Heroku logs Monitoring
Monitor Heroku logs to track application performance, detect errors instantly, and optimize Heroku dyno resources for seamless scalability.
Monitor Heroku logs to troubleshoot application and platform-level issues
Analyze application log streams
Capture Heroku application logs emitted by dynos to understand runtime behavior and errors.
Track dyno lifecycle events
Monitor Heroku logs related to dyno restarts, crashes, and scaling events.
Detect routing errors
Analyze Heroku router logs to identify request timeouts, routing failures, and HTTP error responses.
Monitor platform warnings
Capture Heroku system log entries indicating memory limits, request queueing, and throttling.
Track build and release issues
Inspect Heroku build and release logs to identify failed deployments and configuration errors.
Observe add-on related errors
Monitor Heroku logs generated by add-ons to detect integration and connectivity issues.
Detect security-related events
Analyze logs for authentication failures and access-related warnings.
Correlate platform and app logs
Link Heroku platform logs with application logs for full execution context.
Get Actionable Insights From Your Heroku Logs in Atatus
Forward Heroku log streams into Atatus to surface performance signals such as request timing, dyno behavior, and resource trends in real time.
Unclear Request Latency From Logs
Raw log lines do not show how long requests take, and aggregating response time patterns from logs highlights where latency clusters form.
Throughput Variations Mask Performance Shifts
Plain log text hides traffic trends, while throughput metrics derived from logs reveal when spikes or dips align with slowdowns.
Dyno Resource Flags Do Not Stand Out
Memory and CPU warnings get buried in log streams, and extracting dyno resource signals surfaces when constraints affect performance.
Frequent Dyno Restarts Can Indicate Hidden Stress
Dyno restart events embedded in logs indicate instability, and surfacing crash frequency shows recurring patterns clearly.
Log Volume Spikes Can Hide Anomalies
Sudden increases in log volume can signal unusual behavior, and tracking volume and anomaly trends brings these changes into focus.
Why teams choose Atatus for Heroku logs monitoring
Native Heroku log ingestion
Atatus integrates directly with Heroku log streams without requiring additional agents.
Unified app and platform logs
View application, router, and system logs together for complete execution context.
Faster dyno issue detection
Quickly identify dyno crashes, restarts, and memory-related issues from logs.
Log-based alerting
Trigger alerts on critical Heroku log events impacting availability or performance.
Cross-service correlation
Correlate Heroku logs with downstream service logs for end-to-end visibility.
Production-ready retention
Store and analyze Heroku logs over time to identify recurring operational issues.
Unified Observability for Every Engineering Team
Atatus adapts to how engineering teams work across development, operations, and reliability.
Developers
Trace requests, debug errors, and identify performance issues at the code level with clear context.
DevOps
Track deployments, monitor infrastructure impact, and understand how releases affect application stability.
Release Engineer
Measure service health, latency, and error rates to maintain reliability and reduce production risk.
Frequently Asked Questions
Find answers to common questions about our platform