Rollbar captures exceptions, groups them by fingerprint, and links them to a session replay. Atatus captures the same errors and connects them to the distributed trace, the slow database query, the infrastructure state, and the logs that explain why it happened at the system level.
Rollbar captures what broke in your code. It does not show how long your API endpoints were taking before the error, which database query was running slow, or whether a server was under resource pressure. These are the questions that matter most during an active incident.
Rollbar shows you the stack trace, the occurrence count, and the session replay for a browser error. What it cannot show is whether that error was caused by a timed-out third-party API call, an overloaded database connection pool, or a memory-constrained container. Atatus captures the full distributed trace for every request, so the error and its system-level cause are visible in the same view.
Before errors start firing, your p95 response time climbs, a specific database query starts taking 10x longer, or a downstream service starts timing out. Rollbar has no visibility into any of these signals. Atatus monitors endpoint latency, throughput, and query-level performance continuously, so degradations are visible before they become exceptions. The error and the performance timeline that preceded it are correlated in one place.
A pod restart, a CPU spike, or a memory limit hit on a container can cause a cascade of application errors. Rollbar surfaces the errors. It has no data about the infrastructure state at the time they occurred. Atatus monitors server CPU, memory, disk I/O, container health, and Kubernetes workloads natively. When an error fires, the infrastructure metrics from the same time window are already in the incident view.
If your investigation consistently ends with "the error is here, but I need another tool to find out why", these are the scenarios where Atatus changes that.
Every exception in Atatus is linked to the end-to-end trace for that request. You see every service hop, every database call, and every external API request that was part of the failing transaction.
Atatus profiles database queries per operation. N+1 query patterns, slow queries above threshold, and connection pool exhaustion are detected and linked to the traces where that overhead appears.
Atatus measures Core Web Vitals (LCP, CLS, INP, FCP, TTFB) from actual browser sessions, tracks JavaScript error impact per session, and links frontend performance to backend traces by request ID.
When an error fires, Atatus shows the server CPU, memory, and container restart events from the same time window. You identify whether the root cause was in code or in the infrastructure it was running on.
Atatus ingests application and infrastructure logs and correlates them to traces and errors by request ID. The relevant log output surfaces in the error context automatically, with full-text search available.
Atatus runs HTTP, HTTPS, TCP, and synthetic transaction checks from global probe locations. Uptime events, performance data, and application errors all feed into the same alert and investigation workflow.
A precise breakdown of what each platform covers, where each one stops, and what sets them apart.
Real-time error and exception tracking with full stack trace, request parameters, environment details, and impacted user count
Source map support for JavaScript errors — maps minified stack traces to original source file, line, and column
Session replay linked to browser errors with full DOM snapshot, event timeline, and network request log
Every error is linked to the full distributed trace for that request, showing the server-side cause in the same view
Deploy tracking with pre/post deployment comparison of error rate, response time, and throughput
Intelligent grouping, alert routing, and noise suppression based on error fingerprint and occurrence threshold
Real-time error tracking with full stack trace, occurrence grouping by fingerprint, and affected user count
Source map support for JavaScript errors with automatic source map upload during CI/CD
Session replay triggered on errors, configurable sampling, DOM snapshots, click/input/navigation timeline, and console output
No distributed tracing. Rollbar error context does not include a backend trace. The server-side cause requires a separate APM tool.
Deploy tracking with release markers linked to error occurrence history
Automatic error grouping, smart notifications, and Jira/Asana/GitHub issue creation from errors
“We used Rollbar for error tracking and had a separate tool for APM. Every time an error spiked, we opened Rollbar to find the exception, then opened the APM tool to find the slow endpoint, then searched our log aggregator to find the relevant lines. With Atatus, the error links directly to the trace, and the trace links to the logs. Three tabs became one. Our on-call investigation time dropped from 20 minutes to under 5.”
Direct answers based on how both platforms actually work.
Yes. Atatus captures exceptions across JavaScript, Node.js, Python, Ruby, PHP, Java, .NET, Go, iOS, and Android with full stack trace, source map resolution, occurrence grouping, impacted user count, and deploy association. Session replay triggered on errors is included. The key difference is that every error in Atatus is linked to the distributed trace for that request, so the server-side cause is visible without switching tools.
Atatus does not price by occurrence count. Pricing is based on infrastructure footprint and data volume. This is meaningful for teams with high-traffic applications where a single deployment incident can generate thousands of error occurrences in a short window. Per-occurrence billing scales unpredictably under load. Atatus pricing stays predictable as error volume spikes.
Yes. Session replay in Atatus is accessible directly from the error detail view, similar to how Rollbar surfaces it. The difference is that Atatus session replay is also linked to the full backend APM trace by request ID. You see the browser interaction, the network requests, and the server-side trace that processed those requests, all accessible from the same error context.
Yes. Atatus ships error tracking, APM, distributed tracing, log management, infrastructure monitoring, and uptime checks natively in one platform. Teams running Rollbar alongside a separate APM tool and a log aggregator can consolidate all three into Atatus. Beyond cost simplification, the value is that the data is correlated natively. An error links to its trace. A trace links to its logs. No manual timestamp matching across tools.
Error tracking typically takes under 30 minutes for any supported language. Full APM instrumentation for auto-supported runtimes (Node.js, Python, Java, Ruby, PHP, .NET) typically completes in under a day with minimal code changes. Migration from Rollbar does not require rearchitecting your error handling logic. The Atatus agent wraps at the framework level and begins collecting both error and performance data from the same integration point.
Yes. Atatus is OpenTelemetry compatible. If your services already emit OpenTelemetry traces, those traces can be ingested directly into Atatus without changing your instrumentation. Rollbar does not support distributed tracing or OpenTelemetry ingestion at all. This makes Atatus a natural fit for teams already invested in OpenTelemetry standards who want a single backend for errors, traces, and logs.
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