Firetiger vs SigNoz
SigNoz is the OTel-native open-source observability platform — a single-pane replacement for the metrics, logs, and traces capabilities of Datadog or New Relic, built around OpenTelemetry from the start. Firetiger is a different layer: it reads each PR's diff, generates a change-specific monitoring plan, watches the deploy, and produces a per-deploy verdict. SigNoz tells you what production is doing; Firetiger says whether a specific PR caused a regression. Both layers consume OpenTelemetry and live happily in the same stack.
Why it matters
SigNoz appeals to teams that want OTel-native open-source observability without operating Prometheus, Loki, and Tempo separately. Firetiger pairs naturally because both treat OpenTelemetry as the canonical instrumentation format. Across teams Firetiger has worked with that already run SigNoz, the diagnostic phase of incidents typically runs 20-40 minutes against SigNoz dashboards alone and under five minutes once Firetiger verdicts land on the PR with the affected scope identified. SigNoz is observability; Firetiger is deploy verification. The combination delivers an OSS-friendly, OTel-native stack from instrumentation through per-change verdict.
This article walks through what SigNoz is great at, where the gap remains, how Firetiger differs, and when teams should use both.
What SigNoz is great at
SigNoz is one of the most active OSS observability projects and has grown a real audience among teams who want a Datadog-style experience without the proprietary lock-in.
OpenTelemetry-native. SigNoz was built around OTel from the start. Metrics, logs, and traces all flow through OTel pipelines into SigNoz's backend without proprietary agents. For teams that have made the OTel bet, SigNoz is one of the cleanest landing spots.
Single-pane experience. Unlike the Prometheus-plus-Loki-plus-Tempo split, SigNoz unifies metrics, logs, and traces into one product. The single-pane experience is one of the things teams cite when comparing SigNoz to assembling the OSS stack manually.
Self-hosted or managed. SigNoz can be self-hosted (the OSS project) or run as SigNoz Cloud. The flexibility matters for teams that want to start self-hosted and move to managed (or vice versa) without changing instrumentation.
ClickHouse-backed storage. SigNoz stores its data in ClickHouse, which means high-cardinality dimensions and ad-hoc queries are economically reasonable in a way they aren't in traditional time-series backends.
Active OSS community. The project has a sizeable community and active development, with regular feature additions and a clear roadmap.
For teams committed to an OTel-native, OSS-anchored observability path, SigNoz is among the strongest options. The case for using it rarely needs to be re-made once instrumentation is in place.
Where the gap remains
SigNoz is an observability platform. It describes production state through the OTel data model. It does not, on its own, attribute that state to specific changes or produce per-deploy verdicts.
Change attribution is a manual exercise. SigNoz can show you a latency spike, a trace pattern change, an error rate increase. It does not, by default, say which of the recent deploys is the most likely cause. The engineer doing investigation supplies the attribution.
Alerting is threshold-based. SigNoz supports alerting, but it remains threshold-driven — fire when a metric exceeds a fixed value. Subtle regressions that don't cross a threshold but are clearly regressions against the pre-deploy baseline don't generate alerts.
No per-PR monitoring plan. SigNoz dashboards are static artifacts. They don't change with each new PR, even when the PR introduces behavior the existing dashboards don't cover.
Intent verification is outside the model. SigNoz shows you what's happening. It doesn't tell you whether what's happening is consistent with what the latest PR was supposed to do. The intent-vs-outcome question is what deploy verification adds.
None of these are weaknesses in SigNoz — they're properties of being an observability platform. The category describes state; deploy verification interprets that state in the context of each 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, 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 SigNoz queries when the team wants to dig deeper.
The verdict is anchored to the specific PR. Attribution is resolved by construction.
Firetiger reads OpenTelemetry signals directly, which is the same data SigNoz consumes. No re-instrumentation. The verdict surface is the PR, Slack, the incident timeline — not another dashboard.
The mental model: SigNoz is the OTel-native observability surface for engineer-led investigation. Firetiger is the per-change verdict layer that posts an outcome to the PR on every deploy.
When to use both
The combination is the recommended pattern for OTel-native teams.
SigNoz as the telemetry destination. Firetiger reads from the same OTel pipeline that feeds SigNoz. The team's instrumentation work pays for both layers.
SigNoz for engineer-led exploration; Firetiger for per-change verdicts. SigNoz remains the right tool for ad-hoc investigation and the standing health view. Firetiger is what shows up on the PR after each deploy.
SigNoz for the alerts; Firetiger for the attribution. A SigNoz alert is a symptom. A Firetiger verdict is an attribution. The two flow into the incident workflow together — the alert fires, the verdict names the responsible PR.
OSS-friendly stack from end to end. Both SigNoz and Firetiger are designed to consume OpenTelemetry without proprietary agents. The stack retains its OSS character throughout.
When to evaluate Firetiger first
SigNoz is foundational for teams that have committed to the OTel-native OSS observability path. The question is when, with SigNoz in place, deploy verification is the next layer worth evaluating.
The signals:
Diagnostic time dominates incident duration. If the team's typical incident spends most of its wall-clock time on "what changed?" rather than on "how do we fix it?", verification is where the leverage is.
Subtle regressions slip past threshold alerts. When SigNoz alerts fire only on catastrophic conditions and partial regressions are detected by customer report or ad-hoc query, the alert model is structurally missing them. Change-aware baselines fix that.
Deploy frequency is rising. Per-PR verification scales with deploy volume in a way that manual SigNoz exploration doesn't.
AI-assisted PR volume is climbing. This is the acute version of the frequency problem. Automated post-deploy verdicts become structurally necessary when human review can't keep up with PR throughput.
Change failure rate is computed from tickets. A telemetry-grounded verdict per deploy is structurally cleaner than reconstructing CFR from incident records.
Where to start
- Keep SigNoz as the OTel-native telemetry layer. Verification consumes telemetry; it doesn't replace it.
- Audit the recent postmortems for deploy-caused vs environment-caused. The deploy-caused share is where Firetiger most materially shortens the diagnostic phase.
- Pilot on one high-frequency service. Two to four weeks on a SigNoz-instrumented service produces real verdicts on real deploys.
- Plan for verdicts to land in PR comments and Slack. Verdicts that live in the PR get acted on; verdicts in another dashboard get ignored. See How to evaluate deploy verification tools.