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🏭 PRODUCTION SETUP FAQ

Context: Questions related to deploying the Traylinx Core infrastructure to a live production environment.

Q: What are the possibilities for running Traylinx Core in production, and what is the best setup?

A: The Production Landscape

To determine the best setup, we analyzed three primary architectural approaches:

1. Quick Start: Docker Compose on a Single VM

  • Description: Running the existing docker-compose.yml on a large EC2/Compute Engine instance.
  • Pros: Extremely simple (same as local dev), cheap, fast to deploy.
  • Cons: Single Point of Failure (SPOF). If the VM dies, the entire network goes down. No zero-downtime updates. Hard to scale specific components (e.g., just the Router).
  • Verdict: Good for Staging or POC, unsuitable for Production.

2. Serverless (Cloud Run / AWS Fargate)

  • Description: Deploying each service as a containerized serverless function.
  • Pros: Zero infrastructure management, pay-per-use (great for low traffic).
  • Cons: Connection Pooling issues with PostgreSQL (requires PgBouncer/RDS Proxy). "Cold starts" can affect the real-time latency required for high-frequency Agent-to-Agent (A2A) communication.
  • Verdict: Viable for the Router, but adds complexity for the Registry and Subscription services due to database connections.
  • Description: Running the services as Pods in a managed Kubernetes cluster.
  • Pros:
    • Native Fit: Traylinx is built as "Kubernetes for Agents"; running it on Kubernetes is the natural choice.
    • Resilience: Self-healing (auto-restart crashed pods).
    • Scalability: The Router Agent can auto-scale (HPA) independently to handle traffic spikes.
    • Zero-Downtime Deployments: Rolling updates for all services.
  • Verdict: The Best Production Setup. It provides the robustness required for an "Operating System" layer.

1. Infrastructure Layer (The Hardware)

  • Orchestration: Managed Kubernetes (GKE AutoPilot or EKS).
  • Database: Managed PostgreSQL (AWS RDS or Google Cloud SQL).
    • Do NOT run Postgres inside K8s for production. Let the cloud provider handle backups, replication, and patching.
    • Provision separate logical databases: registry, subscription, cortex.
  • Redis: NOT REQUIRED (Following the successful caching refactor).

2. Service Deployment Strategy

Each microservice maps to a Kubernetes workload type:

Service K8s Workload Scaling Strategy Storage Needs
Agent Registry Deployment Static (2-3 replicas) for HA. None (Stateless App, state in DB).
Router Agent Deployment Horizontal Pod Autoscaler (HPA). Scale on CPU/RAM. None (Totally Stateless).
Subscription Service Deployment Static (2 replicas). None (State in DB).
Traylinx Cortex StatefulSet Single replica (if holding memory state) or Deployment (if using external DB). PVC for caching (optional).

3. Connectivity & Security

  • Ingress Controller: NGINX Ingress or AWS ALB Controller to manage external access.
  • TLS Termination: usage of cert-manager with Let's Encrypt for automatic HTTPS.
  • Authentication (Sentinel):
    • Keep using the managed Traylinx Sentinel (api.makakoo.com) as the Identity Provider (IdP).
    • Inject TRAYLINX_CLIENT_ID and TRAYLINX_CLIENT_SECRET via Kubernetes Secrets.

⚠️ Readiness Gap Analysis

Before deploying to this architecture, the following gaps must be addressed:

  1. Missing Docker Assets:
    • The traylinx_router_agent is missing a production Dockerfile.
  2. Configuration Management:
    • Services currently default to localhost DB strings. We need to ensure DATABASE_URL is overrideable via environment variables (this appears supported but verify).
  3. Observability:
    • Production needs centralized logging (Fluentd/Datadog) and metrics (Prometheus/Grafana) to trace A2A calls across the cluster.