The most revealing difference between BugSnag and Honeybadger appeared after I deliberately shipped an error I'd already marked as fixed. Both tools caught it again and reopened the issue. But BugSnag recorded exactly why it reopened, while Honeybadger returned it to Unresolved without adding a corresponding entry to its history.
That difference says a lot about how the tools approach error monitoring. BugSnag focuses on exception tracking, triage, and release stability. Honeybadger combines error tracking with Insights for logs and custom metrics, plus uptime monitoring, check-ins, and status pages.
The two platforms also made different trade-offs during my tests. BugSnag gave me clearer information about application stability and related errors from the same session. Honeybadger captured both frontend and backend failures in one project, kept changing error messages together, and revealed a slow database query through application logs.
However, neither offered full distributed tracing in my configuration. Honeybadger's automatic resolve-on-deploy setting also briefly cleared real, unfixed bugs from the error list, illustrating why a clean inbox doesn't necessarily mean a healthy application.
I tested both against a deliberately unreliable Node.js bookstore, introducing exceptions and recurring failures across multiple releases. The comparison covers setup, error capture, debugging, grouping, regression tracking, browser monitoring, performance, logs, alerts, AI workflows, and pricing.
The verdicts reflect my October 2026 test setup. BugSnag was tested on the server through two releases, while Honeybadger covered the browser and server through three. Features tested only in Honeybadger aren't treated as head-to-head wins.
| Area | Better in my test | Why |
|---|---|---|
| Setup | Mixed | Both had simple Express integrations; Honeybadger needed development enabled explicitly |
| Server error capture | Tie | Both captured all six planted server failures |
| Debugging an error | Mixed | BugSnag had Correlated events; Honeybadger exposed request parameters and linked deployments |
| Error grouping | Tie on shared test | Both separated server bugs; Honeybadger also handled variable messages in a later test |
| Browser error tracking | Honeybadger only | BugSnag's browser SDK wasn't tested |
| Release stability | BugSnag | Automatic session stability scores for both tested releases |
| Regression detection | BugSnag | Recorded the reopening reason in issue activity |
| Resolve on deploy | Honeybadger only | Automatically resolved issues by environment, including unfixed bugs |
| Performance monitoring | Neither demonstrated tracing | BugSnag needed OpenTelemetry; Honeybadger exposed slow-query logs |
| Logs and custom metrics | Honeybadger | Insights collected and queried structured events |
| Uptime and check-ins | Not compared | Honeybadger offered both; neither was tested |
| AI debugging | Not compared | Both offered external assistant workflows that weren't tested |
| Pricing | Mixed | Both had free plans; Honeybadger published a clear paid starting price |
My test application, monitoring-lab, is a deliberately unreliable online bookstore. It uses an Express API backed by SQLite, a Vite storefront, a simulated upstream pricing service, and a background reporting job.
The application generates several predictable problems:
TypeError when a requested book doesn't exist.TypeError when checkout receives an unknown coupon.| Release | What I tested |
|---|---|
| 1.0.0 | SDK setup, error capture, grouping, debugging context, and available monitoring features |
| 1.1.0 | A resolved coupon error returning without a code fix |
| 1.2.0 | Honeybadger only: automatic resolution on deploy, slow checkout queries, and new payment failures |
The final release mattered because it introduced two independent problems. A database query became slow, and a payment operation started failing intermittently.
Would the monitoring platform identify both, or would the new exceptions hide the performance regression?
Only Honeybadger received this release, so its findings show what it could do rather than proving a difference in quality against BugSnag.
| Platform | SDKs |
|---|---|
| BugSnag | @bugsnag/js with @bugsnag/plugin-express |
| Honeybadger | @honeybadger-io/js 6.17.0 for browser and server |
The conditions were as follows:
These differences are important because a missing feature in my test doesn't necessarily mean the product lacks it.
Both platforms let me monitor Express errors with a small SDK integration. However, Honeybadger provided more functionality within one project, while BugSnag immediately collected the session data needed for its stability reporting.
BugSnag's onboarding asked where the application runs, offering platforms such as browser, server, mobile, and desktop.
After selecting Express, it provided installation instructions for @bugsnag/js and @bugsnag/plugin-express.
The Express integration uses a request handler before the routes and an error handler after them. No separate deployment reporting was needed to associate errors with application versions.
Once traffic arrived, the overview displayed release stability.
Release 1.0.0 showed 290 sessions and 83.103% session stability, classified as Critical stability.
There was an important detail behind that number: the Express plugin counted each request as a session.
For a server application, that percentage therefore describes request-based stability rather than the percentage of unique customers who experienced a crash-free visit.
The overview also displayed two performance monitors as Alerting even though no performance instrumentation had been installed. That made the performance status difficult to interpret at first.
Honeybadger used the same @honeybadger-io/js package for the browser and server.
Both adapters sent telemetry to one project using the same API key. The Express integration used requestHandler and errorHandler, with reportData enabled.
However, Honeybadger ignores the development environment by default.
Because my local API ran in development, I had to configure the SDK to report those events.
The setup page also included a Waiting for data panel and displayed the API key inside the integration snippet.
After configuration, frontend and backend errors appeared in the same Errors list.
The list contained eight groups from both sides of the application, with separate environment labels for browser and server errors.
Yes.
I uploaded browser source maps and reported deployments through Honeybadger's API using the project key.
Both worked during the trial, without a separate paid token.
The deployment appeared in Honeybadger's Deployments page.
The row recorded the revision, environment, deployment time, and user.
It also included a Compare link that constructed a GitHub comparison between revisions. My repository didn't contain the corresponding release tags, so that comparison wasn't useful for identifying code changes.
The deployment API worked, but correct repository revision information was still necessary to make the comparison meaningful.
| Capability | BugSnag | Honeybadger |
|---|---|---|
| Express integration | ✔: plugin and middleware | ✔: request and error handlers |
| One project for browser and server | Browser not tested | ✔: one project |
| Development environment | Captured in test | Needs explicit configuration |
| SDK-based release version | ✔: appVersion |
✔: revision |
| Automatic server sessions | ✔: per request | Not observed |
| Source-map upload | Not tested | ✔: API upload on trial |
| Deployment API | Not needed for stability | ✔: project key worked |
Verdict: Mixed. BugSnag reached useful release health information with less configuration. Honeybadger made it convenient to collect frontend and backend errors in one project and automate deployments and source maps.
Both tools captured all six planted server failures in their respective runs.
That included the unhandled recommendation rejection, which is particularly important because unhandled asynchronous failures can be easy to overlook.
After release 1.0.0, BugSnag displayed six errors.
The missing-book and invalid-coupon TypeErrors remained separate.
BugSnag also displayed handled and unhandled indicators directly in the list, making it easy to distinguish reported business errors from unexpected failures.
Its inbox filters included Open, For review, Assigned to me, Fixed, Snoozed, and Ignored.
The unhandled promise rejection appeared as its own error.
The stack contained one application frame, loadRecommendations, and the error was marked unhandled.
This was enough to identify the function where the rejection originated.
Honeybadger also captured all six backend failure types.
Its initial Errors list contained eight groups: six backend issues and two browser groups.
The backend entries included both TypeErrors, the out-of-stock error, the upstream 503, the unhandled recommendation rejection, and the reporting job failure.
The unhandled rejection was captured even though Honeybadger's Express handler produced an additional response-handling side effect.
In my test, the rejection was passed into Express after the response had already been sent. The final error handler logged ERR_HTTP_HEADERS_SENT, although the server continued running.
This didn't prevent Honeybadger from reporting the exception, but it was an integration behavior worth investigating before relying on the same middleware configuration in production.
Honeybadger captured the wishlist exception and API request failures through its browser SDK.
The API failures were reported under one ApiError group.
That included the order-tracking 404, checkout 500, missing-book requests, and price-service 502.
The browser errors carried the production environment because Vite built the storefront in production mode.
The server continued reporting development.
That split came from my test application, although it later affected deployment-based resolution.
| Test | BugSnag | Honeybadger |
|---|---|---|
| Missing-book exception | ✔: captured | ✔: captured |
| Invalid-coupon exception | ✔: captured | ✔: captured |
| Out-of-stock error | ✔: captured | ✔: captured |
| Upstream service error | ✔: captured | ✔: captured |
| Unhandled promise rejection | ✔: captured | ✔: captured |
| Background job failure | ✔: captured | ✔: captured |
| Browser exceptions | Not tested | ✔: captured |
| Handled and unhandled indicators | ✔: list badges | Browser and server details |
Verdict: Tie for server error capture. Both captured all six planted backend failures. Honeybadger also captured browser errors, but BugSnag's browser SDK wasn't tested.
Both tools collected the server exceptions, but their error pages didn't show the surrounding backend source lines.
I also tested the same errors in Better Stack.
The error page displayed the failing statement alongside its neighboring code, making it possible to understand the exception without opening an editor. The six planted backend failures also remained in six separate groups.
Once an exception has been recorded, the usefulness of the error page depends on how much context you can recover.
Who encountered the failure? What happened immediately beforehand? Did the same person run into other problems?
BugSnag and Honeybadger answered these questions in different ways.
BugSnag organized error information into separate tabs for stack traces, breadcrumbs, request data, user information, device information, and custom metadata.
Its stack trace displayed the application frame and surrounding framework calls.
The missing-book error pointed to server.js:38:45, but the interface didn't display the actual code around the failing line.
The breadcrumbs were more informative.
The timeline included an application breadcrumb named "book found", with the book's ID, title, and available stock.
Breadcrumbs could be filtered by type or searched directly.
User details appeared in their own tab, and handled errors retained additional metadata, including book IDs and quantities.
This was one of BugSnag's strongest features.
Its Correlated events tab displayed other errors associated with the same user and session.
In the test, carol's coupon error appeared alongside her out-of-stock errors.
Instead of manually searching for the shopper's other failures, I could inspect them from the current error page.
That made BugSnag particularly useful when one customer encountered several related problems during a visit.
Honeybadger's server error page displayed the exception's location, environment, host, and associated deployment.
The stack pointed to server.js:150:45.
As with BugSnag, the backend error didn't show surrounding source lines.
However, Honeybadger provided additional information in its Context and Params sections.
The Context section included the user's ID and email.
It also displayed an "Email user" action, allowing the developer to contact the affected customer from the error.
The Params section contained the checkout request body, including the book ID, coupon, and quantity.
This was particularly useful for understanding errors caused by invalid input.
Honeybadger also captured application breadcrumbs.
The timeline included "checkout started" and "book found", providing the sequence immediately before the exception.
Unlike BugSnag's correlated events, these breadcrumbs focused on what happened within the request.
They were useful for understanding the failing operation, but I didn't find an equivalent way to display every error the same shopper encountered in the session.
| Capability | BugSnag | Honeybadger |
|---|---|---|
| Backend source lines | ✘: frames only | ✘: frames only |
| Breadcrumbs | ✔: search and filters | ✔: request timeline |
| User identity | ✔: dedicated tab | ✔: Context section |
| Request parameters | Request and metadata tabs | ✔: Params section |
| Related errors from same session | ✔: Correlated events | Not observed |
| Deployment linked to error | SDK release information | ✔: linked deploy |
| Contact affected user | Not tested | ✔: Email user |
| Custom metadata | ✔: Details tab | ✔: Context fields |
Verdict: Mixed. BugSnag provided stronger context across errors from the same session. Honeybadger made request parameters, user information, and the associated deployment easy to inspect.
Good error grouping keeps repeated failures together without hiding separate problems.
The two platforms performed well on the initial server test, but Honeybadger's later release demonstrated both a strength and a limitation of grouping by exception class and location.
BugSnag kept the initial six backend failures separate.
The two TypeErrors didn't merge, even though they were both property-access failures.
Its project settings also offered alternative grouping methods.
The available options included grouping by error class and by error context.
I kept the default grouping configuration for the test.
BugSnag wasn't tested against the payment failures introduced in release 1.2.0, so I can't say how it would have grouped errors with changing amounts.
Honeybadger grouped errors primarily using exception class and location.
That behavior helped when the error message contained changing details.
For example, out-of-stock requests for different books remained in the same OutOfStockError group.
The error's displayed title updated to the most recent message, but the underlying group remained intact.
Release 1.2.0 provided another example.
The payment error produced seven notices with different charge amounts.
Honeybadger kept them in one group because they shared the exception class and application location, server.js:173:15.
Changing values in the message didn't create additional groups.
This reduced the risk of one underlying payment bug filling the inbox with separate entries.
Yes, and Honeybadger's browser errors demonstrated the opposite problem.
All application-reported ApiError events were grouped together.
That included failed book requests, checkout failures, order-tracking 404s, and price-service errors.
Why did these unrelated requests appear under one issue?
They shared the same exception class and top stack frame, src/api.js:29, where the application reported failed fetches.
Honeybadger treated them as one underlying error.
That made the list smaller, but it also combined different failure causes.
A developer could inspect individual notices to distinguish them, although the main list didn't separate the problems.
| Scenario | BugSnag | Honeybadger |
|---|---|---|
| Six initial backend bugs | ✔: six groups | ✔: six server groups |
| Two similar TypeErrors | ✔: separate | ✔: separate |
| Out-of-stock messages for different books | Grouped in initial test | ✔: one group across changing books |
| Payment messages with different amounts | Not tested | ✔: one group |
| Different browser API failures | Not tested | ✘: merged into one group |
| Custom grouping controls | ✔: class and context options | Not evaluated |
Verdict: Tie on the shared server tests. Both kept the initial bugs separate. Honeybadger handled changing backend messages well in its later test, but its browser grouping combined unrelated API failures.
Honeybadger collected browser exceptions and source-mapped stack traces in my test.
BugSnag's browser SDK wasn't installed, so this section documents Honeybadger's behavior without treating it as a direct product comparison.
Honeybadger used the same JavaScript package and project as the server.
The browser SDK captured the wishlist exception and failed API requests.
It also associated the errors with the production environment and release revision.
The browser API error page provided several triage actions.
The actions included Ignore, Resolve on Deploy, Copy for LLM, Export, Merge, and Share URL.
The browser source maps also worked after uploading them through the API.
The stack trace resolved to src/api.js:29:28 and displayed the failing throw new ApiError(...) statement.
That was a better source-code view than Honeybadger provided for my backend errors.
It can show the sequence of recorded events through breadcrumbs.
The timeline included UI clicks with selectors, navigation changes, fetch requests, and the application's own breadcrumbs.
That made it possible to understand the activity immediately preceding the exception.
However, Honeybadger didn't provide visual session replay or a full browser performance timeline in my test.
Its browser and backend errors also remained disconnected at the request level.
Although both appeared in the same project, I couldn't open a browser error and follow a distributed trace to the server exception behind it.
BugSnag offers a browser SDK and performance instrumentation, including page-load data through OpenTelemetry.
I didn't test either capability.
Consequently, I can't compare its browser error context, source maps, or performance reporting with Honeybadger's.
| Capability | BugSnag | Honeybadger |
|---|---|---|
| Browser errors | Not tested | ✔: captured |
| Browser source maps | Not tested | ✔: API upload worked |
| Automatic breadcrumbs | Not tested | ✔: clicks, navigation, requests |
| Browser performance | OpenTelemetry required; not tested | Not observed |
| Visual session replay | Not tested | ✘: not offered |
| Frontend-to-backend tracing | Not tested | ✘: not offered |
Verdict: Not compared. Honeybadger provided useful browser error tracking and source maps, but BugSnag's browser capabilities weren't tested.
Honeybadger collected browser and backend errors in one project, but it didn't connect them through distributed tracing. BugSnag's browser integration wasn't tested.
I also examined the same storefront in Better Stack.
The timeline contained 30 browser spans and seven backend spans, connecting the customer's actions to the server operations they triggered.
This provided request-level context that wasn't available in the Honeybadger configuration I tested.
Both platforms reopened the coupon error after I marked it fixed and generated another occurrence.
However, they used different rules to decide when an issue should reopen.
BugSnag allows an error to be marked fixed until it occurs in a newer version.
I used that option before deploying release 1.1.0.
When the error returned, BugSnag reopened it automatically.
The activity history explained why: the error had occurred in version 1.1.0 after previously being fixed.
That distinction matters because the resolution was tied to the application version.
Additional occurrences from the old release wouldn't necessarily invalidate the fix.
It appeared as an ordinary open issue.
The reopened coupon error had no Regressed or Reopened badge.
You had to open it and inspect its activity to understand what happened.
BugSnag's release pages provided another perspective.
Release 1.0.0 showed 83.103% session stability across 290 sessions.
Release 1.1.0 showed 83.893% across 298 sessions.
The stability score improved slightly, even though the coupon bug was still present.
The release detail separated Errors seen from Errors introduced. There were no newly introduced errors in 1.1.0, which was correct.
However, it didn't provide a separate list of returning bugs.
Honeybadger handled resolution differently.
Its error page provided a status toggle.
After I marked the coupon error resolved, the interface stated that it would alert me if the error occurred again.
There wasn't a separate "resolve in the next version" option.
The test sequence also differed slightly from BugSnag's.
I had already deployed release 1.1.0 and run traffic once before resolving the coupon error. I then marked it resolved and ran more 1.1.0 traffic without another deployment.
The new occurrence reopened the issue.
The latest occurrence appeared after the resolution, and the error returned to Unresolved.
This demonstrated recurrence detection, but the test didn't establish that Honeybadger had recognized a new release as the trigger.
This was the clearest difference between the two products.
Honeybadger's History tab recorded the original occurrence and the manual resolution.
However, the history didn't contain an entry explaining that the issue had reopened.
The issue also returned to the default list without a special regression label.
BugSnag was more explicit: its activity identified the newer version responsible for reopening the error.
Honeybadger also provided a project-wide setting called Resolve errors on deploy.
I enabled it before deploying release 1.2.0.
What happens when a monitoring tool assumes every deployment is intended to fix existing errors?
The result was revealing.
After reporting the development deployment, Honeybadger automatically resolved all six backend error groups.
Only the two browser error groups remained visible, because they belonged to the production environment.
The backend errors hadn't actually been fixed.
They were simply marked resolved because a new development deployment had been reported.
I then reported the production deployment and generated traffic against release 1.2.0.
The old errors reappeared as Unresolved, alongside the newly introduced payment error.
This demonstrates both the convenience and risk of automatic resolution.
It creates a clean slate for each deployment, but a clean error list immediately after deployment doesn't mean the bugs were fixed.
The environment-specific behavior also means an application with frontend and backend telemetry under different environment names may require separate deployment records.
| Capability | BugSnag | Honeybadger |
|---|---|---|
| Application version | ✔: SDK appVersion |
✔: SDK revision |
| Reopens after new occurrence | ✔: on a newer version | ✔: after resolution |
| Reopen reason in activity | ✔: names newer version | ✘: not observed |
| Regression badge in main list | ✘: not observed | ✘: not observed |
| Release stability percentage | ✔: per version | Not observed |
| Automatic resolve on deploy | Optional release workflow | ✔: project setting |
| Deployment reporting | Not required for tested workflow | ✔: API, per environment |
| Risk of resolving unfixed errors | Not observed | ✔: demonstrated with auto-resolve |
Verdict: BugSnag for regression history and release stability. Its activity explained why the coupon issue reopened, and its release page showed stability percentages automatically. Honeybadger's resolve-on-deploy workflow was flexible but also resolved unfixed errors temporarily.
BugSnag recorded the reason for reopening an error, while Honeybadger reopened it without a corresponding entry in its history. Neither highlighted the return in the main error list.
I also tested that scenario in Better Stack.
The returning coupon error displayed a red Reoccurred status directly in the list.
Its activity feed recorded the resolution and subsequent return, making the failed fix visible without manually comparing versions.
An error tracker can tell you when an exception occurs. Performance monitoring needs to identify slow operations even when they don't fail.
BugSnag and Honeybadger approached that requirement differently.
BugSnag's performance monitoring product uses OpenTelemetry.
The regular error tracking SDK doesn't automatically collect performance traces.
The dashboard listed Page Loads, Inbound Requests, Outbound Requests, and Custom Spans.
However, every performance view remained empty because I hadn't installed the OpenTelemetry instrumentation.
For a team already using OpenTelemetry, this could fit an existing monitoring architecture.
For a team installing BugSnag only for errors, performance monitoring requires additional work.
I didn't test the OpenTelemetry integration, so I can't assess its performance analysis quality.
Honeybadger doesn't provide distributed tracing in the configuration I tested.
However, the new payment error contained breadcrumbs with relative timestamps.
The "checkout started" event appeared at approximately 2.96 seconds before the error.
The "book found" event appeared at approximately 0.55 seconds before it.
That left a gap of roughly 2.4 seconds.
The interval corresponded to the deliberately slow database lookup.
The breadcrumbs revealed that checkout had stalled between two application events, although they didn't provide a dedicated database span or automatically identify the slow SQL statement.
There was also an unexpected breadcrumb from an earlier Honeybadger notice, approximately 51 seconds before the failure.
That suggested the request context hadn't fully isolated every breadcrumb between requests in my setup.
Yes, because the application itself logged a warning.
Honeybadger Insights allowed me to query warning events using BadgerQL.
The query returned 44 instances of "slow query: SELECT book for checkout" in release 1.2.0.
It also returned 36 payment gateway warnings from the same release.
This made the two problems visible in one place.
However, Honeybadger didn't measure the query duration itself in this test. It depended on the application emitting meaningful logs.
The difference matters because a slow operation without a corresponding log warning could remain unexplained.
| Capability | BugSnag | Honeybadger |
|---|---|---|
| Request timing from error SDK | ✘: additional instrumentation | ✘: not observed |
| Distributed tracing | OpenTelemetry required | ✘: not offered in test |
| Slow request timing | Not tested | Breadcrumb timing |
| Database query spans | Not tested | ✘: not available |
| Slow-query warning analysis | No log product | ✔: Insights query |
| Regression tied to release | Not tested for 1.2.0 | ✔: warning revision field |
Verdict: Neither demonstrated full tracing in this test. BugSnag required OpenTelemetry, while Honeybadger revealed the slowdown through breadcrumbs and application logs. Honeybadger's findings were useful, but they depended on what the application already recorded.
BugSnag's performance integration wasn't configured, and Honeybadger identified the slow query from application warnings rather than measured spans.
I investigated the same faulty release in Better Stack.
The request took 2.19 seconds. The book lookup accounted for 1.53 seconds, followed by a 660-millisecond payment operation that failed.
The spans provided measured durations for both operations, making it possible to distinguish the slow database query from the payment exception.
This was Honeybadger's clearest product advantage in the features I tested.
BugSnag concentrates on errors, metadata, breadcrumbs, and performance data. Honeybadger adds Insights, a queryable event store for application logs and custom metrics.
BugSnag collected useful contextual information inside individual error events.
However, I didn't find a general-purpose application log explorer or a custom metrics query interface in the tested setup.
That means logs would need to be retained and searched elsewhere.
For teams that already have a logging platform, this may not matter.
For teams trying to reduce the number of operational tools, it's a gap.
Honeybadger Insights collected logs, error notices, console messages, and custom application events.
The interface brought these events into one queryable view.
However, I noticed duplicate log data.
My adapter sent log events explicitly, while the SDK also captured console messages.
That resulted in some application messages appearing twice.
The duplication came from my instrumentation choices and could be avoided by changing how the application reports logs.
Yes.
I sent several custom metric events, including order counts and revenue values.
Honeybadger's BadgerQL query engine let me aggregate those events.
For example, this query counted metric events and summed their values by metric name:
It returned 55 checkout.orders events with additional totals for checkout.revenue and report.revenue.
That demonstrated that Honeybadger could do more than collect exceptions.
It could also aggregate application-defined operational data.
The editor's automatic closing of brackets and quotes occasionally made writing expressions awkward, but the queries worked.
| Capability | BugSnag | Honeybadger |
|---|---|---|
| General-purpose application logs | ✘: not offered | ✔: Insights |
| Searchable event store | Error search | ✔: BadgerQL |
| Custom metrics | Not available in tested setup | ✔: queried |
| Application warnings by release | Not available | ✔: revision filtering |
| Logs in the same product as errors | ✘: not offered | ✔: supported |
| Duplicate instrumentation risk | Not observed | ✔: console and explicit log capture |
Verdict: Honeybadger. Insights collected logs and custom metrics alongside errors, allowing me to investigate application warnings and aggregate operational values without leaving the platform.
Honeybadger also includes capabilities beyond error tracking, such as uptime monitoring, check-ins, and status pages.
BugSnag focuses more narrowly on error monitoring and integrations with external operational tools.
However, I didn't test uptime checks, status page publishing, or notification delivery.
BugSnag's project settings listed integrations for email notifications, Slack, Datadog, Jira, Trello, PagerDuty, Splunk, and webhooks.
It also provided flexible snoozing conditions.
The menu allowed errors to be snoozed until they occurred again, occurred after a specified period, reached a configured event count, or affected additional users.
That final option is particularly useful for errors whose severity depends on how widely they spread.
A team can temporarily suppress a low-impact bug while allowing it to return for review when more customers are affected.
Honeybadger includes uptime monitors, scheduled job check-ins, and public status pages.
These capabilities are useful alongside error tracking because an application can become unavailable without generating a captured exception.
A background job can also stop running entirely, leaving no new error event to report.
Honeybadger's check-ins are designed for those situations.
Its trial interface also offered Resolve on Deploy and error notification workflows.
I didn't configure uptime checks, send background job heartbeats, publish a status page, or verify notification delivery.
It depends on what you're trying to monitor.
BugSnag's snoozing and integrations help teams manage incoming exceptions and coordinate their response.
Honeybadger's broader monitoring products can also cover website availability, scheduled tasks, and public service communication.
However, the quality of Honeybadger's uptime checks and alerts can't be determined from feature availability alone.
| Capability | BugSnag | Honeybadger |
|---|---|---|
| Error notifications | ✔: integrations available | ✔: available |
| Slack | ✔: supported | ✔: supported |
| Conditional snooze | ✔: user and count thresholds | Not evaluated |
| Uptime monitoring | Separate service required | Included, not tested |
| Scheduled task check-ins | Separate service required | Included, not tested |
| Public status pages | Separate service required | Included, not tested |
| Alert delivery tested | No | No |
Verdict: Not compared for monitoring quality or alert delivery. Honeybadger offered more monitoring categories, while BugSnag provided useful error triage controls. Neither platform's alert reliability was tested.
Neither platform's AI workflow was tested against the planted bugs.
Both offered ways to send error information to an external coding assistant, although the actions differed.
BugSnag included a Fix with MCP action on error pages.
The action opened a prompt to install the SmartBear MCP Server.
The integration allows an external coding assistant to work with BugSnag error information.
I stopped before installing it.
Consequently, I can't evaluate whether the assistant would correctly identify the source of the coupon or recommendation errors.
Honeybadger provided a Copy for LLM action on its error page.
The action is intended to copy error information for use in another assistant.
That can be convenient when a team already relies on an AI coding tool.
However, copying error data into an external assistant is different from a fully integrated investigation that automatically searches traces, logs, and source code.
I didn't run the copied prompt through an assistant, so its usefulness remains untested.
Honeybadger has also introduced a hosted MCP server, but it wasn't part of these hands-on tests.
| Capability | BugSnag | Honeybadger |
|---|---|---|
| AI action in error page | Fix with MCP | Copy for LLM |
| External coding assistant | ✔: through MCP | ✔: through copied context or MCP |
| In-product autonomous root cause investigation | Not tested | Not tested |
| Tested against planted failures | No | No |
Verdict: Not compared. Both offered ways to use external AI assistants, but I didn't test their debugging accuracy.
BugSnag and Honeybadger exposed external AI workflows, but I didn't test either of them.
I did investigate the same faulty release using Better Stack's AI SRE without connecting my source repository.
The investigation identified the book lookup query as the operation responsible for the latency regression.
It also found that 10 of 48 payment calls failed in the new release, separating the payment problem from the slow database operation.
The investigation took approximately four minutes and cost $0.49.
Both BugSnag and Honeybadger offer permanent free plans, but their paid pricing structures differ.
BugSnag uses volume-based event and span selections. Honeybadger publishes base subscription prices with included quotas for errors, Insights, and uptime monitoring.
The figures below come from the BugSnag pricing page and Honeybadger pricing page, checked on November 2, 2026.
| Feature | BugSnag Free | Honeybadger Developer |
|---|---|---|
| Base price | $0 per month | $0 per month |
| Users | 1 | 1 |
| Error allowance | 7,500 events per month | 5,000 errors per month |
| Performance data | 1 million spans per month | 50 MB Insights ingest per day |
| Error retention | 7 days | 15 days |
| Uptime monitoring | Not included | 1 monitor |
| Status pages | Not included | 1 status page |
| Projects | Unlimited | Unlimited |
BugSnag's free plan includes a larger error allowance.
Honeybadger includes a smaller error quota but provides logs, uptime checks, and a status page within the free offering.
Both are viable starting points for a small application, depending on the type of monitoring you need.
| Feature | BugSnag Select | Honeybadger Team |
|---|---|---|
| Published starting price | Price not listed for selected usage | $26 per month |
| Billing | Volume selection | Annual billing equivalent |
| Users | Unlimited | Unlimited |
| Included error volume | Selected event tier | 50,000 per month |
| Performance allowance | Selected span tier | 100 MB Insights ingest per day |
| Error retention | 60 days | 90 days |
| Uptime monitors | Not included | 5 |
| Status pages | Not included | 1 |
Honeybadger's Team plan also includes collaboration features and higher retention allowances.
BugSnag's pricing page displayed Select as "Starting at $0" alongside volume selectors, but didn't provide a reliable fixed paid price without choosing a specific event and span volume.
That makes a direct subscription-cost comparison difficult.
| Requirement | BugSnag | Honeybadger |
|---|---|---|
| Higher error volume | Larger event tier | Error usage above included quota |
| Performance monitoring | Span allowance plus instrumentation | More Insights ingest |
| Tracing | OpenTelemetry integration | Separate tracing tool |
| Application logs | Separate log service | Included Insights allowance |
| Uptime checks | Separate service | Additional monitor capacity |
| Status pages | Separate service | Included page, additional capacity |
| AI coding assistant | External tool | External tool |
Honeybadger's Team plan lists an optional error overage rate of $0.0003 per event above the plan quota, subject to its quota and overage settings.
BugSnag's actual cost depends on the selected usage tiers.
The starting price doesn't tell the whole story.
With BugSnag, you get focused error monitoring and SDK-based release stability. You still need additional instrumentation for performance tracing, and a separate platform for application logs, uptime checks, or public status pages.
Honeybadger includes more of those operational capabilities in its base plan.
Its Team plan starts at $26 per month on annual billing, with 50,000 errors per month, 100 MB of daily Insights ingest, five uptime monitors, and one status page.
However, Honeybadger doesn't provide distributed tracing or visual session replay in the tested product.
Those capabilities would require additional tools.
The real cost depends on how much monitoring you're consolidating. BugSnag may suit a team that already has logging and uptime covered. Honeybadger may reduce the number of subscriptions needed for a smaller application.
Verdict: Mixed. BugSnag provides a larger free error allowance, while Honeybadger publishes a clear paid starting price and includes more monitoring capabilities in the base subscription.
| Feature | BugSnag | Honeybadger |
|---|---|---|
| Main focus | Error tracking and release stability | Error tracking, Insights, uptime |
| Express integration | ✔: plugin | ✔: middleware |
| Browser and server in one project | Browser not tested | ✔: supported |
| Server errors captured | Six of six | Six of six |
| Unhandled promise rejection | ✔: captured | ✔: captured |
| Handled and unhandled badges | ✔: in inbox | Not evaluated |
| Backend source code | ✘: frames only | ✘: frames only |
| Browser source maps | Not tested | ✔: worked |
| Breadcrumbs | ✔: searchable | ✔: automatic and manual |
| Related errors by session | ✔: Correlated events | Not observed |
| Request body | Available in event context | ✔: Params section |
| Contact affected user | Not tested | ✔: Email user |
| Default server grouping | Six separate groups | Six separate groups |
| Variable payment messages | Not tested | ✔: one group |
| Different browser API errors | Not tested | ✘: combined |
| Release stability score | ✔: automatic | Not observed |
| Reopening after resolution | ✔: tied to newer version | ✔: after recurrence |
| Reopen reason in history | ✔: recorded | ✘: not observed |
| Resolve errors on deploy | Optional workflow | ✔: project setting |
| Distributed tracing | OpenTelemetry required | ✘: not offered in test |
| Logs | ✘: no log explorer | ✔: Insights |
| Custom metrics | Not tested | ✔: BadgerQL |
| Uptime monitoring | External service | Included, not tested |
| Scheduled job check-ins | External service | Included, not tested |
| Status pages | External service | Included, not tested |
| Conditional snoozing | ✔: available | Not evaluated |
| AI workflow | Fix with MCP | Copy for LLM and MCP |
| AI accuracy tested | No | No |
| Permanent free plan | Yes | Yes |
| Free error allowance | 7,500 monthly events | 5,000 monthly errors |
| Published paid starting price | Volume-dependent | $26 per month annually |
BugSnag and Honeybadger both handled the core server errors well. The decision depends on whether you want richer exception triage and release stability or more operational monitoring in the same platform.
BugSnag makes sense when your team spends much of its time investigating exceptions, prioritizing bugs, and tracking application stability.
It captured every planted server error, distinguished handled from unhandled failures, and provided searchable breadcrumbs and correlated events.
Its release stability scores were particularly useful. They appeared automatically from SDK sessions, without requiring deployment API calls.
The version-based resolution workflow also recorded why a previously fixed error reopened.
However, BugSnag's focus leaves gaps outside error monitoring. Performance requires OpenTelemetry, and general-purpose logs and uptime checks need other tools.
Its browser SDK also wasn't part of this test, so the browser-side experience would require separate evaluation.
For a comparison with a broader debugging platform, see our Sentry vs BugSnag hands-on comparison.
Honeybadger makes sense for teams that want to monitor application failures, inspect structured logs, track custom metrics, and check service availability without maintaining several separate products.
It captured all six backend failures and handled browser errors through the same project.
Its BadgerQL interface made it possible to investigate the slow-query warning and summarize custom checkout metrics.
Honeybadger also kept payment failures with changing amounts in one group, avoiding duplicates for that scenario.
The trade-offs appeared in grouping and regression tracking.
Its browser ApiError group combined unrelated failed requests, and its error history didn't record the coupon issue reopening.
The optional resolve-on-deploy setting also marked unfixed errors resolved until they occurred again.
Honeybadger gives you broader operational coverage, but its error workflows need careful configuration.
For another comparison involving Honeybadger's error tracking approach, see our Sentry vs Honeybadger comparison.
Both captured all six planted backend failures.
BugSnag provided stronger issue context through Correlated events and clearer version-based reopening history.
Honeybadger added request parameters, deployment links, and broader log analysis through Insights.
Yes.
Honeybadger's JavaScript SDK collected browser exceptions and failed API requests in the same project as the backend errors.
Source maps and automatic breadcrumbs also worked in my test.
However, Honeybadger grouped several unrelated browser API failures under one error because they shared the same reporting location.
Yes, through OpenTelemetry-based performance monitoring.
The regular error tracking SDK didn't collect performance traces by itself in my setup.
I didn't install the required OpenTelemetry instrumentation, so I couldn't evaluate BugSnag's tracing performance.
Honeybadger Insights can collect structured application logs and custom metrics, and its BadgerQL query language supports filtering and aggregation.
It worked for my test application's warning messages and checkout metrics.
Whether it can replace your existing logging platform depends on your retention, query, alerting, and data volume requirements.
Both reopened the resolved coupon error after it occurred again.
BugSnag recorded the newer application version as the reason for reopening.
Honeybadger returned the issue to Unresolved, but its History tab didn't contain a corresponding reopening entry.
Neither highlighted the returned issue directly in the main list.
It can.
Honeybadger offers a Resolve errors on deploy setting, which I enabled for release 1.2.0.
It automatically resolved existing errors in the deployed environment, including bugs that weren't actually fixed.
Those issues reopened when new occurrences arrived.
Both offer permanent free plans.
BugSnag's free plan includes 7,500 monthly error events, compared with Honeybadger's 5,000.
Honeybadger Team starts at $26 per month with annual billing and includes logs, uptime monitors, and a status page.
BugSnag's paid costs depend on event and span volume, so a reliable comparison requires estimating your application's usage.
BugSnag and Honeybadger caught the same six backend failure types, but their strengths became clearer once I looked beyond capture.
BugSnag gave me stronger error triage and release history. Its correlated events helped explain related failures, while automatic session stability scores provided a useful view of application health across versions.
Honeybadger offered broader operational coverage. Insights made logs and custom metrics accessible alongside errors, and its built-in uptime and check-in features could reduce the need for separate monitoring products. However, its broader scope came with trade-offs in grouping and automatic error resolution.
The deciding factor is what happens after the first error arrives. Choose BugSnag when exception investigation is the priority. Choose Honeybadger when you want errors and everyday operational monitoring under one subscription.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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