Modern applications are intricate systems, built from interconnected components that must balance user demand, security risks and infrastructure limits. When one piece falters, the ripple effects can quickly escalate into downtime, lost revenue and frustrated customers.Fortunately, failures rarely arrive without warning. Subtle signals—whether in system metrics, logs or shifts in user behavior—often surface hours or even days in advance. By learning to detect and act on these early warning signs, tech teams can intervene before small glitches snowball into full-blown failures. Below, members of Forbes Technology Council share the indicators they rely on to spot trouble before it takes applications offline. 1. User Behavior Shifts One critical signal is a sudden shift in user behavior patterns, such as unusual access times or transaction flows. These often hint at stress points or hidden vulnerabilities in the application. Viewing such anomalies not just as security red flags but as early indicators of resilience gaps helps teams act before failure occurs. – Senthil Muthu, SITCA Pty Ltd. 2. Database Waits And Thread Accumulation In systems with relational databases, impending outages are often signaled by high waits, memory page life approaching zero, or thread accumulation caused by locks, deadlocks or IO-related waits. Thresholds for these events vary based on database host characteristics, such as CPU, memory and IO, as well as configuration settings like maximum query parallelism. – Ronald Nelson, Shift Technology 3. Google Search Queries About Outages When Google search queries like “is X down?” start spiking, you know something’s off. Teams at Google would set up alerts on these search queries to monitor applications as black boxes—a type of monitoring where you don’t look inside the system at its logs or metrics, but instead treat it as a “black box” and infer its health by observing its external behavior. – Lalit Kundu, Delty 4. Open And Overdue AppSec Vulnerabilities Continually monitoring AppSec metrics—such as the number of open AppSec vulnerabilities and the percentage of overdue AppSec vulnerabilities—is critical. Elevated levels in these metrics could indicate increased risk exposure to malicious attacks that may lead not only to application failure, but also to network breaches, which could cause significant organizational and reputational damage. – Sivan Tehila, Onyxia Cyber 5. Memory Growth And Garbage Collection Decline The most predictive signal is a gradual increase in memory consumption, coupled with declining garbage collection efficiency. When heap utilization climbs higher than 85% but recovery drops below 20%, application failure is imminent within two to four hours. This pattern appears a few hours before crashes, giving teams critical response time to implement auto-scaling, restart services or trigger failover protocols. – Rishi Gupta, Infosys DX Consulting 6. Helpdesk Ticket Surges A surge in real-time helpdesk tickets mentioning odd, seemingly unrelated errors—especially ones linked to third-party integrations—often foreshadows cascading application failures. By connecting ticket trends to system events, teams can uncover hidden issues faster than they can using automated metrics alone, preventing outages before they reach scale. – Lindsey Witmer Collins, WLCM “Welcome” App Studio 7. Rising Response Latency One signal is increasing response latency. Growing delays between a user request and a system’s response can act as an early signal of an application’s impending failure and highlight congested network loads. System memory limits and high traffic volumes can cause slow system responses and eventually crash applications due to a constant overburdening of the system. – Daniel Keller, InFlux Technologies Limited (FLUX) 8. Unusual User Drop-Off Rates One useful signal is unusual user drop-offs. If many users suddenly leave an app or stop a process halfway, it often signals slow speed, hidden errors or system strain. Tracking this early helps teams find the root problem and fix it before the app fully fails or crashes. – Jay Krishnan, NAIB IT Consultancy Solutions WLL 9. Spikes In Database Connection Pool Exhaustion Watch for sudden spikes in database connection pool exhaustion, even when overall traffic appears normal. This often signals memory leaks or inefficient queries that can cascade into complete application failure within hours. Unlike obvious metrics such as CPU or memory, connection pool saturation is subtle but dangerous—applications may appear healthy while slowly strangling themselves. – Harshith Vaddiparthy, JustPaid 10. Error Variance In Logs One strong predictor of application failure is variance in error logs. Even if average error rates look stable, sudden changes in the distribution of errors can signal that the system is destabilizing. Monitoring this variance helps teams catch issues before outages occur. – Vivek Venkatesan, The Vanguard Group 11. Retry Loops A subtle warning sign of application failure is when systems keep retrying the same task over and over. It may look like normal traffic, but it’s often a signal that something deeper is stuck. Spotting and fixing these early retry loops can prevent a small hiccup from snowballing into a full outage. – Rishi Kumar, MatchingFit 12. Slow Degradation Of A Key Metric The most telling signal is the slow, linear degradation of a key metric—the “death by a thousand cuts.” We once missed a memory leak that grew by just 0.1% daily, leading to a massive crash three months later. Don’t just monitor static thresholds; track the rate of change. If your P99 latency creeps up by 1ms every day for a week, that’s your real canary in the coal mine. – Nikhil Jathar, AvanSaber Technologies 13. High Disk IOPS Usage One of the most important metrics to monitor is disk IOPS usage. It’s often overlooked, but tracking it can help predict future failures by showing the load on storage. Keeping historical data reveals when spikes occur and helps identify their root cause. – Osmany Barrinat, SecureNet MSP 14. Non-Critical Log Anomalies Non-critical log anomalies reveal system failures before they escalate. While teams often focus on fatal errors, early warning signs hide in “noise”—clustering timeouts, retry patterns or dependency warnings. Sudden spikes in “retry succeeded” messages or benign alerts often signal hidden bottlenecks. Anomaly detection on these logs predicts issues hours early. – Mohit Menghnani, Twilio 15. Memory Leaks And Thread Contention Watch