Observability and SLO Platform on Kubernetes

What and When

Observability lets teams understand a system's internal state from its outputs: metrics, logs, and traces. An SLO platform turns that telemetry into service level objectives and error budgets that drive alerting and prioritization. Build this when microservices make failures hard to diagnose, when alert fatigue is high, or when you want to manage reliability with data rather than gut feel.

This design runs entirely on Kubernetes using open standards so it is portable across clouds and avoids vendor lock-in.

Components

• OpenTelemetry Collector receives metrics, logs, and traces from instrumented services in a vendor-neutral format and routes them to backends.
• Prometheus stores metrics and evaluates alerting rules; Thanos adds long-term storage and a global query view across clusters.
• Loki stores logs cheaply with label-based indexing; Tempo stores distributed traces.
• Grafana unifies metrics, logs, and traces in dashboards and visualizes SLOs and error budgets.
• Alertmanager deduplicates and routes alerts to on-call channels.

Data Flow

Services export telemetry via the OpenTelemetry SDK to the Collector running as a DaemonSet. The Collector batches and forwards metrics to Prometheus, logs to Loki, and traces to Tempo. Prometheus evaluates SLO-based rules and fires alerts through Alertmanager when an error budget is burning too fast. Engineers open Grafana to correlate a metric spike with related logs and a trace, pivoting across signals to find the root cause.

Scaling and Resilience

Prometheus scales by sharding and federating, with Thanos providing durable object storage and global queries across clusters. Loki and Tempo store data in cheap object storage, decoupling retention cost from query nodes. The Collector buffers telemetry to absorb backend slowdowns. Run components across nodes and zones, and cap cardinality to keep Prometheus healthy under high label volume.

Security

Telemetry can leak secrets, so scrub sensitive fields at the Collector before storage. Secure scrape and ingest endpoints with mTLS and authentication. Apply least privilege to Grafana and to query access, since logs and traces often contain sensitive data. Retain data per policy and protect dashboards that reveal architecture. Treat the observability stack as production infrastructure with its own monitoring.

Trade-offs and Alternatives

A self-hosted open-source stack avoids licensing cost and lock-in but demands real operational effort, especially managing Prometheus cardinality and storage. Managed platforms such as Datadog, Grafana Cloud, or Honeycomb trade cost for less operational burden and richer features. The OpenTelemetry layer keeps instrumentation portable, so you can start self-hosted and move to managed later without re-instrumenting applications.