Airbrake vs Raygun: A Hands-On Error Tracking Comparison for 2026

A monitoring platform can capture every error in an application and still give you a misleading picture of what is going wrong. Airbrake combined two unrelated crashes into one error group, while Raygun missed an unhandled promise rejection in my Node.js test. Both limitations matter when a team relies on an error inbox to decide what needs fixing.

The platforms also have different strengths. Airbrake links backend exceptions to route-level latency, request volume, and error rates. Raygun separates Crash Reporting, Real User Monitoring (RUM), and Application Performance Monitoring (APM) into products that cover more of the browser experience, deployment history, and performance investigation.

A broader feature list doesn't automatically mean a better investigation, though. Raygun's APM integration created an unexpected compatibility problem: after I installed it, native JavaScript exceptions began arriving as "Unknown error" without their messages or stack traces.

To understand the trade-offs, I tested both platforms against a deliberately unreliable Node.js bookstore. I examined how they captured and grouped errors, exposed debugging context, recognized returning bugs, monitored slow requests, and handled pricing and integrations.

Airbrake vs Raygun: Results at a glance

These findings reflect my October 2026 configuration. Both platforms received the initial backend failures and a returning bug, but only Raygun received the intentionally faulty 1.2.0 release.

How I tested Airbrake and Raygun

The test application, monitoring-lab, is an online bookstore with an Express API, SQLite database, Vite storefront, a simulated upstream price service, and a background sales report job. I deliberately planted failures with known causes so I could distinguish useful diagnoses from misleading displays.

The main scenarios were:

• Server crashes: Missing-book and invalid-coupon TypeErrors, plus an unhandled recommendation promise rejection.
• Handled exceptions: An out-of-stock order and an upstream pricing service returning HTTP 503.
• Background failures: A scheduled sales report that failed intermittently and changed its message between runs.
• Browser errors: A broken wishlist action and failed fetch requests for books, orders, prices, and checkout.
• Slow operations: A CPU-heavy search, slow upstream pricing, and a later database-driven checkout slowdown.
• Deployment problems: A supposedly fixed bug returning and a new intermittent payment error.

Releases and test coverage

The traffic runs were separate, so raw event totals aren't directly comparable. Airbrake used its Node.js notifier 2.1.9. Raygun used raygun 2.2.10 for the API, raygun4js 3.1.4 for the browser, and, during the APM test, raygun-apm 2.3.0.

Raygun's browser monitoring was tested, while Airbrake's browser notifier was not. Neither platform's notification delivery or external AI workflow was evaluated.

Setup and onboarding

Getting the SDK running is only part of onboarding. You also need to understand how many projects, integrations, and paid capabilities the monitoring workflow depends on.

Airbrake: Express error monitoring with route metrics

Airbrake's signup required a password and payment information for its trial. The activation form described a 30-day trial and a starting combination of account features, error quota, and performance events.

After I integrated @airbrake/node, the error dashboard began receiving server errors, and the Performance area automatically collected request rates and response times. No separate APM agent was needed to get those route metrics.

Raygun: Separate frontend and backend applications

Raygun first asked me to create an application and choose a monitoring product. I created separate applications for the Node.js API and the browser, each with its own API key and error list.

The onboarding flow handled one product at a time and initially waited for a first event before progressing. That organization was understandable, but required more configuration than a single shared error list.

The browser setup also needed a workaround. Importing raygun4js from a lazily loaded module didn't initialize it after the page's load event. Using queued calls before the import resolved that issue.

Source-map uploads introduced another limitation: automated API upload required a paid Personal Access Token, unavailable on my trial. I uploaded the files manually and entered absolute resource URLs for each build file.

The upload eventually worked, but every rebuild with changing bundle names required more attention.

Verdict: Mixed. Airbrake offered useful server monitoring with fewer components, while Raygun provided a broader browser setup but required separate products and more configuration.

Error capture

A useful error tracker needs to distinguish failures that are deliberately reported from exceptions the application didn't anticipate. Both products recorded the common Express failures, but one missed a rejection.

Airbrake: All six backend failure types arrived

Airbrake's initial list contained five groups representing the six planted failures. The missing-book and coupon TypeErrors were merged, but both were present as separate occurrence types inside the group.

The unhandled recommendation rejection appeared with a one-frame backtrace pointing to loadRecommendations.

There was no explicit unhandled indicator, URL, or user context on that occurrence. It was captured, but the event didn't explain the mechanism of the failure.

Raygun: Five server groups, with one rejection missing

Raygun's Node.js error list contained five groups: the two TypeErrors, out-of-stock orders, the pricing service failure, and the sales report. The recommendation rejection didn't appear, despite reportUncaughtExceptions being enabled.

This result is specific to the SDK configuration I tested. It doesn't establish that every Raygun integration misses unhandled rejections, but it is a meaningful gap in this hands-on run.

Raygun also captured browser exceptions in its separate web application. Those browser events are not comparable with Airbrake here because I didn't install Airbrake's browser notifier.

Verdict: Airbrake for server capture. It recorded all six planted failure types, whereas Raygun missed the recommendation rejection in this configuration.

Debugging an individual error

After capture, the question changes from "Did an error happen?" to "What caused it, and whom did it affect?" Airbrake and Raygun had different ways of presenting that context.

Airbrake: Route impact, raw parameters, and limited source context

Airbrake showed the exception's source file and location, request route, environment, and version. For the coupon error, it also displayed that checkout had an 11.76% error rate across 85 requests.

However, the stack trace didn't display surrounding source code. The underlying context reported that the local file:// path couldn't be loaded for source mapping.

Custom breadcrumbs were present, but only as JSON under Parameters, mixed with process and operating-system data.

This makes the details available, but harder to scan during an investigation.

Raygun: Organized breadcrumbs and browser source code

Raygun's backend error page showed occurrences, affected users, request information, and separate tabs for breadcrumbs and custom data.

For the coupon failure, its backend breadcrumbs contained POST /checkout, "checkout started", and "book found". These were scoped to the current request.

The backend stack trace still lacked surrounding source lines, but its browser source maps resolved the wishlist failure to pages.js:122, with the offending code highlighted after manual upload.

A more structured event page helped Raygun's debugging experience, although its frontend and backend errors remained in separate applications.

Verdict: Mixed. Raygun presented event breadcrumbs and users more clearly, while Airbrake offered immediate route-impact context and deep occurrence aggregations.

Error grouping

Grouping determines whether engineers get one useful issue per bug or a confusing mixture of unrelated failures. Each platform had a different problem.

Airbrake: Two distinct TypeErrors became one group

The missing-book failure occurred on GET /books/:id at server.js:38, while the coupon failure occurred on POST /checkout at server.js:81.

Airbrake normalized both messages into Cannot read properties of undefined (reading {}) and combined the occurrences. Its Aggregations view showed 13 title failures and 10 discount failures in the same group during the first run.

That was a serious triage problem: resolving the coupon issue also resolved the missing-book bug because they were one group. I couldn't find a split or merge control to correct it.

Raygun: Better initial separation, but duplicates later

Raygun initially kept the two backend TypeErrors separate, which more accurately represented the underlying bugs.

Its browser grouping was less consistent. The same browser ApiError type appeared as separate items when the failing book URL changed, while other URL variations remained grouped.

Raygun's faulty 1.2.0 release exposed another problem. It split the new payment failure into two groups based on charge amount, even though the exceptions shared the same class and stack location. It also created new groups for old coupon and out-of-stock errors after the checkout stack changed.

Airbrake never received that release, so there is no evidence that it would have handled the exact same stack changes better.

Verdict: Raygun on the shared backend test. It separated two different bugs that Airbrake merged. Raygun's later grouping problems remain significant, but they were observed only in Raygun's additional release test.

Search, filtering, and error impact

When a group contains hundreds of occurrences, the ability to isolate one user, route, or release becomes just as important as the group itself.

Airbrake: Detailed filters and occurrence shares

Airbrake's search interface exposed 27 filter keys, including user, version, route, HTTP method, file, and message parameter.

Filtering for one user returned the number of matching occurrences inside each group, such as four matching TypeError occurrences out of 23.

Combining a route and version filter isolated the coupon failures from the merged TypeError group. That was important because the grouping itself had obscured the two bugs.

The limitation was the query interaction: filters had to be selected through chips. Typing route:/check as a query didn't behave like a structured expression.

Raygun: Customer and release navigation

Raygun's Crash Reporting linked error occurrences to customer records. Its separate browser RUM product also offered session and page-performance filtering.

These were useful views, but my notes don't support a controlled comparison of the complete search languages or custom-filter capabilities in both products.

Verdict: Airbrake for the tested filtering workflow. Its matching counts and aggregations helped compensate for the over-grouped TypeError issue.

Real user monitoring and session context

Airbrake's browser notifier wasn't part of this test. Raygun's RUM product was, so the findings here demonstrate Raygun's capabilities rather than a head-to-head browser verdict.

Raygun: Browser performance and per-user sessions

Raygun RUM collected page-load and Core Web Vitals information, including LCP, CLS, and INP.

Its virtual page table normalized routes such as /books/3 to /books/*. In my run, the books route averaged 1.58 seconds, while checkout averaged 1.76 seconds.

The Customers page also connected recorded sessions and frontend errors to identified users.

The individual session showed a timeline of pageviews, navigation, and time spent on each page. It wasn't a visual session replay, so it didn't reconstruct the screen itself.

Airbrake: Browser not instrumented

Airbrake has a browser JavaScript notifier, but I only configured the Node.js integration. Its browser capture, breadcrumbs, source maps, and any browser performance integration therefore remain untested in this comparison.

Verdict: Not compared. Raygun offered useful browser performance and customer-session information. Airbrake's browser capabilities weren't evaluated.

Release tracking and regressions

Marking an issue fixed is easy. Recognizing that the fix didn't hold is where release tracking becomes useful, and Raygun provided the clearer regression view.

Airbrake: Automatic resolution made every old bug look resolved

Airbrake's Resolve action immediately changed the issue status, with an Undo notification. It offered no option to resolve in a specific future version.

Airbrake's project setting to resolve all errors after a deployment was enabled by default. After I reported the 1.1.0 deployment, it resolved four other open groups as well as the issue I'd already fixed manually.

When the old bugs occurred again, they reopened, but the error list displayed no Regressed label. The merged TypeError showed 46 occurrences, including 23 since the last deployment.

A deployment view showed zero fixed and zero introduced errors, even while the activity feed stated that the deployment had resolved four errors. The counts were difficult to interpret without the context of automatic resolution.

Raygun: A dedicated regression tab

Raygun's Resolve in next version workflow explicitly kept old-version events resolved and reactivated the error only if it appeared in a newer version.

The first attempt to test that workflow was interrupted by an APM compatibility problem, so I removed raygun-apm and repeated the 1.1.0 traffic. Once native errors were reported normally again, the coupon issue reopened.

Its activity history recorded that the error had reoccurred, while the deployment page displayed one issue under a dedicated Regressions tab.

This made the returning bug easier to identify than Airbrake's status flip and "since last deploy" count.

What happened with Raygun's faulty deployment?

In release 1.2.0, Raygun's deployment page reported five new error groups. Those included the newly introduced payment failure, but also duplicate groups created for older errors whose stack traces changed.

The dedicated regression view was useful for the controlled 1.1.0 test, but grouping mistakes could still distort a deployment's New errors count.

Verdict: Raygun. Its version-based resolution and dedicated Regressions tab made the failed fix visible without digging through one large error group.

Performance monitoring

The core question here is whether a tool can identify a slow request without an exception. Airbrake did this through automatic route metrics. Raygun offered both browser RUM and a separate Node.js APM product, but the server profiler introduced serious limitations.

Airbrake: Slow routes without extra setup

Airbrake's Performance overview displayed Apdex, mean response time, throughput, and error rate. In the first run it showed 270 requests, a mean response of 239.04 milliseconds, Apdex 0.92, and a 10% error rate.

The Routes table immediately identified GET /search at p95 1.90 seconds and GET /prices/:id at p95 1.70 seconds, highlighting both as slow.

This was useful even when a request returned successfully: the search endpoint had zero recorded errors but still had slow response times.

Airbrake couldn't explain the time inside those routes in my configuration. The route's detailed breakdown was empty, and Queries and Jobs had no data because the relevant instrumentation wasn't configured.

Raygun: APM setup and missing application functions

Raygun's APM required the raygun-apm package plus a separate agent. On my Mac, the profiler initially sent to 0.0.0.0 and failed to reach the agent. Setting RAYGUN_AGENT_HOST=127.0.0.1 resolved that connection problem.

The APM overview did display request metrics: Apdex 0.94, median 83 milliseconds, and p99 4,754 milliseconds.

However, SQLite queries weren't supported, and the traces didn't show the application methods I expected. A slow price request appeared mostly as garbage collection and idle time; its upstream fetch didn't appear as an external API call.

This meant Raygun could indicate that a request was slow, but not explain the relevant application operation in my ESM-based Node.js setup.

The more serious issue: APM broke Crash Reporting

Installing raygun-apm caused native JavaScript exceptions to arrive in Crash Reporting as Unknown error, with empty messages and stack traces, even when the profiler wasn't started.

I traced this to the installed Crash Reporting SDK loading an APM bridge that replaced the global Error constructor. Native V8 exceptions then failed the SDK's instanceof Error check.

Disabling that bridge with RAYGUN_APM_CRASH_REPORTING_INTEGRATION=false, or uninstalling the APM package, restored normal reporting.

This is a version-specific compatibility finding, not a claim that every Raygun installation behaves this way. It was nonetheless the most concerning issue of the test because adding performance monitoring damaged error quality.

Did Raygun catch the later checkout slowdown?

The faulty 1.2.0 release made checkout requests slow, but APM was uninstalled by then. Crash Reporting had no request timing, while RUM's Compare view reported that 1.2.0 was 34.8% faster in median page-load time.

That result wasn't a false calculation of page loads. It was an unsuitable metric for the problem: the slow operation was the /checkout XHR, not the initial page load. The comparison simply measured a different part of the experience.

Airbrake wasn't tested on 1.2.0, so this cannot be treated as evidence of an Airbrake win on that release.

Verdict: Airbrake for backend monitoring in the tested setup. It identified slow routes with minimal configuration. Raygun offered broader products, but its APM integration neither exposed the expected code-level detail nor coexisted safely with Crash Reporting in this test.

Logs, alerts, and integrations

Neither platform provided a full application log explorer in the configuration I tested. Airbrake focused on errors and performance routes, while Raygun separated crashes, RUM, and APM.

Airbrake supported email digests, severity thresholds, Slack, Microsoft Teams, Jira, GitHub, GitLab, and webhooks. Its Jira form offered automatic ticket creation and reopening when an error returned.

Raygun also offered notifications and issue categorization, with email, Slack, and Teams among its published alert channels. I did not verify an actual notification arriving from either service.

Verdict: Not compared for alert quality. Airbrake's notification controls were inspectable, but I didn't exercise delivery, and Raygun's alert rules weren't evaluated head to head.

AI-assisted debugging

Raygun advertises AI Error Resolution and a remote MCP server for connecting external coding assistants to monitoring data. I did not test either workflow against the planted bugs.

No comparable AI root-cause workflow was evaluated in Airbrake. That prevents a meaningful verdict on which assistant would diagnose these failures more accurately.

Verdict: Not compared. Feature availability does not establish the accuracy or usefulness of AI-assisted debugging.

Pricing

The following is a snapshot of published pricing checked on November 2, 2026. Actual subscriptions can depend on usage, contract terms, and add-ons. See Airbrake pricing and Raygun pricing.

Free allowances and trials

Airbrake's free usage tiers are helpful for small projects, but activating an account can still require payment details. Raygun's trial covered Crash Reporting and RUM, while the APM integration introduced additional setup.

Entry-level paid plans

Airbrake meters error and performance events separately. Raygun sells Crash Reporting, RUM, and APM as separate products, with small bundled allowances in some plans.

Comparing the actual cost

For a server application that mostly needs error grouping, request rates, and slow-route detection, Airbrake can cover those needs without paying for three monitoring products. But its plan-package and event-tier structure requires checking the final combination carefully.

Raygun may make more sense when browser performance and customer sessions are central to the evaluation. Those capabilities come through RUM, and deeper Node.js APM carries a separate subscription and agent requirement.

Verdict: Depends on scope. Airbrake offers a lower published entry point and basic route performance. Raygun offers a broader set of separately priced monitoring products, but the combined cost is substantially higher.

Full feature comparison

Which should you choose?

Both platforms collect application failures, but they suit different workflows once debugging goes beyond the initial error message.

Choose Airbrake if backend errors and slow routes are your main concerns

Airbrake is appealing for teams that want to see which Express endpoints are slow, how often they fail, and which users or versions are affected. Its filters and aggregations were strong, and basic performance data arrived without an additional tracing agent.

Its limitation was error quality. Two unrelated TypeErrors ended up in one group, and the raw event context made debugging less convenient. Automatic resolve-on-deploy also created misleading signals when unfixed bugs returned.

For another hands-on comparison, see Rollbar vs Airbrake.

Choose Raygun if frontend experience and deployment regressions matter more

Raygun's RUM product added browser performance, customer sessions, and Core Web Vitals. Its dedicated deployment Regressions tab made a returned error much easier to identify than Airbrake's issue list.

The trade-offs were more setup, separate product subscriptions, and worrying gaps in the tested Node.js integration. Raygun missed one unhandled rejection, and installing APM made native JavaScript exceptions lose their diagnostic messages and stacks. Those specific SDK combinations deserve verification before production adoption.

For a related hands-on evaluation, see BugSnag vs Raygun.

Frequently asked questions

Is Airbrake better than Raygun for error tracking?

Airbrake captured all six planted backend failure types, while Raygun captured five. However, Airbrake merged two different TypeErrors, and Raygun initially kept them separate. The better fit depends on whether capture completeness or issue separation is more important in your environment.

Does Raygun include real user monitoring?

Yes. Its separate RUM product collected browser performance data, Web Vitals, customer sessions, and virtual page metrics in my test. The session view was a page-activity timeline, not visual session replay.

Does Airbrake offer APM?

Yes. It automatically collected route response times, Apdex, throughput, and error rates from my Express application. Database queries and background job details were empty because I hadn't configured the additional instrumentation.

Can Raygun APM break error reporting?

It did in my test with raygun 2.2.10 and raygun-apm 2.3.0. Native exceptions arrived as Unknown error with empty messages and stacks. Uninstalling the APM package or disabling its crash-reporting bridge restored normal behavior. This is a specific SDK compatibility finding, not a universal claim about Raygun.

Which product identifies returning bugs more clearly?

Raygun. Both reopened issues, but Raygun's deployment page provided a dedicated Regressions tab. Airbrake showed that the issue was unresolved and counted occurrences since the last deploy, without a special regression label.

Does either platform provide distributed tracing?

Neither connected the browser and backend through distributed traces in my tested setup. Airbrake provided route statistics, while Raygun's separate APM profiler exposed limited server timing without the expected application function details.

Which platform is cheaper?

Airbrake lists a paid starting price of $19 per month, while Raygun Crash Reporting Basic starts at $40 per month on annual billing. Raygun charges separately for RUM and APM. Total cost depends on errors, performance events, sessions, and the products required.

Final thoughts

Airbrake was better at spotting slow backend routes, and Raygun was better at showing browser experience and deployment regressions. Neither won every part of the test: Airbrake combined distinct server bugs, while Raygun missed an unhandled rejection and its Node.js APM package disrupted Crash Reporting.

For teams focused on API health, Airbrake's automatic route statistics are a meaningful advantage. For teams that spend more time investigating frontend performance and deployment changes, Raygun has useful capabilities across its products.

The final decision is about what you cannot afford to miss: an endpoint getting slower or an application failure being misreported. In this test, both platforms showed why that distinction matters.

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