Deploy monitoring tools compared
If you're trying to make sure your deploys don't break production, you'll find that the tools people point you to — Datadog, Sentry, New Relic, Grafana, GitHub Actions — were each built for a different job, and "monitoring a deploy" sits awkwardly across all of them. This page compares them by the job they actually do, so you can tell which one solves your problem: catching the bug your last change introduced.
The short version: APM and metrics platforms answer "is the system healthy?", error tracking answers "what's throwing?", CI smoke tests answer "did it boot?", and per-change monitoring answers "did the change I just shipped break anything, and did it do what I intended?" Most teams need more than one, but only the last category is built specifically for the deploy window.
APM and metrics platforms — Datadog, New Relic, Grafana, Honeycomb, Prometheus
What they're for: continuous, always-on observability. They collect traces, metrics, and logs and let you build dashboards and threshold alerts across your whole system. This is the foundation layer, and you almost certainly want one.
Where they fit deploys: most support deployment markers or release tracking, so you can overlay "a deploy happened here" on a graph and set alerts on key metrics.
The gap for deploy monitoring: the thresholds are static and human-defined. After a deploy, deciding which of your thousands of metrics matter for this specific change, what their baseline should be right now, and whether a movement is a real regression or just normal traffic variance — that judgment is still manual. The platform shows you everything and helps you decide nothing. That's by design; it's a general observability tool, not a change-verification tool.
If you're searching for a "Datadog alternative" for deploy monitoring: you probably don't want to replace Datadog as your metrics store — you want the change-aware layer it doesn't provide. See per-change monitoring below.
Error tracking — Sentry, Rollbar, Bugsnag
What they're for: capturing exceptions and surfacing new or spiking error types, usually with release tagging so you can attribute an error to a deploy. If your regression is a thrown exception, this is excellent and fast.
The gap for deploy monitoring: a large share of post-deploy regressions never throw. A query that now scans 10× more rows, a doubled p99 latency, a collapsed cache hit rate, a feature flag that silently changed behavior — these degrade the product without raising an exception, so they don't appear in error tracking at all. Error tracking also can't tell you whether the change accomplished its intended effect; it only knows about failures, not about whether the improvement you shipped actually landed.
If you're searching for a "Sentry alternative" for deploy monitoring: Sentry is probably still the right tool for exceptions — you're looking for coverage of the non-exception regressions it can't see.
CI smoke tests — GitHub Actions and similar
What they're for: cheap, immediate post-deploy sanity checks. A workflow fires on a successful deploy, hits /health, curls a few critical routes, and fails if something 500s.
The gap for deploy monitoring: they only catch hard failures in the first few seconds. They miss anything that emerges under real traffic minutes or hours later, anything gradual, and anything that "works" but is subtly wrong. You also write and maintain every check by hand, and they drift out of date as the system grows.
Worth keeping: smoke tests are a good first line of defense. They're just not sufficient on their own.
Per-change monitoring — Firetiger Change Monitors
What it's for: the specific question the others leave unanswered — "did the change I just deployed break something, and is it doing what I intended?" A change monitor reads the diff and description of the PR you deployed, decides which signals matter for that change, computes fresh baselines from your existing telemetry, watches the rollout across each environment, and reports back on the PR.
How it covers the gaps:
- It catches non-exception regressions (latency, query volume, cache behavior) because it watches the signals relevant to the diff, not just error counts.
- It verifies intended effect — if your PR was meant to cut database queries per request, it checks whether that actually happened, not just whether errors stayed flat.
- It's time-aware — checks run densely right after the deploy and taper over hours and days, so delayed-onset regressions still get caught long after a human would have moved on.
- It investigates, not just alerts — when something moves, it cross-references the anomaly against what the PR changed and posts a root-cause hypothesis you can hand to your coding agent.
Where it sits: on top of your existing stack, not instead of it. It reads the telemetry you already collect (including OpenTelemetry data) and adds the change-aware judgment that APM, error tracking, and CI checks don't provide.
How you trigger it: comment @firetiger monitor this PR, enable auto-monitoring on the GitHub connection, or have your coding agent (Claude Code, Codex, Cursor) start it via MCP — so monitoring attaches to changes automatically without a separate manual step.
Which should you use?
| Your question | The tool for it | |---|---| | Is the system healthy right now? | APM / metrics (Datadog, New Relic, Grafana) | | What's throwing exceptions? | Error tracking (Sentry, Rollbar, Bugsnag) | | Did the deploy at least boot? | CI smoke tests (GitHub Actions) | | Did the change I just shipped break anything — and did it do what I intended? | Per-change monitoring (Firetiger) |
Match the tool to the question you're actually asking:
- Is the system healthy right now? → APM / metrics (Datadog, New Relic, Grafana)
- What's throwing exceptions? → Error tracking (Sentry, Rollbar, Bugsnag)
- Did the deploy at least boot? → CI smoke tests (GitHub Actions)
- Did the change I just shipped break anything — and did it do what I intended? → Per-change monitoring (Firetiger)
For most teams the answer is a combination: an APM platform for raw signals and infrastructure alerts, error tracking for exceptions, and per-change monitoring for the deploy window itself. The mistake is assuming that because you have Datadog and Sentry, your deploys are covered — they cover health and exceptions, but not the change-verification question that causes most "it passed CI but broke in prod" surprises.
To set this up in practice, see how to catch bugs in production after you deploy.