Error Tracking & Observability
Capture errors, traces, logs, and metrics; route alerts; and track SLOs, release regressions, and synthetic checks across frontend and backend.
Options
Observability Backend*
Signal Coverage*
Alerting & Routing*
Reliability Engineering
Decision Points
Managed (Sentry, Datadog, Honeycomb) or self-host?
If yes
Default to managed. Sentry for errors, Datadog/New Relic for APM, Honeycomb for high-cardinality tracing. Self-hosting is only defensible at real scale or for regulated data that cannot leave your infrastructure.
If no
Self-host only with a dedicated ops owner. The Grafana LGTM stack is the pragmatic choice.
End-to-end tracing via OpenTelemetry?
If yes
Instrument via OTel SDKs and collectors. Avoids vendor lock-in and lets you route different signals to different backends. Adoption overhead is real but pays off.
If no
Vendor-native SDKs are faster to integrate but locks your instrumentation into one provider. Fine for small teams.
Separate log aggregation from metrics backend?
If yes
Yes — logs belong in a cheap searchable store (Loki, CloudWatch), metrics in a purpose-built time-series DB. Mixing them inflates cost and slows queries.
If no
Unified backends (Datadog) work at small scale but become expensive once log volume grows.
Sample errors at high volume or capture all?
If yes
Sample aggressively above a threshold — cap per-fingerprint events-per-minute so a single noisy loop cannot blow the budget. Capture 100% of net-new errors always.
If no
Capturing every event is feasible only at low volume; beyond a threshold it becomes a cost and signal-to-noise problem.
Upload source maps automatically on deploy?
If yes
Wire source-map upload into your CI pipeline and tag by release. Without this, production JS stack traces are unreadable and debugging frontend errors is guesswork.
If no
Acceptable only for internal tools where minification is off. Public-facing apps must have it.
Scrub PII from error payloads?
If yes
Configure SDK scrubbers for emails, tokens, card numbers, and request bodies. Scrub at the SDK, not after ingest — once the data is in the vendor, it is effectively there forever.
If no
The default SDK config will ship PII. Regulated industries and SOC 2 will require scrubbing.
Alerts routed to Slack, PagerDuty, or email?
If yes
Use all three with severity-aware routing. Slack for business-hours visibility, PagerDuty for on-call paging, email for FYI/digest. Email-only alerts get ignored.
If no
Single-channel is only viable for solo teams. Any team over ~5 people needs severity routing.
Auto-assign errors to owning team by code path?
If yes
Use CODEOWNERS-style metadata or vendor ownership rules. Routes the page straight to the right team and eliminates triage toil.
If no
Manual triage scales poorly beyond ~3 teams. Fine for small engineering orgs.
Tag errors with release version for regression detection?
If yes
Tag every event with the deploy SHA/version. Makes "did our deploy break X?" answerable in seconds and enables first-seen-in-release alerts.
If no
Untagged errors make post-deploy triage painful. Essential for any team shipping more than weekly.
Capture user session replay on errors?
If yes
Mask PII by default, sample replays (not every session), and keep a short retention window. The reproduction value is real but privacy overhead is not optional.
If no
Replay is not required; good breadcrumbs plus structured logs are usually enough for reproduction.
SLOs / error budgets tracked and reported?
If yes
Define 2–3 user-facing SLOs per critical service; alert on burn rate, not raw counts. This is the single highest-leverage change against alert fatigue.
If no
Threshold alerts are fine for small systems but produce noise at scale. Plan to adopt SLOs once on-call exists.
Synthetic monitoring for critical user flows?
If yes
Scripted probes from multiple regions against login, checkout, and the top revenue flows. Catches DNS/CDN/third-party outages that your app-internal metrics miss.
If no
Real-user signal catches most issues eventually, but synthetics are the only way to detect outages before a user reports them.
Per-tenant error isolation in dashboards?
If yes
Tag every event with tenant/workspace-id and build scoped dashboards. Support and CS will use this constantly to answer "is this just us?"
If no
Global dashboards only are fine for consumer products; multi-tenant B2B SaaS will need per-tenant views quickly.
Unify frontend and backend errors in one view?
If yes
Propagate trace-ids from the browser into backend spans and ingest both into the same vendor. Massively shortens root-cause time for full-stack bugs.
If no
Separate views work for simple apps but force engineers to correlate by timestamp and URL — painful at scale.
Auto-open tickets for new recurring errors?
If yes
Wire a threshold rule (N occurrences in M minutes) to Linear/Jira. Tune thresholds carefully — overly sensitive rules produce ticket spam and teams disable them.
If no
Manual ticket creation is fine until the team outgrows it; automate only once triage volume justifies the upkeep.
Tradeoffs
Fastest time-to-value but costs scale with event volume — retention and sampling tuning becomes a finance problem
Ingest and storage cost can exceed the rest of your observability spend combined; sample aggressively
Alert fatigue drops meaningfully — only issues that threaten user experience reach pagers
Requires dedicated operational ownership — schema migrations, retention policy, and scaling all become your problem
Implementation Examples
Error tracking with release tracking, source-map support, and an OSS self-host option.
Unified metrics, logs, traces, RUM, and synthetics with deep integrations — expensive but comprehensive.
High-cardinality tracing for debugging complex distributed systems with BubbleUp and SLO tooling.
APM, logs, and infrastructure monitoring in one platform with consumption-based pricing.
Open-source observability stack: Loki (logs), Grafana (dashboards), Tempo (traces), Mimir/Prometheus (metrics).
Vendor-neutral instrumentation standard for traces, metrics, and logs; pluggable into any compatible backend.