AI-based monitoring and anomaly detection is the key to ensuring that businesses can keep pace with the high level of service required for mission-critical applications. Early, contextual detection is a basic requirement for speedy resolution. AI-based monitoring creates more visibility and provides the agility needed to mitigate the outages, blackouts, glitches and issues that do and will happen. 

Compared to manual, dashboard-based monitoring systems, AI/ML-based solutions enable unprecedented scale, accuracy and speed. They empower today’s telecom engineers to handle, manage, optimize, monitor and troubleshoot multi-technology and multi-vendor networks. Machine learning enables CSPs to move from reactive problem solving to proactive monitoring and learn more about what is happening across their networks before any minor issues escalate into bigger problems. 

That’s why smart communications service providers (CSPs) are investing in AI. By cutting time to detection, reducing false alarms and alert storms, and providing the context for the shortest time to resolution, AI solutions enable CSPs to ensure availability and reliability, deliver more business value, and stay ahead of the competition. 

But as CSPs realize the benefits they can achieve from autonomous monitoring compared to manual/static methods, they’re faced with the immediate question: build our own system — or buy one?


Building your own monitoring solution

The build option poses multiple conceptual, technical, and resource challenges, and is therefore usually only viable for extremely large, innovative companies with a dedicated team of AI researchers and developers. Depending on the robustness of the solution the CSP chooses to pursue, some build scenarios could take more than four years to develop, particularly for large, complex, and changing monitoring needs.

To achieve the capacity and prowess of a fully matured autonomous monitoring platform, there are three solution maturity levels to be pursued one on top of the other: Basic, Intermediate, and Advanced.

CSPs opting to build their own solutions need to understand the costs, staffing challenges, and potential pitfalls to ensure that any home-grown solution not only serves its intended purpose, but also provides a comparable return on investment. While the promise of open-source AI-based solutions is great, so are the challenges associated with implementing them at scale, and, especially, of moving beyond the proof of concept to production – an endeavor which only a fraction of companies building their own platforms successfully achieve. 


Buying options for AI-based network monitoring

Monitoring solutions differ in the area of the business they are designed to monitor. The three main monitoring categories are: 

Enterprise Data Monitoring Platforms

A data platform is a complete solution for ingesting, processing, analyzing and presenting the data generated by the systems, processes and infrastructures of modern digital organizations. These solutions offer network, infrastructure, IT, APM and Security and Information Event Management (SIEM) monitoring. 

Network Automation Solutions

AI development frameworks for mobile networks with network visibility, anomaly detection, predictive network intelligence, and process automation utilities for network operations and customer care. These solutions are typically siloed to specific network types/layers (few data sources out of the box), have limited usability (use cases), and usually require on-premise service delivery and platform installation. 

Autonomous Network Monitoring 

Autonomous network monitoring is the brain on top of existing OSS tools, giving CSPs a holistic view across domains (multiple network types, layers and services) for real time detection of service-impacting incidents. These solutions aggregate inputs from network functions and logic such as fault management KPIs, xDRs, OSS/BSS tools, performance management KPIs, probe feeds, counters and alerts for all network types and layers into one centralized analytics platform to analyze 100% of data streams and metrics, regardless of the business’s original data architecture and silos.


There is a wide range of monitoring solutions on the market, and adoption is often correlated with the organization’s maturity level. IT monitoring is implemented very early on, and APM usually follows closely. Mature organizations require the monitoring abilities that only Autonomous Business Monitoring can deliver by monitoring and analyzing 100% of the business’s data, including complex signals influenced by volatile parameters such as seasonality and human behavior. 


Build vs. buy comparison

When viewing the build vs. buy options for autonomous monitoring side by side, some key points come to light:

The complexity of autonomous monitoring makes it especially hard to build. That’s why generally, build scenarios can only be applicable for very large, innovative CSPs with dedicated R&D and dev teams.

The complexity of autonomous monitoring makes it especially expensive to build and maintain. Estimates show that developing and maintaining a data-driven enterprise software application can cost upwards of $4 million USD per year. Given that real-time monitoring is at the cutting edge of computer science, your project might greatly exceed that figure.

Building your own solution? Expect an exceedingly long time to value. The duration of building an anomaly detection and monitoring solution is ~12mo for a basic solution, ~24mo for an intermediate solution, and up to 48mo for an advanced solution.

You will struggle to achieve the scale and performance of best of breed dedicated solutions like Anodot. Basic home grown solutions usually struggle to scale with the business, as the number of metrics to monitor multiplies. More mature home grown solutions still struggle to achieve the performance of dedicated solutions built on the cutting edge of monitoring science, and will usually provide less accurate detection that will inevitably result in longer time to detection and resolution and more noise. 

Most home grown AI solutions fail.  According to Gartner, 85% of AI projects ultimately fail to deliver on their intended promises to business. High failure rates of bringing AI to production and keeping it on the rails hinge on multiple factors. Most prominent are the inherent complexity of AI solutions, multi-faceted data challenges, and production challenges related to both maintaining model confidence and scaling the solution. 

With autonomous solutions, customization comes with the territory. While customization is a key driver towards the “build” route, it is actually better achieved by the advanced machine learning algorithms built into mature solutions. This is doubly true in case of Unsupervised ML systems, that instantly adapt to any data architecture, business logic and signal type out there. 

Fast and seamless integration and implementation level the playing field for time to value. For many companies, the typical exceedingly long implementation time of monitoring solutions is a trigger for building their own. In the case of some autonomous monitoring solutions this rejection is irrelevant, since they can be up and running within weeks. 


Learn more

To start with AI-based monitoring fast it’s critical to accelerate time to value by reducing prolonged development and implementation times. In the case of monitoring solutions, reducing time to value works in two channels: less resources are spent on building a solution, while implementing a monitoring solution without delay dramatically cuts costs on faster detection and resolution of incidents that are already happening right now.  

To learn more about the approach and solution that are right for you, download The Telecom Executive’s Guide to AI-based Network Monitoring.

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Written by Anodot

Anodot leads in Autonomous Business Monitoring, offering real-time incident detection and innovative cloud cost management solutions with a primary focus on partnerships and MSP collaboration. Our machine learning platform not only identifies business incidents promptly but also optimizes cloud resources, reducing waste. By reducing alert noise by up to 95 percent and slashing time to detection by as much as 80 percent, Anodot has helped customers recover millions in time and revenue.

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