People ask me how I can quote such high cost savings when talking about AlvaLinks CloudRider’s AI-powered observability. Usually I dive into technical details, but let me explain this in terms that every IT and operations person will recognize – because they’ve lived it.

Everyone knows MTTR (Mean Time To Resolution) – that critical metric showing how fast you can identify, address, fix and test solutions when your network goes down. Organizations obsess over keeping this number low, but most don’t understand the true cost of the time involved. After decades in network operations, I’ve seen this pattern repeat countless times, and the financial impact is staggering.

The MTTR Process – And Where Time Disappears

Here’s what actually happens during a network incident, step by step:

Step 1: Event Detection – The time it takes to identify there’s even an issue. Could be monitoring tools, alarms, logs, or that dreaded phone call from operations.

Step 2: Event Acknowledgment – After detecting the event, operations teams need to acknowledge it, confirm the problem, identify next steps, assign engineers, call service providers, alert management, and set up resources.

Step 3: Investigation and Diagnosis – Going through logs, alarms, and metrics trying to diagnose the problem. Attempting to replicate the issue in production or lab environments. Correlating data across different systems to identify the root cause.

Step 4: Repair – Once you understand the root cause, implement the fix: equipment replacement, configuration changes, applying patches, or firmware upgrades.

Step 5: Testing – Verify everything functions correctly, ensure no other issues exist, and confirm the network operates as expected.

Step 6: Back to Normal – Notify stakeholders and management that the issue is resolved.

Most organizations have Site Reliability Engineers (SREs) leading this process. But here’s where productivity vanishes and costs explode:

Why Steps 1-3 Become Productivity Killers

Event Detection – The Sampling Problem

Modern monitoring tools often miss short-lived events because of sampling resolution limitations. If you’re trying to detect a 100-millisecond network event with monitoring tools that sample every minute, the probability of detection is extremely low. This isn’t a theoretical problem – it’s why network issues that impact video quality, VoIP calls, or real-time applications often go undetected for extended periods.

The result? Teams know something is wrong (users are complaining), but can’t pinpoint when or where the problem occurs. Event detection can take a day or more while engineers manually investigate inconsistent symptoms.

Event Acknowledgment – The Resource Coordination Challenge

Many organizations require multiple occurrences of an event before committing resources to investigation. When events are intermittent or produce different alarms each time, it’s challenging to recognize them as related incidents.

Then comes resource allocation. Everyone is busy, consultants need to be contracted, vendors need to be engaged, and management needs to be briefed. This coordination often takes days to a week, during which the underlying problem may worsen or impact more systems.

Investigation and Diagnosis – The Real Time Sink

This is where costs explode. Investigation typically involves teams of internal and external technical experts doing:

• Log Review: Combing through thousands of log entries across multiple systems
• Metric Correlation: Attempting to correlate performance data from different tools that use different time stamps and granularities
• Configuration Validation: Checking settings across network devices, applications, and cloud services
• Workflow Analysis: Understanding how different components interact and where failures might propagate
• Problem replication test – once a possible problem is identified – test it to rule out or identify it as been the true root cause.
• If replication fails – repeat the investigation process to find the next possible problem

The challenge? Every stakeholder has their own monitoring tools, their own data, and their own theories. Network teams point to application performance. Application teams point to infrastructure. Cloud providers insist their services are operating normally. Equipment vendors suggest the problem is elsewhere.

Without proper correlation capabilities, teams spend weeks testing theories, ruling out potential causes, and coordinating between different technical domains. Knowledge gaps often require bringing in specialized consultants. Investigation may take weeks with extended resources from multiple organizations.

The Financial Reality

Let’s quantify this with moderate assumptions. Assuming a technical person costs $750 per day (including overhead, benefits, and indirect costs):

Typical Incident Breakdown:

• Detection: 1 day × 1 person = $750
• Acknowledgment: 4 days × 1.5 persons = $4,500
• Investigation: 30 days × 6 technical experts = $135,000
• Repair: 3 days × 2 technical persons = $4,500
• Testing: 3 days × 1 person = $2,250

Total: 196 working days = $147,000

Per incident. And this doesn’t include business impact, SLA penalties, customer churn, or opportunity costs.

Ask yourself: How many such events does your organization face each year?

How AlvaLinks Changes the Economics

AlvaLinks CloudRider transforms the most expensive phases of MTTR through proactive observability:

Revolutionizing Detection Instead of waiting for sampling-based tools to catch intermittent events, CloudRider actively injects test traffic into your network, continuously measuring performance at millisecond resolution. Short-lived events that traditional tools miss are immediately visible. Detection time: Hours instead of days.

Eliminating Investigation Delays By continuously correlating performance data across network paths and providing AI-enhanced analysis, CloudRider delivers root-cause insights with supporting evidence. Instead of weeks of finger-pointing between teams, engineers get clear, actionable information. Investigation time: Hours to days instead of weeks.

Immediate Acknowledgment When you have clear correlation and evidence of the problem source, resource allocation becomes straightforward. No more debates about whether the issue is “real” or where to focus efforts.

The New Economics:

• Detection: 0.5 days
• Acknowledgment: 0.1 days
• Investigation: 1 day
• Repair & Testing: 6 days (unchanged)

Total: 7.6 days = $5,700

That’s a 96% reduction in incident cost, while freeing your best engineers to focus on proactive improvements instead of reactive firefighting.

Why This Matters Now

As networks become more complex with hybrid cloud deployments, microservices architectures, and distributed applications, the traditional approach to incident response becomes increasingly expensive and ineffective. The cost of reactive troubleshooting grows exponentially with network complexity.

Proactive observability isn’t just about faster incident response – it’s about fundamentally changing the economics of network operations. When you can detect, diagnose, and resolve issues in hours instead of weeks, you transform network management from a cost center into a competitive advantage.

This is why our customers see dramatic ROI from the disruption we’ve made in network observability – not just in technology capabilities, but in operational economics.

The question isn’t whether you can afford to implement proactive observability. It’s whether you can afford not to.