Node.js and JVM services in production leak memory silently for days before OOM-killing, and profiling tools cannot safely diagnose the cause on live systems

Memory leaks in garbage-collected languages (JavaScript/Node.js, Java, C#) manifest as slow, linear RAM growth over hours or days, invisible to standard health checks until the process hits container memory limits and gets OOM-killed. A documented case showed a Node.js analytics service consuming 500MB more RAM every hour, crashing within 8 hours. So what? The service restarts, causing 10-30 seconds of downtime and dropping in-flight requests, which for payment processing or real-time bidding systems means direct revenue loss per incident. So what? SRE teams add memory headroom (provisioning 2-4x the steady-state requirement), wasting cloud spend of $50K-$200K annually per service at scale. So what? When they try to diagnose the root cause, heap dump analysis on a JVM service causes a stop-the-world pause of 10-60 seconds (killing availability), produces multi-gigabyte dump files that fill disk, and only captures a snapshot that may not contain the leaking reference. So what? Teams resort to 'restart and hope' cron jobs every 4-6 hours instead of fixing the root cause, masking the problem and accumulating technical debt. So what? The leak eventually worsens after a code change, the restart window shortens, and a 3am page wakes up an on-call engineer who spends 6-12 hours bisecting commits because no profiling data exists from the actual failure. This persists because production-safe profiling (JFR, async-profiler) has only 2-3% overhead but captures CPU-centric data, not allocation-site tracking; full allocation profiling has 10x+ overhead and is unusable in production; and most teams lack the specialized knowledge to interpret heap histograms even when they get them.