deploy

Placeholder. Where the service (../02-measure/, ../03-analyze/, ../05-dashboards/, ../06-optional/mcp-server/) runs. Recipes, not scripts: the user's agent adapts them to local infra rules.

• Input: target environment and its rules.
• Output: running service, endpoint URL, auth.
• Data it touches: the chosen host only.

Coverage (tentative priority)

• Local trial: python3 ../02-measure/collector.py.
• First team rollout: Docker Compose on one VM.
• Managed containers: AWS (ECS Fargate), Google Cloud Run, Azure Container Apps.
• Kubernetes: Helm chart.
• Existing backend: harnesses point at Datadog, Grafana or an OpenTelemetry Collector (../04-auto-analysis/cloud-logs.md); analyze reads from there.

Checklist

checklist.md: what a deployment has, lacks, or could add. Start and end every install by reading it.

Workflow

1. Ask: where, who can reach it, data region, retention.
2. Deploy, put TLS and auth in front (../06-optional/identity.md).
3. Verify with a test record end to end.
4. Hand endpoint and auth to the workstation install (../02-measure/AGENTS.md).

Pitfalls

• Laptops must reach the endpoint: VPN, zero-trust proxy, or a public TLS endpoint with a token.
• Endpoint down means silent data loss on workstations; monitor ingest rate, alert on drop to zero.
• Clock skew between machines breaks session to commit linking; store server receive time too.
• Backups and retention are the owner's call; state the defaults.