Deployment Guide

Deploy and operate TrustGraph across different environments

What’s in This Section?

This section provides platform-specific deployment instructions for running TrustGraph in various environments, from local development to production cloud deployments.

This Section is For:

  • DevOps engineers deploying TrustGraph infrastructure
  • System administrators managing TrustGraph instances
  • Developers setting up local development environments
  • Architects planning production deployments

Not What You Need?

Choosing Your Deployment

Not sure which deployment option fits your needs? See Choosing a Deployment for a decision guide with comparison tables and recommendations.

Quick Decision Guide

Your Situation Recommended Option
First time trying TrustGraph Docker Compose
Local development & testing Docker Compose or Minikube
Learning Kubernetes Minikube
Small production (<100 users) AWS EC2 Single Instance or Docker Compose
Production with scaling needs AWS RKE, Azure AKS, or GCP
GPU acceleration required Intel/Tiber Cloud
Budget-conscious cloud Scaleway

Deployment Options

Local Development

Perfect for testing, development, and evaluation.

Docker Compose

Easiest way to get started - Deploy TrustGraph locally with all services orchestrated.

  • ✅ Best for: First-time users, POCs, local development
  • ✅ Pros: Simple setup, all-in-one, easy to tear down
  • ⚠️ Limits: Single machine, not for production scale
  • Time to deploy: 15 minutes
  • Prerequisites: Docker/Podman, 8GB RAM, 4 CPU cores

Minikube

Local Kubernetes - Run TrustGraph on Kubernetes locally.

  • ✅ Best for: Learning K8s, testing K8s deployments
  • ✅ Pros: Real Kubernetes environment, good for learning
  • ⚠️ Limits: Single node, resource intensive
  • Time to deploy: 30 minutes
  • Prerequisites: Minikube, kubectl, 16GB RAM recommended

Cloud Platforms

Production-ready deployments with scalability.

AWS (Amazon Web Services)

Production AWS with RKE2 - Enterprise-ready deployment on AWS.

  • ✅ Best for: Production deployments, enterprise scale
  • ✅ Pros: High availability, auto-scaling, managed services
  • 💰 Cost: Medium to high (depends on resources)
  • Time to deploy: 2-3 hours
  • Also see: AWS EC2 Single Instance for simpler development setup

Azure AKS

Microsoft Azure Kubernetes - Deploy on Azure with AKS.

  • ✅ Best for: Azure-committed organizations
  • ✅ Pros: Azure integration, managed K8s, enterprise support
  • 💰 Cost: Medium to high
  • Time to deploy: 2-3 hours

Google Cloud Platform

GCP deployment - Run TrustGraph on Google Cloud.

  • ✅ Best for: GCP users, ML/AI workloads
  • ✅ Pros: VertexAI integration, GKE, good for AI projects
  • 💰 Cost: Medium (free credits available)
  • Time to deploy: 2-3 hours

Intel / Tiber Cloud

GPU-accelerated - High-performance with Intel GPU acceleration.

  • ✅ Best for: GPU workloads, high-performance needs
  • ✅ Pros: Hardware acceleration, optimized for Intel
  • 💰 Cost: Variable
  • Time to deploy: 2-4 hours

Scaleway

Budget-friendly European cloud - Cost-effective cloud deployment.

  • ✅ Best for: Budget-conscious deployments, EU data residency
  • ✅ Pros: Lower cost, European data centers
  • 💰 Cost: Lower than major clouds
  • Time to deploy: 2-3 hours

AWS EC2 Single Instance

Simple AWS setup - Single EC2 instance for development/testing.

  • ✅ Best for: Development, small-scale testing on AWS
  • ✅ Pros: Simple, cost-effective for development
  • ⚠️ Limits: Not for production scale
  • 💰 Cost: Low
  • Time to deploy: 1 hour

Production Considerations

Before Going to Production

Review these critical resources:

  1. Production Considerations - HA, monitoring, backups, disaster recovery
  2. Security Guide - Authentication, encryption, access control (Phase 4)
  3. Choosing a Deployment - Detailed comparison and requirements

Production Checklist

  • High availability configured
  • Monitoring and alerting set up
  • Backup strategy implemented
  • Security hardening completed
  • Resource sizing validated
  • Disaster recovery plan tested
  • Performance benchmarks established
  • Documentation for operations team

Troubleshooting

Common Issues

See Troubleshooting Guide for solutions to common deployment problems:

  • Container startup failures
  • Network connectivity issues
  • Resource constraints
  • Configuration errors
  • Service dependencies

Getting Help

Deployment Architecture

Components

TrustGraph deployments typically include:

  • Processing Services: Document processing, entity extraction, GraphRAG
  • Storage Layer: Graph database (Cassandra), vector store (Qdrant)
  • Message Queue: Apache Pulsar for service communication
  • LLM Integration: Connection to local or cloud LLMs
  • Web Interface: TrustGraph Workbench
  • Monitoring: Grafana dashboards (optional but recommended)

Network Requirements

  • Internal: Service-to-service communication
  • External: API access, web interface
  • LLM Access: Outbound to cloud LLMs or local model access
  • Storage: Persistent volumes for databases

Next Steps

Just Starting?

  1. Try Docker Compose locally
  2. Load sample data: Getting Started
  3. Explore features: How-to Guides

Planning Production?

  1. Read Choosing a Deployment
  2. Review Production Considerations
  3. Set up monitoring and security
  4. Select your cloud platform guide above

Need Help?

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