This blog post is a brief summary of our conference talk "DevOps and Scalability Guide for LangChain Applications". Reach out to us for a deck or if you'd like any further details on the guide. This guide covers key aspects of deploying and maintaining production LangChain applications based on experience of implementing such systems in Big Tech and finance.
You have three main approaches to serve a LangChain app:
Direct serving works well for basic use cases - just wrap your chain in a FastAPI endpoint and you're done. LangServe provides a more maintainable solution with less boilerplate code. For internal tools with unpredictable usage patterns, serverless deployment (like AWS Lambda) can work.
For production LangChain applications, you have three main deployment patterns:
Managed container services (AWS ECS/Google Cloud Run/Azure Container Apps) work well for most cases. Package your app (whether it uses FastAPI, Uvicorn, or LangServe) into a container, push it to your cloud provider's registry, and deploy to their container service. For simpler deployment workflows, services like Elastic Beanstalk can help manage the container infrastructure.
Serverless deployment via AWS Lambda or similar services works well for internal tools with erratic usage. You'll need provisioned concurrency to avoid cold starts affecting response times.
For high-scale applications, Kubernetes (EKS/GKE/AKS) provides more control and scalability but requires more operational expertise.
A LangChain app typically isn't just a standalone container. You need vector stores, caching, auth, and other supporting services. Manual infrastructure setup becomes unmanageable quickly.
Use IaC tools to define and version your entire infrastructure. Terraform is the standard choice, while Pulumi offers a code-first alternative. Cloud-specific options like AWS CloudFormation work but lock you into that provider.
Your CI/CD pipeline should handle both application deployment and infrastructure provisioning. It needs to manage dev/staging/prod environments consistently. GitLab CI with AWS build runners or GitHub Actions are solid choices. The pipeline should validate infrastructure changes before applying them.
The core application usually needs:
Keep all these components within a single cloud provider's ecosystem when possible - it simplifies security, networking, and cost management.
Document processing for RAG can become a bottleneck. Offload intensive operations like ingestion to separate serverless functions or K8s microservices. Use message queues (RabbitMQ/SQS) to manage load and ensure reliable processing. For file processing pipelines, set up interim storage (MinIO/S3) to handle documents awaiting processing.
Never use .env files in production. Use your cloud provider's secrets management service:
Configure your container settings to properly propagate these credentials.
Implement native cloud monitoring integration from the start. Use CloudWatch/Cloud Monitoring/Azure Monitor depending on your platform. For Kubernetes deployments, consider additional tools like Sentry for error tracking.
Track token usage carefully. While simple chatbots are cheap to run, tasks like web scraping or email generation can quickly consume significant resources. Set up cost alerts and usage monitoring early.
Treat prompts like any other critical code component. They need version control, testing, and deployment pipelines. Here's a production-grade approach:
Store prompts in a dedicated repository or package. Structure them as template classes with proper typing and documentation.
Example:
class ClassificationPrompt(BasePrompt):
categories: List[str]
category_descriptions: Dict[str, str] = {}
def format_with_categories(self, text: str) -> str:
categories_text = "\n".join(
f"- {cat}: {self.category_descriptions.get(cat, '')}"
for cat in self.categories
)
return self.format(
categories=categories_text,
input_text=text
)Pull these prompt packages into your application during CI/CD builds. This allows prompt engineers to iterate independently of application code while maintaining version control and rollback capabilities.
Even if you don't need a separate repository yet, at minimum store prompts in configuration files - never hardcode them in application logic. This separates prompt engineering from application development and makes updates safer.
For high-traffic applications, skip single-container services and start with Kubernetes/docker-compose. Include automated testing in your CI/CD pipeline - consider tools like SPLX for LLM-specific testing.
Set up proper load balancing and keep all components in private subnets within your VPC. This network architecture is often required for security compliance and enterprise sales.