Langfuse v3 Terraform Module Sample

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Langfuse v3 Terraform Module Sample

This Terraform module provides infrastructure components for deploying Langfuse v3 self-hosted on Amazon Web Service(AWS).

Langfuse is an open-source observability and analytics platform designed for LLM applications.

[!NOTE] This module specifically focuses on Langfuse core components deployment. It does not include:

  • VPC and networking configurations
  • Infrastructure for LLM applications that will interact with Langfuse

You can either:

  • Use this as a module within your existing Terraform codebase
  • Create your own VPC and network infrastructure separately

Features

archtecture.jpg

Core Components

  • Web Server: Deployed on AWS App Runner for scalable web application hosting
  • Async Worker: Implemented using AWS ECS Fargate for efficient background processing

Data Storage

  • OLTP Database: Amazon Aurora Serverless v2 (PostgreSQL-compatible) for transactional data
  • Queue/Cache: Amazon ElastiCache for Redis (Valkey) for high-performance caching
  • Blob Storage: Amazon S3 for object storage for media files and event logs
  • OLAP Database: ClickHouse running on AWS ECS Fargate with Amazon EFS for data persistence

Analytics & Monitoring

  • Analytics Dashboard: Grafana deployed on AWS ECS Fargate for OLAP query execution and visualization

Infrastructure

  • AWS-native service integration for efficient and cost-effective deployment, I mean, no EC2 instances!
  • Scalable and production-ready setup
  • Secure configuration with AWS best practices

For more information on Langfuse's architecture, please check the official documentation.

Prerequisites

  • AWS Account
  • Terraform >= 1.0 (tested version: v1.8.2)
  • AWS CLI configured with pushing Docker images to Amazon ECR

Setup Instructions

  1. Create Terraform backend resources for state management
  2. Configure required variables in variables.tf
  3. Deploy using the provided examples or integrate as a module

Infrastructure Configuration

  • identity_name - Unique identifier for resources (e.g., "mycompany")
  • vpc_id - Your VPC ID where Langfuse will be deployed
  • private_subnet_ids - List of private subnet IDs for components deployment
  • custom_domain_name - Domain name for Langfuse and Grafana (e.g., tubone-project24.com)
  • custom_domain_id - Route53 Hosted Zone ID

Security Configuration

  • web_next_secret - Session cookie validation key (Generate: openssl rand -base64 32)
  • web_salt - API key hashing salt (Generate: openssl rand -base64 32)
  • encryption_key - 256-bit encryption key (Generate: openssl rand -hex 32)

Database Configuration

  • database_user - Aurora Serverless v2 database username (Default: "langfuse")
  • database_max_capacity - Maximum Aurora capacity units (Default: 10)
  • database_min_capacity - Minimum Aurora capacity units (Default: 0.5)

Optional Configuration

  • env - Environment name (Default: "dev")
  • region - AWS region (Default: "us-east-1")
  • availability_zones - List of AZs (Default: ["us-east-1a", "us-east-1b", "us-east-1c"])
  • cache_node_type - ElastiCache instance type (Default: "cache.t2.micro")
  • is_spot_instance - Use spot instances for workers (Default: false)
  • worker_desire_count - Number of worker instances (Default: 1)

Push Docker Images to ECR

After running terraform apply, you need to push the required Docker images to the created ECR repositories.

Langfuse web

App Runner requires a x_86-64 image, so you need to pull x_86_64 image and push the image to ECR.

docker pull langfuse/langfuse:3
docker tag langfuse/langfuse:3 ${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.amazonaws.com/langfuse
docker push ${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.amazonaws.com/langfuse

Langfuse worker

Langfuse worker, use ECS Fargate, you can choose ARM64 image.

So you need to pull ARM64 image and push the image to ECR. (Cost-effective)

docker pull --platform linux/arm64 langfuse/langfuse-worker:3
docker tag langfuse/langfuse-worker:3 ${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.amazonaws.com/langfuse-worker
docker push ${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.amazonaws.com/langfuse-worker

Clickhouse

Clickhouse, use ECS Fargate, you can choose ARM64 image.

docker pull clickhouse/clickhouse-server:24
docker tag clickhouse/clickhouse-server:24 ${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.amazonaws.com/clickhouse
docker push ${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.amazonaws.com/clickhouse

Optional: Use Clickhouse with S3 Disk

If you want to use Clickhouse with S3 Disk, you need to use a custom Clickhouse image with S3 support. (Cost-effective)

docker pull ghcr.io/tubone24/clickhouse-server-s3disk
docker tag ghcr.io/tubone24/clickhouse-server-s3disk ${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.amazonaws.com/clickhouse
docker push ${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.amazonaws.com/clickhouse

Grafana

TBD(Configurable)

Examples

Complete deployment examples are available in the examples directory.

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