AIOps & SRE Automation
incident.io vs Rootly vs FireHydrant — which one helps most with investigation vs just incident workflow?
9 min read
Most teams evaluating incident.io, Rootly, and FireHydrant are really asking two different questions at once: “Will this clean up my incident workflow?” and “Will this actually help me figure out what broke?” Those are not the same problem. All three tools are strong on orchestration (declaring incidents, comms, postmortems). None of them run real production investigations in the sense of forming hypotheses, querying telemetry, and delivering an evidence-backed root cause.
Quick Answer: incident.io, Rootly, and FireHydrant are incident management and workflow platforms first. They help you coordinate people and process, not autonomously investigate systems. For deep investigation and root cause, you still need observability tooling and, increasingly, an AI investigator like Cleric layered underneath.
Quick Answer: incident.io, Rootly, and FireHydrant are incident management platforms that excel at workflow, communication, and post-incident hygiene, but they do not replace Datadog-style debugging or an AI SRE teammate like Cleric that actually runs the investigation.
Frequently Asked Questions
Where do incident.io, Rootly, and FireHydrant actually help—and where do they stop?
Short Answer: They shine at incident workflow (declaring, triaging, coordinating, documenting) and stop just before the hard part: deeply investigating systems and finding root cause across logs, metrics, traces, and infra.
Expanded Explanation:
Think of these tools as “incident control planes.” They help you:
- Standardize how incidents are declared in Slack.
- Assign roles, severity, and ownership.
- Handle stakeholder comms and status pages.
- Capture timelines and postmortems.
Where they don’t go deep is the investigation itself. None of them are reaching into Datadog queries, Kubernetes APIs, or cloud APIs to form and test hypotheses like a human SRE would. They might have light integrations (e.g., link a Datadog dashboard or auto-create a Zoom), but they’re not autonomously narrowing down “this is probably a CrashLoopBackOff in service X caused by a config change in deployment Y.”
That’s the gap where an investigation engine like Cleric sits. Cleric plugs into the same Slack channels, but underneath it’s building a hypothesis tree, running queries against observability data, correlating alerts, and returning an evidence-backed diagnosis directly under the incident thread.
Key Takeaways:
- incident.io, Rootly, and FireHydrant are workflow and coordination platforms, not debugging engines.
- You still need observability tools—and, if you want automation, an AI investigator—for root-cause analysis.
How does the investigation process usually work with incident.io, Rootly, or FireHydrant in place?
Short Answer: They orchestrate who does what and when, but the actual investigation is still done manually in Datadog, Grafana, cloud consoles, and Kubernetes—then pasted back into Slack or the incident tool.
Expanded Explanation:
With any of the three tools, your on-call still follows roughly the same process:
- An alert fires from PagerDuty, Alertmanager, or similar.
- The incident bot helps you declare an incident in Slack.
- Roles/ownership get assigned; a channel or “war room” is created.
- From there, engineers leave Slack and begin digging through logs, metrics, traces, Kubernetes dashboards, and cloud consoles to figure out what broke.
The incident platform’s job is to track that work, not to do the work. It records who ran what command, links to related dashboards, and captures notes. Some add structured fields (impact, cause, mitigation) to make postmortems easier. But none are autonomously connecting “this CPU spike” with “this OOMKill” and “this rollout” to declare a probable root cause.
Cleric is built to plug into that gap. By the time the alert lands in Slack, Cleric has already started an investigation: querying Datadog, calling Kubernetes APIs, checking recent deploys in your cloud, comparing against prior similar incidents, and then posting a concise diagnosis with evidence and a confidence score.
Steps:
- Alert fires: PagerDuty or Alertmanager notifies Slack and/or creates an incident via incident.io/Rootly/FireHydrant.
- Workflow kicks in: The incident platform spins up channels, assigns roles, and tracks status; humans start context-gathering in Datadog/Grafana/Kubernetes/cloud.
- Manual investigation: Engineers run queries and commands, then paste findings back into the incident channel or postmortem fields.
With Cleric plugged in:
- Alert fires: Same trigger, but Cleric starts investigating immediately.
- Hypotheses & queries: Cleric forms theories, hits Datadog/Prometheus/CloudWatch, Kubernetes APIs, and cloud APIs in parallel.
- Diagnosis in Slack: Under the alert, you see a candidate root cause, supporting metrics/logs/traces, and recommended next steps—ready to “trust but verify.”
How do incident.io, Rootly, and FireHydrant compare on investigation vs workflow?
Short Answer: All three are closer to “incident CRM” than “debugging engine.” Differences are mainly in UX, opinionated workflows, and ecosystem fit—not in deep, automated investigation capabilities.
Expanded Explanation:
From a systems standpoint, they sit at the same layer: incident orchestration. None of them are designed to be an SRE that reads your telemetry and infers root cause.
Broadly:
- incident.io leans into product polish and opinionated workflows. Strong on Slack-first experience, incident templates, status updates, and postmortems. Investigation is still manual, anchored in whatever observability tools you use.
- Rootly positions heavily around automation in workflows: auto-runbooks, integrations, and action sequencing. Again, that’s about process, not reasoning over metrics/logs to find root cause.
- FireHydrant has heritage in infrastructure and emphasizes service catalogs, incident runbooks, and reliability programs. Stronger on connecting incidents to services and owners, but still not doing hypothesis-driven investigations.
If your main pain is chaotic comms, unclear ownership, and messy postmortems, these tools help a lot. If your main pain is “we spend hours figuring out what actually broke,” none of them solve that by themselves. You’re still paying the orientation cost on every incident.
Comparison Snapshot:
- Option A: incident.io / Rootly / FireHydrant
- Incident workflow, comms, roles, postmortems.
- Integrates with alerts and dashboards; does not autonomously reason about telemetry.
- Option B: Cleric (AI SRE teammate)
- Hypothesis-driven investigations across logs, metrics, traces, Kubernetes, cloud, and docs.
- Delivers an evidence-backed diagnosis and next steps directly under the alert in Slack.
- Best for:
- Use incident.io/Rootly/FireHydrant to standardize incident process.
- Use Cleric to compress time-to-root-cause and eliminate repetitive “where do I look?” work.
How would I actually implement deeper investigation alongside incident.io, Rootly, or FireHydrant?
Short Answer: Keep your existing incident workflow tool, and layer Cleric underneath it by connecting your observability stack, Kubernetes, and cloud APIs so every incident gets an automated investigation in Slack.
Expanded Explanation:
You don’t have to rip out any of the three platforms to get investigation automation. They remain your “incident shell.” Cleric becomes the investigation engine behind that shell.
Mechanically, Cleric:
- Listens to alerts from PagerDuty, Alertmanager, or Slack channels used by incident.io/Rootly/FireHydrant.
- Connects to Datadog/Prometheus/CloudWatch, Sentry/OpenSearch, Kubernetes APIs, and your cloud APIs (AWS/GCP/Azure).
- Builds an internal model of services, dependencies, and owners (semantic memory).
- Reuses previous investigation traces and engineer feedback (episodic memory).
- Encodes successful debugging patterns as reusable skills (procedural memory).
When a new incident is declared, Cleric doesn’t start from zero. It uses that accumulated production memory to orient fast, test the most likely hypotheses first, and show you its reasoning trail—so you can validate before acting.
What You Need:
- Read-only integrations to your stack: Datadog/Prometheus/CloudWatch, Sentry/OpenSearch, PagerDuty, Kubernetes APIs, and AWS/GCP/Azure. Cleric is read-only by default, with all actions logged and auditable, and your data encrypted and never used for training.
- Slack workspace with incident channels: So Cleric can post diagnoses directly under alerts, tagged to the right service owners, and fit naturally into your incident.io/Rootly/FireHydrant flows.
Strategically, how should we think about “incident workflow” vs “investigation” over the next 12–24 months?
Short Answer: Treat incident.io, Rootly, and FireHydrant as your process backbone, and invest separately in investigation automation that reduces orientation cost and compounds learning from every incident.
Expanded Explanation:
The strategic trap is assuming better workflow tooling will automatically reduce time-to-root-cause. It won’t. It makes the work more organized, but the work is still the same: re-learning what each service does, what changed, what’s normal, and where the true root cause is hiding.
That orientation cost dominates modern incidents, especially when symptoms fire across multiple services and the root cause lives in a dependency, a deploy, or your Kubernetes/cloud control plane. Engineers bounce between Datadog dashboards, kubectl, AWS/GCP consoles, and docs, while the incident bot politely tracks their stress in a timeline.
The durable advantage is production memory and reasoning:
- Production memory that compounds:
- Semantic: understanding of your services, dependencies, owners, and normal behavior.
- Episodic: history of investigations and how past incidents were resolved.
- Procedural: debugging skills that can be reused across teams and incident types.
- Reasoning, not rules:
- Generic rule-based automation flattens out. You end up with brittle playbooks that don’t generalize.
- Hypothesis-driven systems like Cleric systematically eliminate wrong theories using real data, then show their work so humans can verify.
That’s why teams using Cleric report MTTR dropping to minutes, with >90% of investigations yielding actionable findings, and why companies like BlaBlaCar have let Cleric handle first-level incident response across thousands of production alerts. The incident bot keeps everyone in sync; the AI SRE teammate does the heavy investigative lifting.
Why It Matters:
- MTTR and reliability: Shorter time-to-root-cause means less customer impact and fewer escalation chains. Workflow tools help here indirectly; investigation automation hits it directly.
- Engineer focus and retention: When AI handles the repetitive, orientation-heavy investigations, engineers stay in deep-focus work more often. Less alert fatigue, more time building, and less reliance on “who knows this service” tribal knowledge.
Quick Recap
incident.io, Rootly, and FireHydrant are valuable for standardizing how you run incidents—declaring, coordinating, communicating, and learning. But they don’t run real investigations. Root cause still comes from humans stitching together logs, metrics, traces, Kubernetes, and cloud signals by hand.
If you want to materially cut MTTR and escape perpetual “orientation cost,” you need an investigation layer underneath your incident workflow: something like Cleric that plugs into your observability stack, forms and tests hypotheses, leverages production memory, and posts a clear, evidence-backed diagnosis directly in Slack. Treat workflow and investigation as complementary layers, not interchangeable features.