Real-Time Monitoring & Observability Platform

We use OpenTelemetry to build distributed traces that span every service boundary -- CDN edge, serverless functions, Contentful or Sanity webhooks, Algolia search calls, Auth0 or Clerk authentication. Custom correlation IDs propagate through the entire request lifecycle automatically. So when a user in Melbourne hits an error, you're not guessing. You pull the trace, follow it back, and you'll see the exact third-party API call that timed out or the cache invalidation that never completed. That's the difference between a fifteen-minute fix and a four-hour debugging session.

Raw telemetry costs spiral fast on high-traffic platforms -- honestly faster than most teams expect. We implement pre-ingest filtering and intelligent sampling that typically cuts observability platform costs by 40-60% compared to naive instrumentation. But here's the thing: tail-based sampling means you capture 100% of errors and slow requests while sampling routine successful requests at lower rates. You're not flying blind on the stuff that matters. You're just not paying to store millions of identical 45ms successful cache hits.

Yes, and we're pretty opinionated about not ripping out platforms you've already invested in. OpenTelemetry is our collection layer -- it's vendor-neutral by design, so we can route telemetry to Datadog, New Relic, Grafana Cloud, or any OTLP-compatible backend. Already running Datadog? We extend it with Next.js-specific dashboards, content pipeline alerts, and proper SLA reporting rather than starting over. Already on Grafana Cloud? Same approach. The instrumentation stays; we just make it actually useful for your specific stack.

From actual user experience -- not infrastructure status, which is a critical distinction. We deploy synthetic monitoring probes across your target regions running real browser checks every one to five minutes, then layer in RUM data from real user sessions. Infrastructure can report perfectly healthy while users are hitting errors from CDN misconfigurations, DNS propagation issues, or edge function cold starts. We've seen it happen on Cloudflare, Fastly, Vercel's edge network. Our SLA calculations are built from what users actually encountered, not what your load balancer reported.

Negligible, when it's done correctly -- and that caveat matters. Our OpenTelemetry instrumentation adds less than 2ms to server-side request processing. We ship logs asynchronously, use sampling strategies that reduce trace volume without losing error visibility, and deploy lightweight RUM snippets that don't touch your Core Web Vitals. Every project we instrument maintains Lighthouse 95+ scores. If your observability layer is meaningfully slowing your site down, it's been implemented wrong.

Tiered alerting built on SLO burn rates rather than raw error thresholds. Here's how it works in practice: a brief spike that consumes 0.1% of your monthly error budget gets logged, not paged. But a sustained issue burning through budget at 10x the normal rate? That's an immediate P1. And honestly, this approach cuts alert noise dramatically while catching real incidents faster -- because you're tracking trajectory, not just point-in-time error counts. Your on-call team stops ignoring pages, which means they actually respond when it counts.

Yes -- and this is a genuine blind spot for most headless setups, including ones with otherwise solid monitoring. We instrument the entire chain: CMS webhook delivery, build trigger acknowledgment, ISR revalidation success, CDN cache invalidation lag, and first-user-request timing, all correlated into a single timeline. If content isn't live within your target window -- say, 60 seconds from publish in Contentful -- an alert fires and tells you exactly which pipeline stage stalled. Not "something's wrong with content." The webhook delivery to your build hook timed out at stage three. Fix it in minutes.