Ruby Application Performance Monitoring
Get end-to-end visibility into your Ruby performance with application monitoring tools. Gain insightful metrics on performance bottlenecks with Ruby monitoring to optimize your application.
The Hidden Cost of Ruby in Production
Blind runtime behavior
When Ruby apps slow under real traffic, teams lack a reliable view into what the runtime is actually doing. Assumptions replace evidence, and fixes become guesswork.
Unclear failure paths
Errors surface without context on what triggered them. Engineers see symptoms but not the execution path that led there.
Slow Root Causes
Incidents demand fast answers, yet teams burn hours stitching clues across systems. The delay increases blast radius and operational stress.
Scaling side effects
What works at low traffic breaks subtly at scale. Latency compounds, queues back up, and the root issue hides behind secondary failures.
Noisy signals
Production generates massive amounts of data, but little of it explains why things broke. Teams struggle to separate signal from background noise.
Environment drift
Behavior differs across staging, production, and regions. Bugs reproduce only under specific conditions that are hard to isolate.
Ownership confusion
Multiple teams touch the same Ruby services. When incidents happen, responsibility is unclear and response slows down.
Reactive firefighting
Without continuous clarity, teams operate in crisis mode. Engineering time shifts from building to chasing production issues.
Stop Ruby Performance Blind Spots From Slipping Into Production
Get real-time visibility into slow routes, slow queries, third-party call delays, and errors that customer traffic exposes first.
Slow Ruby Requests Without Clear Breakdowns
Rails or Sinatra routes can be slow but you don't know whether the delay is in code, DB, or downstream calls without detailed traces.
Database Query Delays Hidden in Full Responses
Unoptimized SQL or repeated fetches pad response times, yet they remain hard to spot without query-level timing.
External API Calls Dragging User Experience
Third-party service timeouts or delays inflate request latency, but without tracing, you can't see where or why.
Errors Lacking Execution Context
Exceptions and stack traces show the line, but missing correlated request data slows down root-cause analysis.
Logs That Don't Tie to Request Traces
Separate logs force manual correlation; combining logs with traces reveals exactly what happened during a request.
How Engineering Teams Regain Control With Atatus?
Teams choose Atatus for predictable observability of Ruby services in distributed environments.
Fast understanding
Teams quickly grasp what is happening in production without long setup cycles or steep learning curves.
Shared context
Platform, SRE, and backend engineers work from the same source of truth, reducing handoffs and misalignment.
Developer trust
Engineers trust what they see because the system reflects real execution, not sampled guesses or partial views.
Low friction
Adoption does not disrupt existing workflows. Teams get value without reorganizing how they build or operate services.
Incident confidence
During outages, teams move with certainty instead of speculation. Decisions are backed by clear production evidence.
Scale readiness
As traffic and complexity grow, teams retain control instead of losing visibility into system behavior.
Operational clarity
Production behavior becomes understandable rather than mysterious, even under load or failure conditions.
Team autonomy
Engineers diagnose and resolve issues independently without relying on tribal knowledge or senior-only expertise.
Long term control
Teams choose Atatus to stay ahead of production complexity instead of reacting to it after problems surface.
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