Start with the questions the platform must answer

Enterprise Telemetry Optimisation Strategies for Cost-Controlled, Resilient Observability

At 18:03 on a weekday evening, a payment service started timing out in Johannesburg. The first alert blamed API latency. The second blamed a Prometheus instance. Neither was the real problem: load-shedding had moved part of the workload to a backup site, increasing round-trip latency to eu-west while a burst of debug logs filled the Loki ingestion queue. The incident was visible everywhere, but understood nowhere.

That is the failure mode this article addresses. Enterprise Telemetry Optimisation Strategies are not about collecting less data indiscriminately. They are about making telemetry cheaper, more reliable and more useful during the exact moments when engineers need it most.

Start with the questions the platform must answer

Telemetry optimisation begins with operational questions, not agents and dashboards. For a South African hybrid estate, the questions might be:

• Can customers complete payments while traffic is served from a secondary region?
• Is elevated latency caused by the application, an upstream dependency or the network path to eu-west?
• Which services are exhausting database connections?
• Did a deployment increase error rates, or is the apparent increase caused by a change in log volume?
• Can the organisation investigate an incident without exporting personal information outside the approved jurisdiction?

Map each question to a signal and a retention period. Metrics usually answer “how much” and “how often”; traces explain a request’s path; logs provide event detail. Profiles can explain CPU and memory behaviour when traces show that a service is slow but not why. Recent observability practice is moving towards combining Prometheus with OpenTelemetry rather than treating them as competing choices. Grafana’s 2025 survey reported that 71% of respondents used both in some capacity, while OpenTelemetry continued to gain momentum as a vendor-neutral instrumentation framework.[4]

Use service-level objectives as the filter. A customer-facing checkout service may need high-resolution latency metrics and sampled traces for several weeks. A batch reconciliation worker may need daily success metrics and short-lived logs. Treating every workload identically is expensive and produces noisy alerts.

Control cardinality before it controls the bill

Unbounded labels are one of the fastest ways to make Prometheus and Mimir expensive. Request IDs, full URLs, email addresses and user-agent strings do not belong in metric labels. They create a new time series for every changing value and make queries slower at the same time.

Prefer bounded dimensions such as service, route, method, status_class and region. Normalise routes before instrumentation so that /orders/12345 becomes /orders/{id}. Keep high-cardinality values in logs or trace attributes, where they can be sampled and retained selectively.

sum by (service, route, status_class) (
  rate(http_server_requests_total[5m])
)

The query is useful only if route is already bounded. A dashboard cannot repair a poor label model. Establish recording rules for expensive, frequently used queries and review series growth as part of service ownership. Mimir provides horizontally scalable, long-term Prometheus storage, but scale should not become an excuse to ship uncontrolled telemetry.[8]

For infrastructure across Cape Town, Johannesburg and a cloud region, add location labels deliberately. A small fixed set of site and region values can expose failover behaviour without multiplying series by hostnames, pod hashes and ephemeral instance IDs.

Sample traces where investigation value is highest

Tracing every request at full fidelity is rarely necessary. A better policy combines head sampling, tail sampling and temporary incident overrides. Normal traffic can use a modest baseline sample. Errors, slow requests and requests crossing a regional boundary should receive higher priority.

Tail sampling is particularly useful because the decision can be made after the trace completes. Keep traces containing server errors, latency above the checkout SLO, unusual retry counts or calls to a degraded dependency. Sample successful, fast requests at a lower rate to preserve service maps and representative performance data.

Tempo is suited to this model because it stores distributed traces at high volume and links traces with logs and metrics in Grafana.[3] Put the sampling policy in the collection layer, close to the workload, rather than forwarding every span across an expensive or unreliable last-mile connection.

During a load-shedding event, telemetry pipelines need their own resilience plan. Buffer locally, use bounded queues and apply backpressure. Do not allow an exporter retry loop to consume application CPU or block request handling. For remote sites with intermittent connectivity, prioritise alerts and error traces, then forward lower-value telemetry when the link recovers.

Make Loki useful without turning it into a data lake

Loki works best when labels identify a small number of stable streams and the log body carries the searchable detail. Labels such as cluster, namespace, app and level are generally more sustainable than request IDs or customer identifiers.

{cluster="jhb-prod", app="payments"} 
| json
| level="error"
| unwrap duration_ms
| quantile_over_time(0.95, [5m])

Structured JSON logs make correlation practical, but they also create a POPIA risk if payloads are copied blindly. Redact identity numbers, payment details, access tokens and unnecessary personal information at the application or collector boundary. Retain a short operational window for verbose logs, a longer period for security-relevant events, and archive only what governance and incident response actually require.

Separate “debug enabled” from “debug retained”. A service can temporarily increase diagnostic detail during an incident, but the change should have an owner, an expiry time and a cost estimate. If the setting survives the incident, the platform eventually pays for an emergency that ended weeks ago.

Use Grafana to connect signals, not multiply dashboards

A useful dashboard answers one operational question from several angles. Start with an SLO panel, then link from a latency or error metric to exemplars, traces in Tempo and matching logs in Loki. This shortens the path from symptom to cause. Grafana supports these links across its observability stack, including trace-to-log navigation and service graphs.[7]

Keep dashboard variables bounded and avoid panels that issue dozens of wide-range queries on every refresh. Set sensible time ranges, use recording rules for common aggregations and display the data resolution appropriate to the question. An executive availability view does not need one-second samples; an on-call latency investigation might.

For Mimir, separate tenants or namespaces where governance, retention and query workloads differ. Apply per-team limits to prevent one exploratory query from affecting production alerting. Alert rules should run close to the metrics backend and should be tested against missing-data conditions, not only clean failures.

Design alerts for outages, failover and silence

Load-shedding-aware alerting requires more than adding “power” to a dashboard. A site transition can create predictable noise: hosts disappear, scrape targets move, queues drain slowly and latency rises while traffic is rerouted. Alert on customer impact first, then on the infrastructure condition causing it.

• Alert on burn rate for critical SLOs rather than every short-lived latency spike.
• Distinguish a failed scrape from a failed service and route each to the correct team.
• Use maintenance or failover labels to suppress known secondary symptoms without hiding the primary alert.
• Monitor telemetry health separately: scrape freshness, remote-write lag, collector queue depth and dropped spans.
• Test alert delivery during generator operation and degraded connectivity.

A silent monitoring system is not a healthy system. Include “no data” and “stale data” scenarios in game days. If Mimir receives no samples from a site, the absence itself may be the incident.

Measure telemetry as a production workload

Telemetry should have an owner, a budget and service-level expectations. Track samples per second, active series, bytes ingested, log volume, trace retention, dropped records, query latency and alert evaluation delay. Express cost in ZAR where possible, because cloud invoices are reviewed in rand even when the underlying services are priced in dollars.

Review these measures monthly with application teams. Remove unused dashboards, obsolete recording rules and duplicate agents. Standardise instrumentation through OpenTelemetry where it reduces vendor-specific code, while retaining Prometheus-native collection where it is already dependable. OpenTelemetry’s growing adoption reflects this need for a common collection and instrumentation layer.[5]

The strongest Enterprise Telemetry Optimisation Strategies are therefore operational rather than cosmetic: bounded labels, intentional sampling, local buffering, privacy-aware logs, SLO-driven alerts and controlled retention. When the next regional failover happens, the objective is not to have every byte. It is to have the few signals that explain what customers are experiencing, why it is happening and what the next safe action should be.