Rootly, Prometheus & Grafana: Best Practices for Faster MTTR

Engineering teams often rely on powerful but siloed observability tools like Prometheus and Grafana. While effective for monitoring, this fragmentation can create a high volume of alerts, leading to alert fatigue, context switching, and ultimately, a higher Mean Time To Resolution (MTTR). The key to overcoming this challenge isn't to replace these tools, but to unify them. Rootly acts as a central incident management hub, ingesting alerts from your entire stack and automating the response lifecycle. This turns passive monitoring into an active, streamlined process designed to resolve incidents faster.

The Challenge: From Powerful Monitoring to Alert Overload

A common Site Reliability Engineering (SRE) toolchain includes Prometheus for time-series data collection and alerting, paired with Grafana for rich data visualization and dashboards. This combination provides deep insights into system health. However, without a centralized system to manage the signals they produce, on-call engineers can be overwhelmed by a deluge of alerts.

Manually triaging these alerts, correlating them, and switching between different tools to gather context is inefficient and error-prone. This manual toil slows down the initial response and prolongs the incident. So, what’s the best way to use Rootly alongside Prometheus and Grafana? The solution lies in integrating these tools to automate the rote tasks of incident response, freeing up engineers to focus on diagnosis and resolution.

How Rootly Transforms Your Response by Integrating with Prometheus and Grafana

Rootly serves as the central command center for incident management, designed to ingest signals from any source and orchestrate the entire response process. By integrating directly with Prometheus's Alertmanager, Rootly turns a passive alert into an active response. When Prometheus detects an issue that meets a defined alerting rule, it sends the alert directly to Rootly, which immediately kicks off automated workflows [1].

This approach centralizes signals from various observability tools into a single pane of glass, eliminating the need for engineers to constantly switch contexts between different UIs. Instead of a chaotic, manual process, you get a repeatable, automated workflow. By consolidating alerts, Rootly helps organizations reduce procedural chaos and transform alerts into actionable responses, securing enterprise scale and improving MTTR.

Best Practices for an End-to-End Automated Workflow

Connecting your monitoring stack to Rootly is about more than just forwarding alerts. It's about building an automated system that manages the entire incident lifecycle.

1. Centralize Alerts with Webhooks

The first step is configuring Prometheus's Alertmanager to forward alerts to Rootly using a generic webhook. This is a critical step, but it's important to configure alert rules thoughtfully. If your alerts are too noisy or poorly defined in Prometheus, you risk simply moving the noise from one system to another. The goal is to send high-signal, actionable alerts to Rootly. Because Rootly can ingest data from any source via webhooks, you can create a unified pipeline for all your monitoring, logging, and tracing tools, ensuring no critical signal is ever missed.

2. Automate Incident Creation and Mobilization

Once an alert reaches Rootly, you can configure Workflows to automatically trigger a sequence of actions. This is where you codify your incident response process. Upon receiving a Prometheus alert, Rootly can:

• Create a dedicated Slack or Microsoft Teams channel for the incident.
• Page the correct on-call engineer using PagerDuty, Opsgenie, or another scheduling tool.
• Populate the incident channel with initial alert details, playbooks, and key links.

These automations are built around incident properties, which help categorize, prioritize, and route incidents to the right teams and individuals automatically.

3. Bring Visual Context into the Incident Channel

A common bottleneck in incident response is gathering diagnostic information. Responders often have to log into Grafana, find the right dashboard, and adjust time windows to understand what's happening.

Rootly workflows can eliminate this step. You can configure an automation to automatically query Grafana and pull relevant dashboard snapshots or panels directly into the incident's Slack channel. This provides responders with immediate visual context—graphs of latency, error rates, or resource saturation—right where the conversation is happening. This simple step saves valuable minutes and reduces the cognitive load on the team.

Unifying Observability: Rootly and OpenTelemetry

How does Rootly integrate with OpenTelemetry for unified observability? OpenTelemetry (OTEL) is a Cloud Native Computing Foundation (CNCF) project that standardizes how applications and services generate and collect telemetry data—traces, metrics, and logs. It offers a vendor-agnostic way to instrument code, but the sheer volume of data it produces can create its own "big data problem" if not managed properly [3].

While Rootly doesn't act as an observability backend itself, it serves as the action layer that sits on top of your observability data. Many observability platforms, like New Relic, Datadog, or Honeycomb, can act as an OTEL backend, receiving standardized trace data from various sources [4]. Your organization can configure its services to export traces and metrics to one of these OTEL-compatible backends [5].

From there, you configure that backend to send alerts to Rootly via webhooks. This is where Rootly's power shines: it ingests the alerts derived from your unified OTEL data and translates them into a consistent, automated incident response process. This allows you to leverage the rich, standardized data from OTEL while using Rootly to ensure every alert is handled quickly and effectively.

Closing the Loop: The Rootly Jira Integration

An incident isn't truly over when the immediate fire is out. Follow-up tasks, bug fixes, and retrospective action items are crucial for preventing recurrence. The Rootly Jira integration ensures this critical loop is closed automatically.

With Rootly's native Jira integration, you can automate the creation of Jira tickets for action items directly from the incident channel in Slack [6]. This ensures that all follow-up work is captured and tracked within your team's existing project management workflow, without manual data entry. The integration supports robust two-way synchronization, allowing you to sync data from Rootly to Jira and also from Jira back to Rootly [7][8], keeping status and ownership consistent across both platforms [1] [2].

This is just one of the dozens of integrations Rootly offers to help create a seamless, end-to-end incident management process that connects with the tools your teams already use every day.

Conclusion: Achieve Faster MTTR with a Fully Integrated System

By centralizing Prometheus alerts in Rootly, automating incident creation, pulling in Grafana dashboards for context, and managing follow-up work with the Jira integration, teams can build a powerful, end-to-end incident management system. This integrated approach transforms your response from a reactive, fragmented process into a proactive, automated, and highly efficient one. The result is a systematic reduction in cognitive load on engineers and a measurable improvement in your Mean Time To Resolution.

Ready to connect your tools and automate your response? Explore Rootly's integrations and see how easy it is to get started, for example, by following a guide like our New Relic installation instructions.

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