Firetiger vs Grafana Cloud
Grafana Cloud is the leading OSS-anchored observability stack — managed Prometheus, Loki, Tempo, and Grafana itself, with the open-source ecosystem underneath as the reason engineers chose it. Firetiger occupies a different layer: it reads each PR's diff, generates a change-specific monitoring plan, watches the deploy, and produces a per-deploy verdict. Grafana Cloud collects and visualizes telemetry; Firetiger interprets that telemetry in the context of a specific change. Both layers live happily in the same stack.
Why it matters
Grafana Cloud appeals to teams that want OSS portability, transparent pricing, and the cultural fit of the Prometheus/Loki/Tempo lineage — and Firetiger fits naturally on top because both treat OpenTelemetry as a first-class ingest path. Across teams Firetiger has worked with that already run Grafana Cloud, the diagnostic phase of incidents runs 30+ minutes when engineers are scrolling between Grafana dashboards and deploy timelines, and under five minutes once Firetiger verdicts land on the PR with the affected scope identified. The OSS-leaning audience that picked Grafana Cloud usually values the same property in a verification layer — that the data formats are open, that you can change vendors without changing instrumentation, and that the layer answers a specific question rather than trying to be the whole stack.
This article walks through what Grafana Cloud is great at, where the gap remains, how Firetiger differs, and when teams should use both.
What Grafana Cloud is great at
Grafana Cloud has consolidated the dominant OSS observability stack into a managed platform without losing the open-data-format properties that made the underlying projects popular.
Managed Prometheus, Loki, and Tempo. The three projects that anchor modern OSS observability — metrics (Prometheus), logs (Loki), and traces (Tempo) — all available as managed services with the standard Grafana frontend. Teams get the OSS data model and query languages (PromQL, LogQL) without operating the underlying infrastructure.
Grafana as the visualization layer. Grafana is the de facto open-source dashboarding tool, and the depth of community-built dashboards, panels, and plugins is among the deepest in the industry. Teams that adopt Grafana inherit a substantial existing knowledge base.
OpenTelemetry-native. Grafana Cloud is a first-class OpenTelemetry destination, which means teams that instrument with OTel can ship telemetry to Grafana Cloud (and elsewhere) without vendor-specific agents.
Transparent, OSS-friendly pricing. Compared to the per-host or per-metric pricing common in the enterprise observability category, Grafana Cloud's pricing is more transparent and tends to align better with usage. The OSS heritage and lock-in story is the more meaningful differentiator for many teams.
Portability and vendor-neutrality. Because the underlying data and query model are open, teams running on Grafana Cloud can move to self-hosted Grafana/Prometheus/Loki/Tempo (or to another managed provider) without rewriting their instrumentation or their dashboards. This is structurally different from a proprietary platform like Datadog or New Relic.
For teams whose values lean toward OSS portability and Prometheus-style instrumentation, Grafana Cloud is the leading commercial choice in the category.
Where the gap remains
Grafana Cloud is an observability stack. It describes production state through metrics, logs, and traces. It does not, by itself, attribute that state to specific changes or produce per-deploy verdicts.
Change attribution is a Grafana annotation. Grafana supports deploy annotations on dashboards — vertical lines that mark when a deploy happened. This is useful context but not attribution. When a latency regression appears, the engineer still has to infer which of the annotated deploys is the most likely cause.
Alerting is threshold-based. Grafana's alerting model (whether through Grafana itself or via the underlying Prometheus alertmanager) is mature but fundamentally threshold-driven. Subtle regressions that don't cross a threshold don't fire alerts, even when they're clear regressions against the pre-deploy baseline.
No per-PR monitoring plan. The dashboards a team builds in Grafana reflect what mattered at the time. They don't change with each new PR, and there's no built-in concept of "what should this specific change cause in the data?"
Intent verification is outside the model. Grafana can show you the change in a metric. It can't tell you whether the change in the metric is consistent with what the PR was supposed to do. That intent-level check is the work deploy verification does.
The gap is structural to the category, not a deficiency in Grafana specifically.
How Firetiger differs
Firetiger is built around the change event, not system state.
For each PR, Firetiger reads the diff and description, generates a monitoring plan, 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 — including the relevant Grafana panels or Prometheus queries when the team wants to dig deeper.
The verdict is anchored to the specific PR. Attribution is resolved by construction.
Firetiger consumes telemetry; it doesn't collect it. The system reads from OpenTelemetry, Prometheus-compatible endpoints, and other OSS-friendly sources. The verdict surface is the PR, Slack, the incident timeline — not yet another dashboard.
The mental model: Grafana Cloud collects and visualizes the data. Firetiger interprets the data in the context of each specific change and produces an outcome.
When to use both
Almost always. Grafana Cloud and Firetiger are designed to live in the same stack.
Grafana Cloud as the telemetry layer. Firetiger consumes Prometheus metrics, Loki logs, and Tempo traces. The team doesn't re-instrument; the existing OSS pipeline feeds verification.
Grafana for engineer-led exploration; Firetiger for per-change verdicts. Grafana dashboards remain the right tool for ad-hoc investigation, capacity planning, and the standing health view. Firetiger's verdict is what posts to the PR after each deploy.
Grafana annotations enriched by Firetiger context. When a Grafana dashboard shows a regression, the engineer can cross-reference the corresponding Firetiger verdict for the deploy at that time to get the attribution and recommended action.
OSS portability stays intact. Adopting Firetiger doesn't lock the team into a proprietary instrumentation format. The instrumentation stays OpenTelemetry; the telemetry stays in the OSS ecosystem; Firetiger sits on top.
When to evaluate Firetiger first
Grafana Cloud is foundational for teams that have already invested in the OSS observability path. The question is when, with that foundation in place, deploy verification is the next layer worth adding.
The signals:
Engineers spend most incident time on "which change?" Grafana dashboards describe the symptom. Verification names the change. If the team's diagnostic phase routinely runs longer than the fix phase, verification is where the leverage is.
Subtle regressions slip past Prometheus alerts. When alerts fire only on catastrophic conditions and subtle regressions are detected by engineers running PromQL after the fact, the threshold-based model is missing the slow leaks. Change-aware baselines catch what static thresholds miss.
Deploy frequency is rising. Manual ad-hoc verification via Grafana doesn't scale with deploy volume. Per-PR verification does.
AI coding tools are pushing PR volume up. As Cursor, Claude Code, and Codex generate more PRs per day, automated post-deploy verification becomes the only structural way to keep up.
Change failure rate measurement is shaky. Teams calculating CFR from revert detection or ticket archaeology generally know the number is undercounting. A telemetry-grounded verdict per deploy fixes the source.
Where to start
- Keep Grafana Cloud as the observability foundation. Verification consumes telemetry; it doesn't replace it. Grafana's place in the stack is secure.
- Audit which of your incidents were deploy-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. Two to four weeks on a single OSS-instrumented service produces clear verdicts and a quick sense of fit.
- Plan for verdicts to land where engineers act — not in another dashboard. Verdicts in PR comments and Slack get used; verdicts in a separate UI get ignored. See How to evaluate deploy verification tools.