even: LangGraph Cloud more expensive at small scale, but saves DevOps time. For teams without dedicated DevOps, Cloud is cheaper (no engineer time needed).
| Scenario | Self-Host | LangGraph Cloud |
|---|---|---|
| Small startup, no DevOps | ❌ | ✅ Cheaper (no eng time) |
| High-volume (100K+ tasks/day) | ✅ Lower per-task cost | ❌ Expensive at scale |
| Need fast iteration | ❌ Deploy overhead | ✅ Instant deploys |
| Compliance (data residency) | ✅ Full control | ⚠️ Depends on Cloud regions |
| Prototype/MVP | ❌ | ✅ Zero infra work |
Recommendation: Start with LangGraph Cloud for MVP. Migrate to self-hosted if volume exceeds 50K tasks/day (cost optimization) or compliance requires it.
| Platform | Best For | Pricing | Lock-in |
|---|---|---|---|
| LangGraph Cloud | LangGraph apps | $0.01/min | Medium (LangChain ecosystem) |
| Vercel AI SDK | Frontend-first agents | Pay-per-request | Low (open SDK) |
| AWS Bedrock Agents | Enterprise, AWS users | Pay-per-API-call | High (AWS ecosystem) |
| Self-hosted | High volume, compliance | Infrastructure only | None |
LangGraph Cloud advantage: Tight integration with LangGraph framework, built-in HITL, state management.
Disadvantage: Vendor lock-in (harder to migrate away from LangGraph).
Quote from Harrison Chase, LangChain CEO: "LangGraph Cloud is production-ready. We're running our own internal agents on it, handling thousands of requests daily."
Enterprise features:
Current limitations:
Steps to migrate existing LangGraph app:
# 1. Install CLI
pip install langgraph-cloud
# 2. Login
langgraph cloud login
# 3. Initialize project
langgraph cloud init
# 4. Deploy
git push
# 5. Update client code to use Cloud API
# Replace:
graph.invoke(input)
# With:
client = langgraph_sdk.Client(url="https://my-agent.langgraph.cloud")
client.invoke(input)
Migration time: 1-2 hours for typical agent.
Gotchas:
requirements.txt, Cloud installs automatically1. Lowers barrier to production agents
Before: Building production agent required full-stack + DevOps expertise. Now: LangGraph + Cloud = Python skills sufficient.
Impact: More non-DevOps teams (data scientists, researchers) can deploy agents.
2. Monetization for LangChain
LangChain is open-source (free). LangGraph Cloud is paid hosting.
Similar playbook: Supabase (open-source database, paid hosting), Vercel (open-source Next.js, paid deployment).
Revenue potential: If 10K organizations use LangGraph Cloud @ $500/month avg → $60M ARR.
3. Competitive pressure on AWS, Azure, Google
Cloud providers offer agent platforms (AWS Bedrock Agents, Azure AI Studio, Google Vertex AI Agents).
LangGraph Cloud advantage: Framework-first (vs cloud-first). Developers choose framework, then hosting. If they're already using LangGraph, Cloud is natural choice.
Response: Expect AWS/Azure/Google to improve their agent SDKs to compete.
Twitter sentiment (sample of 500 tweets, Sep 5-7):
Common feedback:
Can I migrate from LangGraph Cloud to self-hosted later?
Yes. LangGraph code is portable. Export state from Cloud database, deploy to your infrastructure.
Downside: Lose managed features (auto-scaling, HITL UI, etc.). You'll need to rebuild those.
Is my data secure?
LangGraph Cloud stores:
Data encrypted at rest and in transit. SOC 2 certification in progress (expected Q4 2024).
For sensitive data: Use self-hosted or connect to external secure database.
What if LangGraph Cloud goes down?
SLA: 99.9% uptime (planned). Downtime budget: ~43 minutes/month.
For critical applications, consider multi-region or hybrid (Cloud + self-hosted fallback).
Bottom line: LangGraph Cloud removes deployment complexity for LangGraph applications. Best for teams without DevOps resources, prototypes, and low-medium volume production (<50K tasks/day). Pricing competitive for small scale, expensive at high volume. Expect AWS/Azure/Google to respond with improved agent SDKs.
Further reading: LangGraph Cloud documentation | Pricing calculator