Firetiger vs New Relic
New Relic is one of the largest legacy observability platforms, with deep APM coverage, application telemetry, infrastructure monitoring, and a long-standing place in the enterprise stack. Firetiger occupies a different layer: it reads each PR's diff, generates a change-specific monitoring plan, watches the deploy, and produces a per-change verdict. New Relic tells you how the application is behaving; Firetiger says whether a specific PR caused a regression. Most teams use both — Firetiger consumes telemetry that New Relic (or any other source) provides.
Why it matters
New Relic and Firetiger are not direct substitutes. They sit at different layers of a modern reliability stack and answer different questions. Across teams Firetiger has worked with that already run New Relic, the diagnostic phase of incidents typically runs 30-45 minutes against New Relic alone — engineers scrolling between APM, infrastructure, and log surfaces trying to piece together what changed — and under five minutes once Firetiger verdicts land on the PR with the affected scope and suspected change already named. New Relic is APM/observability; Firetiger is deploy verification. The two pair without displacing each other.
This article walks through what New Relic is great at, where the gap remains, how Firetiger differs, and when teams should use both.
What New Relic is great at
New Relic has been one of the most established observability vendors for over a decade, with deep APM strength and a broad enterprise footprint.
Deep APM coverage. New Relic's application performance monitoring is mature — transaction tracing, distributed tracing, service maps, slow-query analysis, and the supporting infrastructure for getting reliable APM data out of most application stacks. For teams that need APM as a first-class concern, New Relic is among the strongest commercial choices.
Unified telemetry data model. New Relic's NRDB stores metrics, events, logs, and traces in a single queryable backend with NRQL as the unified query language. The single-pane experience is genuinely useful for cross-data-type investigation.
Enterprise breadth. Beyond APM, New Relic covers infrastructure monitoring, real user monitoring, synthetics, mobile, browser, AIOps, and a long catalog of integrations. For teams that need broad coverage from one vendor, the platform delivers.
Established alerting and workflow integrations. New Relic's alerting and incident workflow integrations are well-trodden — PagerDuty, Slack, ServiceNow, and the rest. Teams adopting New Relic don't typically have to rebuild their alert routing.
Predictable pricing model. New Relic's per-user-and-ingest pricing model is well-understood in the market, and the long-tenure of the platform means most enterprise buyers know what to expect on the cost curve.
For teams that have already committed to New Relic, the case for staying rarely needs to be re-made; the migration cost is real, and the platform delivers on its core APM promise.
Where the gap remains
New Relic is an observability platform. Like Datadog, Honeycomb, and the rest of the category, it describes production state — it does not, on its own, attribute that state to specific changes or produce per-deploy verdicts.
Change attribution is a manual exercise. When an APM dashboard shows a latency regression, New Relic does not, by default, say which of the last several deploys is the most likely cause. The engineer scrolls through the deploy markers and infers the connection. That inferential step is where most incident wall-clock time goes.
Static thresholds dominate the alert model. New Relic's alerting is mature and flexible, but it remains threshold-based. A regression that moves error rate from 0.5% to 1.0% on one endpoint doesn't trip a reasonably-configured global alert, even when it's a real deploy-caused regression worth catching.
No per-PR monitoring plan. The dashboards and alerts a team builds in New Relic reflect what mattered when they were authored. They don't change when a new PR ships, even if that PR introduces behavior the existing alerts don't cover.
Intent verification is outside the model. New Relic shows you that the application is up and responding. It doesn't tell you whether the change that was supposed to reduce p99 latency by 15% actually did. The intent-vs-outcome question requires authoring a per-deploy expectation, which is exactly what deploy verification does and APM categorically does not.
None of these are weaknesses in New Relic. They are properties of being an APM/observability platform. Observability describes; deploy verification interprets in the context of a change.
How Firetiger differs
Firetiger is built around the change event, not the system state.
For each PR, Firetiger reads the diff and description, generates a monitoring plan describing what the change is expected to do and what signals should move (or stay flat), watches the deploy across staging, canary, and production, and posts a per-deploy verdict back to the PR. When a regression is detected, the verdict identifies the affected scope, the suspected code path, the change author, and the supporting telemetry.
The verdict is anchored to the specific PR. The attribution problem is resolved by construction.
Firetiger consumes telemetry; it does not collect it from scratch. The system reads from OpenTelemetry sources and can ingest New Relic-instrumented signals through OTel-compatible pipelines. The verdict surface is the PR, Slack, the incident timeline — not a separate dashboard.
The mental model: New Relic is the application telemetry layer. Firetiger is the layer that interprets that telemetry in the context of each specific change and produces an outcome.
When to use both
Most teams running New Relic will continue to run New Relic and benefit from adding Firetiger on top. The pairing is additive.
New Relic as the telemetry source. Firetiger reads relevant signals from New Relic when evaluating a deploy's monitoring plan. The team doesn't have to re-instrument anything; the existing New Relic instrumentation feeds verification.
New Relic for the application-wide view; Firetiger for the change-specific view. The team continues to use New Relic dashboards for service-wide health, capacity planning, and ad-hoc investigation. They use Firetiger for the "did this PR work?" question on every deploy.
New Relic for the alerts; Firetiger for the verdicts. A New Relic alert is a symptom; a Firetiger verdict is an attribution. The two flow into incident workflow together — the alert fires, the verdict explains which deploy is the most likely cause.
Cleaner DORA reporting. Firetiger's verdicts feed engineering-intelligence dashboards as a structured CFR signal sourced from production behavior. That replaces or supplements the typical ticket-archaeology CFR computation.
When to evaluate Firetiger first
New Relic is foundational for teams that have already invested in it. The question is when, with New Relic in place, deploy verification is the next layer worth evaluating.
The signals:
Postmortems keep asking "which deploy was it?" If the recurring postmortem theme is "the change was identified twenty minutes into the incident," the diagnostic phase is what's costing the team. Verification gives that time back.
APM dashboards are clean but partial regressions still surprise the team. Static thresholds and APM dashboards catch availability problems. They miss subtle, per-slice regressions that hide inside an aggregate. Change-aware verification with per-slice baselines closes that gap.
Deploy frequency is rising. Manual post-deploy checking against New Relic dashboards doesn't scale past a small number of deploys per day. Per-PR verification scales with deploy frequency.
AI coding tools are increasing PR volume. This is the acute version of the frequency problem. Per-change verification scales with PR volume in a way that manual review against APM dashboards does not.
Change failure rate is calculated from tickets. Teams reporting CFR from incident tickets are usually undercounting. A verdict per deploy from telemetry produces a structurally cleaner number. See Why ticket-based DORA metrics fall short.
Where to start
- Keep New Relic for the application telemetry layer. Verification consumes telemetry; it doesn't replace it.
- Audit which of your incidents were deploy-caused vs environment-caused. Read the last ten postmortems. The deploy-caused share is the territory where Firetiger materially shortens the diagnostic phase.
- Pilot on one high-frequency service. A two-to-four-week pilot of Firetiger on a service already instrumented in New Relic produces real verdicts on real deploys without requiring re-instrumentation.
- Plan for verdicts to land in PR comments and Slack. Verdicts that live in the PR get acted on; verdicts that live only in a separate dashboard get ignored. See How to evaluate deploy verification tools.