Software as a Service (SaaS) is the foundation for so many organizations providing software solutions at scale. These solutions are essential for consumer data storage, faster computation, enabling application-level collaboration, automating system procedures, and reproducing results regularly. To solve a vast variety of problems in the technology industry, SaaS services are becoming more and more complex, which adds an overhead of maintenance. It is imperative that the SaaS systems are performing optimally, safe, reliable, and available. Monitoring platforms transformed by artificial intelligence (AI) are crucial to do so.
Traditional monitoring technologies are no longer enough in today’s SaaS systems due to their sheer volume and complexity. Simple, monolithic applications housed in a single data center are no longer what SaaS systems are. These days, they are frequently constructed with microservices, dispersed over several cloud environments, and dynamically scaled to accommodate changing demand. Because of this amount of complexity, it is getting harder and harder to maintain system stability using just manual monitoring methods. The upcoming SaaS monitoring solutions will be powered by artificial intelligence (AI), which is able to automate, forecast, and optimize performance.
Why SaaS Monitoring Needs Artificial Intelligence
Older monitoring systems mostly rely on human intervention, cutoff points, and pre-established parameters.While this approach worked well for simpler systems, modern SaaS platforms generate enormous amounts of data from several services, containers, and APIs. Manually setting thresholds for every measure, going over logs, and resolving issues can be very demanding, even for the most seasoned teams. Surveillance systems using AI examine the system activity in real time, learn the trends, and identify anomalies instead of just basing it on predefined threshold levels. This shift in technique helps the service analyze real-time data and detect any small change in behavior. Such proactive monitoring optimizes the performance and user experience of the system.
Key Benefits of AI in SaaS Monitoring
1. Proactive Issue Detection and Resolution
The way a SaaS monitoring system works is based on predefined thresholds triggered in the form of alarms, monitors, or events. The escalation engineers only find out about the issue when the systems is already broken. The most significant advantages of AI in SaaS monitoring is the proactive anomaly detection. The systems, due to being trained with so many sets of data, do not need to wait for the predefined thresholds to be hit. The engineers will be notified early to spot these issues before they disrupt production software. The issues could be anything from security breaches, intrusions, bottlenecks, throttling, etc. Artificial intelligence can significantly reduce the downtime and outages of SaaS applications.
Datadog and New Relic are good examples of companies that leverage AI-driven monitoring to detect anomalies in system behavior, raise alerts without the need for human intervention. These platforms can predict when a spike in usage may cause performance degradation, and allow the engineering team to scale the application such that there is no availability issue.
2. Automating Root Cause Analysis
In a complex SaaS setup, determining the root cause of an issue can be a nightmare for engineers. With hundreds of interconnected microservices and APIs in play, the legacy approach to sifting through system logs can become inefficient. Using AI for root cause analysis (RCA) helps reduce the time to get critical data that is buried in meters, logs, and metrics, which can now be utilized much more efficiently and rapidly. The machine learning algorithms are able to connect the dots through different components of SaaS architecture and can identify the exact service/process that is not functioning properly. This ability is a true time saver when it comes to finding the error and mitigating issues.
PagerDuty and Splunk have been implementing AI to not only detect issues but also automate the root cause analysis process. This means that engineers no longer have to spend hours digging into logs to find the root cause for an issue.
3. Dynamic Scaling and Resource Optimization
AI isn’t just about detecting and fixing problems; it can also optimize resource management in SaaS platforms. One such challenge is why pay for a resource if it is not being used. The “pay as you go” model is very common in the industry today. An AI monitoring system can enhance the infrastructure by reducing any unnecessary resource consumption.
Monitoring tools that use AI can change the resources needed by a service in real time to make sure the performance is optimal. This can be scaling up the resources during production deployment, feature launch, or product release to handle the incoming spike in traffic or reducing the resources allocated during low traffic situations. The efficiency of the solution will depend on the AI prediction model.
4. Improved Security Through Anomaly Detection
Another focus for software engineers using AI is security. From simple vulnerability in code to data breaches, the security of complex SaaS applications is a constant battle. Similar to monitoring real time data such as alarms, traditional security systems depend on predefined signatures and vulnerabilities reported in OWASP. However, that is not enough when it comes to such complex platforms, especially handling health data, personal privacy data, etc.
AI powered security systems can predict anomalies and detect any deviation of behavior from what is expected. The critical work of engineers is to evolve the threat detection patterns, train the AI models against large datasets, including the incorrect ones. AI can also help to detect IP breaches, intrusion, prevent DDOS by automatically by rejecting connections and many other evolving threats. Darktrace is a company that leverages AI to monitor cloud based systems by looking at the system behavior over time and subtle deviations to signal a breach or malicious activity.
5. Predictive Maintenance and Downtime Prevention
One of the biggest issues that plague the SaaS industry is “downtime.”. Downtime is also bad for lost revenue, lost data, bad customer experience, and loss of customer trust. If artificial intelligence is trained with enough data, it can help detect potential downtime and notify the escalation engineers to mitigate the issue early.
Platforms that use AI can leverage historical data and pattern recognition models to identify the weakest link in the system in a given time. Engineering teams can take preemptive measures based on the results of the AI scans to perform maintenance, upgrade databases, or fix a bug without causing any customer impact.
The Road Ahead: AI’s Expanding Role in SaaS Monitoring
AI is revolutionizing SaaS monitoring by changing the way companies detect, diagnose, and resolve issues in their cloud environments. We are moving away from static, reactive-based approaches to proactive issue detection to dynamic scaling and predictive maintenance. AI is evolving constantly and furthering the cloud computing industry by reducing costs, optimizing the services, and improving overall application health.
Similar to the transistor industry in the 2000s, we are seeing AI continue to expand to various industries and become even more sophisticated when it comes to detection, predicting user behavior, and managing cloud infrastructure. I see a future where all SaaS platforms will be AI monitored without the need of engineers and escalation managers. The security or compliance team will work on the action items after the incident while the detection and mitigation are taken care of by artificial intelligence.
Though this is possible, we are quite some time away from that day. AI has a lot more potential, and most of the development has been around the B2C market and some internal tooling in the industry. We are yet to see AI company solutions that are at scale solving business and engineering problems.
Reference
1) Microsoft Azure – AI and Predictive Analytics
Microsoft Azure integrates AI into its monitoring services to optimize resource allocation and detect anomalies. Learn more about their monitoring solutions powered by AI here:
Azure AI for Monitoring and Management
2) The New Era Of SaaS Is Here, And AI Is Leading The Charge
AI’s growing role in Saas operations (AIOps) is explored in depth in reports by Forbes, highlighting how businesses leverage AI to outperform other competitors in service reliability and optimize resource allocation.
3) Cloud Monitoring Tools to Ensure Optimal Cloud Performance and Drive Business Success
Digital Ocean talks about how managing cloud-based infrastructure is integral to business operations. Effective use of cloud infrastructure monitoring tools, both automated and manual, enables businesses to gain valuable insights into their application performance.
Cloud Monitoring Tools to Ensure Optimal Cloud Performance and Drive Business Success
4) Gartner – AI in Cloud Monitoring
Gartner’s research explores the trend of AI was found to be more common than other solutions like graph techniques, optimization algorithms, rule-based systems, natural language processing and other types of machine learning.
