We deploy OpenTelemetry as a vendor-neutral instrumentation layer across Next.js middleware, API routes, edge functions, and CMS webhook handlers, routing telemetry to Datadog or Grafana Cloud with intelligent sampling and pre-ingest filtering. Custom correlation engines link CMS publish events through the entire content pipeline to user-facing delivery, while tiered Slack/PagerDuty alerting driven by SLO burn rates eliminates noise without missing critical incidents. Automated SLA reports combine synthetic monitoring probes and RUM data to calculate real user-facing availability across all target regions.
How do you handle observability for headless architectures with multiple third-party services?
We use OpenTelemetry to create distributed traces that span every service boundary — CDN edge, serverless functions, CMS webhooks, search APIs, and auth providers. Custom correlation IDs propagate through the entire request lifecycle. When a user-facing error occurs, you can trace it back to the exact third-party API call or cache invalidation failure that caused it.
What's the cost impact of adding full observability to our platform?
Raw telemetry costs spiral fast on high-traffic platforms. We implement pre-ingest filtering and intelligent sampling that typically cuts observability platform costs by 40-60% compared to naive instrumentation. Tail-based sampling ensures you capture 100% of errors and slow requests while sampling routine successful requests at lower rates.
Can you integrate with our existing Datadog or New Relic setup?
Yes. We instrument with OpenTelemetry as the collection layer, which is vendor-neutral. That means we can route telemetry to Datadog, New Relic, Grafana Cloud, or any OTLP-compatible backend. If you're already invested in a platform, we extend it with web-platform-specific dashboards, alerts, and SLA reporting rather than ripping it out and starting over.
How do you calculate SLA uptime — from infrastructure status or actual user experience?
From actual user experience. We deploy synthetic monitoring probes across your target regions running real browser checks every 1-5 minutes, combined with RUM data from actual user sessions. Infrastructure can report healthy while users hit errors from CDN issues, DNS problems, or edge function cold starts. Our SLA calculations reflect what users actually see.
What's the performance overhead of full observability instrumentation?
Negligible when done correctly. Our OpenTelemetry instrumentation adds less than 2ms to server-side request processing. We use async log shipping, sampling strategies that reduce trace volume without losing error visibility, and lightweight RUM snippets that don't impact Core Web Vitals. Every project we instrument maintains Lighthouse 95+ scores.
How do you prevent alert fatigue while ensuring critical issues are caught?
Tiered alerting with SLO-based burn rates. Rather than alerting on every error spike, we track error budget consumption rates. A brief spike that consumes 0.1% of your monthly error budget gets logged but not paged. A sustained issue burning through budget at 10x the normal rate triggers an immediate P1 page. This cuts the noise while catching real incidents within minutes.
Do you monitor the content pipeline from CMS publish to user-facing update?
Yes, and this is a blind spot we specifically address. We instrument CMS webhook delivery, build trigger acknowledgment, ISR revalidation success, CDN cache invalidation, and first-user-request timing into a single correlated timeline. If content isn't live within your target SLA (e.g., 60 seconds from publish), an alert fires identifying the exact pipeline stage that failed.
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