.avif)
.png){kind=icon}
.png){kind=icon}
In modern technology organizations, a central conflict has emerged: the soaring cost of cloud computing versus the critical need for system reliability. With the global cloud computing market projected to reach approximately $912.77 billion in 2025, the scale of this financial challenge is massive [1]. Data shows that 84% of organizations struggle to manage their cloud spend, often exceeding their budgets [2].
This problem often stems from the different objectives of two critical teams: Financial Operations (FinOps) and Site Reliability Engineering (SRE). FinOps focuses on controlling costs, while SRE is tasked with ensuring uptime and performance, a goal that can lead to over-provisioning resources as a safety measure. This article proposes a solution: Rootly's incident management automation platform can bridge the gap between FinOps and SRE, creating a system for data-driven alignment that yields both superior reliability and significant cost savings.
The High Cost of Misalignment: When FinOps and SRE Operate in Silos
When FinOps and SRE teams operate without a shared framework, the result is friction and financial waste. This disconnect is projected to cause $44.5 billion in cloud infrastructure waste in 2025, a quantifiable outcome of operational silos [5].
The common consequences of this misalignment include:
- Uncontrolled Cloud Wastage: SREs, often lacking cost-impact data, may over-provision infrastructure to guarantee reliability. FinOps, lacking the technical context, can't effectively challenge these decisions, leading to budget overruns.
- Delayed Incident Response: Without a shared understanding of priorities, teams can fall into cycles of finger-pointing during critical outages, which prolongs resolution times and increases the financial impact of downtime [6].
- Uninformed Decision-Making: FinOps may recommend cost-cutting measures that inadvertently increase system fragility, triggering expensive outages that negate any initial savings.
- Mounting Engineering Toil: SREs are forced to spend valuable time on manual, repetitive tasks during incidents, preventing them from focusing on strategic engineering that would improve efficiency and lower long-term costs.
How Rootly Automation Creates FinOps and SRE Alignment
Rootly provides the unifying platform that enables a more methodical approach to reliability and cost management. By centralizing all incident data, Rootly establishes a common ground, allowing teams to connect technical events with their direct financial outcomes.
A Single Source of Truth for Incidents
Rootly functions as a central command center, creating a single, immutable record for every incident. This eliminates chaotic communication and provides a unified dataset for all stakeholders. For SREs, this means streamlined outage coordination with rapid response power. For FinOps, Rootly’s automated timeline reconstruction, which captures every event chronologically, offers clear evidence of what happened, why it happened, and the precise engineering effort (cost) required for resolution.
Automating Workflows to Link Reliability Actions to Cost
Rootly's automation capabilities standardize the incident response process, turning chaotic reactions into controlled, repeatable workflows. Examples of automated workflows include:
- Creating dedicated Slack channels for focused communication
- Paging the correct on-call teams instantly
- Running diagnostic playbooks to gather initial data
- Updating internal and external status pages
This automation generates direct cost savings. By minimizing manual toil, Rootly frees up expensive engineering hours. This allows SREs to shift from reactive firefighting to proactive system improvements, helping to power the future of Autonomous SRE as a more scalable and cost-effective discipline.
Using Incident Properties to Tag and Track Financial Impact
Within Rootly, incident properties can be customized to categorize and track the financial dimension of any incident. This allows teams to build a quantitative model linking technical events to business costs.
Examples of custom properties for analysis:
cost-impact: high/medium/lowrevenue-affected: yes/nosla-penalty-risk: true
By using these properties, teams can run analyses that isolate the most expensive incident types. This ability to leverage incident properties for detailed analytics and to drive automations is fundamental to connecting reliability actions to their financial consequences.
Reducing Cloud Wastage Through Incident Automation with Rootly AI
Rootly's intelligent features provide the analytical engine to test hypotheses about cost drivers, helping to identify and reduce cloud waste with precision.
Using Rootly AI to Identify Cost-Impacting Incidents
Features like Incident Summarization and the "Ask Rootly AI" conversational assistant allow teams to analyze vast amounts of incident data without manual effort. Instead of digging through logs, a FinOps analyst can ask, "Show me all SEV-1 incidents related to our checkout service in the last quarter." This query rapidly correlates service downtime with potential revenue loss. This data-driven analysis helps prioritize which underlying issues should be fixed to produce the greatest financial return, defining Rootly's role in the rise of Autonomous SRE teams by accelerating learning and root cause discovery.
Rootly Total Reliability Cost Reduction: A Case Study
This hypothetical case study demonstrates the method in action, showing how Rootly can reduce the Total Reliability Cost.
Scenario: An e-commerce company observes frequent, costly incidents during peak sales periods. FinOps documents budget overruns from auto-scaling and engineer overtime, while SRE focuses exclusively on service restoration.
Solution: The company implements Rootly.
- They use custom properties to tag incidents by cost and the affected service (for example,
checkout-api,high-cost). - Automated workflows are deployed to reduce the Mean Time to Resolution (MTTR), directly shortening the duration of business impact.
- Rootly's post-incident analytics are used to analyze the collected data, revealing a causal link: a single faulty API is responsible for 60% of the costliest incidents.
Result: By fixing the root cause identified through Rootly's data, the company reduces peak-period incidents by 80%, lowers associated cloud spend by 35%, and reallocates SRE time to proactive optimization projects. This delivers a clear reduction in the company's Total Reliability Cost.
Metric
Before Rootly
After Rootly
Impact
Peak-Period Incidents
15 per month
3 per month
80% Reduction
Associated Cloud Spend
$100,000/month
$65,000/month
35% Savings
SRE Manual Toil
40 hours/week
5 hours/week
87.5% Reduction
Comparing Cost Efficiency: Rootly vs. Manual Operations
A comparative analysis of manual operations versus Rootly-powered automation highlights the clear value proposition.
Metric / Process
Manual Incident Management
With Rootly Automation
Visibility into Cost
Low; data is siloed and requires manual correlation.
High; cost-related data is tagged and centralized in one platform.
Engineering Toil
High; engineers spend hours on repetitive tasks.
Minimal; workflows are automated, freeing up SREs for high-value work.
Mean Time to Resolution (MTTR)
High; coordination is slow and prone to human error.
Drastically reduced; automation accelerates every step of the response.
Data-Driven Decisions
Difficult; based on anecdotes and incomplete data.
Easy; based on comprehensive analytics and automated timelines.
Cost of Incidents
High; prolonged downtime and extensive engineering hours.
Significantly Lower; faster resolution and less manual effort.
This methodology is validated by industry leaders who have integrated cost observability into their engineering workflows, with some achieving a 28% reduction in AWS spend while maintaining 99.99% reliability [7]. A primary challenge for FinOps is often encouraging engineers to act on cost data [8]. By providing clear, shared evidence, Rootly removes ambiguity and makes the case for action compelling.
Conclusion: Build a Culture of Cost-Aware Reliability with Rootly
Aligning FinOps and SRE is no longer optional—it's a critical discipline for financial health in a cloud-first world where 94% of enterprises use cloud services [3]. Traditional methods fail because they lack a shared dataset and a framework for objective analysis.
Rootly operationalizes this alignment by providing a unified platform to automate away costly toil and make the financial impact of reliability visible to everyone. By creating a shared language and analytical framework for FinOps and SRE, Rootly helps you build a culture of cost-aware reliability that improves both your bottom line and your system performance.
Book a demo today to see how Rootly can help you reduce cloud wastage and align your FinOps and SRE teams for maximum savings.