How to scale Alloy as a central telemetry gateway: capacity planning, load testing, and production lessons

Grafana Alloy can serve as a centralized Kubernetes telemetry gateway that receives metrics, logs, and traces from many teams, applies shared authentication, routing, buffering, protocol normalization, and cost-attribution labeling, then forwards data to Grafana Cloud. A Grafana Labs engagement sized a deployment for roughly 17 million active metric series, 1 TB per day each of logs and traces, and significant peak throughput, estimating about 195 GB of memory and 28 CPU cores spread across 33–35 small pods, with no CPU limits and an HPA configured for 30 to 100 replicas. The recommended architecture places Alloy behind an ingress controller while sending Alloy and cluster health metrics through an independent monitoring path directly to Grafana Cloud, avoiding loss of observability during gateway stress. Load testing with telemetrygen and customized k6 instances validated performance at and above anticipated production volumes while tracking accepted and refused data, exporter queues, resource use, and ingress errors. The resulting production fleet scaled to about 60 pods and handled higher-than-planned traffic without reported issues, while key lessons included budgeting for growth rather than baseline demand, configuring retries, controlling write-ahead-log memory growth with GOMEMLIMIT, maintaining adequate minimum replicas to absorb traffic bursts, and ensuring cluster node autoscaling can support pod expansion. Future options include queue-depth-driven autoscaling with KEDA, splitting deployments by telemetry signal, and using a message broker where stronger pipeline decoupling justifies its operational cost.

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