If you run a small product with a small team, you probably want one monitoring bill, one dashboard and as little setup as possible. Honeybadger is built for exactly that buyer. Its free plan bundles error tracking with a log and event store, an uptime monitor and a status page. Sentry offers more depth, especially for performance, and asks for more of your attention in return.
To see what that trade means in practice, I connected both to the same small web shop and shipped a release that made checkout slow and broke one in five payments. Both tools caught the new payment error. For the slowdown, Sentry handed me the answer, while Honeybadger left me clues. Sentry's trace named the database query responsible. Honeybadger has no tracing, yet the timestamps on its breadcrumbs showed a gap of more than two seconds in the same place, and a query over my app's logs counted 44 slow-query warnings, every one from the new release.
Those clues only existed because my app logs its own warnings. That's the real difference between these two products, and it comes up again and again below. Honeybadger shows you what your app reports. Sentry measures what your app does.
In the sections that follow, you'll see both tools take on the same tasks: capturing and debugging errors, grouping them, tracking releases, finding a slowdown, recording user sessions, AI help and price. Each section closes with a verdict. All results come from my own test in October 2026.
| Area | Who did better | Main reason |
|---|---|---|
| Setup | Tie | Both took one project for browser and server; each had one gotcha |
| Capturing errors | Tie | Neither missed a planted error, uncaught promise included |
| Debugging one error | Sentry | Shows server code and joins each browser failure to its server cause |
| Grouping | Depends on the bug | Honeybadger merged four different browser failures; it kept the payment error whole |
| Release tracking | Sentry | Regression labels; Honeybadger's history didn't record the reopen |
| Slowdowns and logs | Sentry on speed, Honeybadger on log queries | Sentry traces requests; Honeybadger's Insights queries logs with BadgerQL |
| Session replay | Sentry | Honeybadger has no replay or real user monitoring |
| AI | Not compared | Seer tested with code access; Honeybadger has no built-in AI |
| Price | Honeybadger for small teams | Free uptime, status page and logs; Team plan $26 a month |
The app is monitoring-lab, a bookstore I wrote to fail on demand. An Express 5 API on SQLite serves a Vite storefront, with a deliberately flaky price service and a reporting job that errors out once a minute. Each tool connects through a small adapter on the server and another in the browser, so both received the same failures from the same code.
What breaks, by design:
A script played three shoppers, alice, bob and carol, through the API and in headless Chromium while I shipped three versions:
| Version | Purpose |
|---|---|
| 1.0.0 | Installation, capture from browser and server, source maps, breadcrumbs and context |
| 1.1.0 | A coupon crash marked as fixed, then shipped again unfixed |
| 1.2.0 | A bad release with a sluggish checkout query and a payment failure on every fifth charge |
Sentry ran @sentry/node and @sentry/browser 11.8.0. Honeybadger ran @honeybadger-io/js 6.17.0, a single package with separate server and browser builds, sending both sides to one project with one API key.
Some conditions affect the results:
The third version is worth a closer look, because it tests two different skills at once.
Its new payment error was a capture problem, and both tools solved it. Each grouped the failures, linked them to the new version and showed the request that triggered them.
Its slowdown was a measurement problem. Checkout went from milliseconds to seconds, and nothing threw an error. Sentry measures request timing, so its trace pointed straight at a 1.79 s database query. Honeybadger doesn't measure request timing, so the slowdown only appeared indirectly: in the gaps between breadcrumbs, and in warnings my app happened to log. The release tracking and performance sections walk through each tool's view.
Both tools put the browser and the server in one project, and both reached first data quickly.
Sentry detected my GitHub repository while creating the project, and its setup snippet arrived with my DSN in place and tracing, logs and profiling switched on.
Honeybadger's setup page showed my API key inside the snippet and waited for data with a "Waiting for data…" panel. It had one surprise. Honeybadger ignores the "development" environment by default, so my server sent nothing until I added reportData: true. The setup page does say this, but it's easy to miss when you're testing locally.
Once both were running, they caught the same set of errors, including the server's uncaught promise rejection. Here's Honeybadger's error list after the first release:
Each row shows its environment, an hourly sparkline, a count, an assignee picker and a resolve toggle, which is all you need for daily triage. There are 8 groups against Sentry's 11, and the grouping section explains why. Sentry's list for the same release looks like this:
Here the four failed storefront requests stay as four issues, and the browser rows show how many recordings are attached.
Neither tool reported the cover image that never loads.
Verdict: a tie. Both captured everything, and each had one small setup trap: Honeybadger's development default, and the extra products Sentry turns on that you may not want yet.
Both tools turned my minified storefront code back into readable source, and both let me upload source maps on a trial account. Honeybadger accepted them through its API with the same project key the app uses, no paid token needed. Its storefront error pointed at the right file and line:
src/api.js:29:28 sits highlighted among the surrounding lines, with the next frame in pages.js below it, so you read the failure without opening your editor.
On the server, the tools split. Sentry showed the code around each frame of the server stack trace, as on this payment error from the third release:
The lines around server.js:173 include the check that fails every fifth payment, so the bug is readable on the error page. Honeybadger showed the file and position for the coupon crash, server.js:150:45 behind a [PROJECT_ROOT] prefix, with no code lines:
The summary does tie the error to a release, though. Its location line reads "development on MacBookPro.lan after deploy 1.0.0".
Context is where Honeybadger does well. The server error for the coupon crash carried the shopper's identity and the request body:
You can see who hit the error, carol, and exactly what she sent: book 8 and the coupon save10. The "Email user" button lets you contact the affected customer straight from the error page.
Breadcrumbs were good on both sides. In the storefront, Honeybadger recorded them automatically:
You see clicks with their selectors, "Page changed" navigations and a breadcrumb for every request, next to the app's own entries. On the server, breadcrumbs stayed with their own request:
Only "checkout started" and "book found" appear, both from the failing checkout, with no other shoppers' requests mixed in.
One flaw showed up later. On a payment error from the third release, the breadcrumb list included a "Honeybadger Notice" from 51 seconds earlier, which belonged to a different request. Most breadcrumbs stayed with their own request, but not every one.
When the shop's checkout request fails, the browser records one error and the server another. Both tools had both errors in the same project, but only Sentry connected them. On the storefront's failed checkout, Sentry's Trace Preview listed the server crash that caused it:
The line "1 other issue appears in this trace" leads to the coupon crash on the server, and the trace behind it shows every step from the click to the database.
Honeybadger keeps the two errors side by side in one list, with nothing joining them, because it doesn't trace requests.
Verdict: Sentry. Server source lines and the browser-to-server link save real time. Honeybadger's context is excellent, and the "Email user" button is a nice extra.
Honeybadger had both halves of the failed checkout in one project and no way to connect them. Better Stack connects them from the error itself. Its "View related" menu on a server exception offers the request's logs, its spans, other errors, the release and the browser session replays from the same visit:
Pick "View session replays" and you watch what the shopper did before the server crashed. Honeybadger has no replay to offer, and Sentry spreads the same links across separate tabs.
Honeybadger groups errors by their type and the place in the code where they were thrown. That rule produced one result I liked and one I didn't.
The downside showed up in the storefront. Four different failed requests, a 404 on order tracking, a 500 at checkout, a 500 loading a book and a 502 from the price lookup, all go through the same helper, so they all throw ApiError from src/api.js:29. Honeybadger put them in one group:
The title reads "ApiError: GET /orders/tracking…", but the same group holds the checkout, book and price failures too, and the title changes to whichever happened last. Four different problems with four different owners end up in one row. Sentry kept them as four issues.
The upside showed up in the third release. Its new payment error includes the failed amount in its message, such as "rejected charge of 88 USD". Honeybadger ignores the message when grouping, so every amount landed in one group:
All 7 notices sit in one group, linked "after deploy 1.2.0". The out-of-stock error behaved the same way, with three different books in one group. Sentry also kept the payment error whole, but it opened duplicate issues for the coupon crash and the out-of-stock error in that release, since the reworked checkout altered how their stacks looked. I saw no such duplicates in Honeybadger.
This doesn't make one rule better everywhere. Honeybadger's rule suits apps that throw distinct error types from distinct places. It suits apps that route many failures through one shared helper much less, which is common for API clients. Honeybadger's Merge action and Sentry's fingerprint rules can each correct the defaults. My Sentry vs Raygun test showed the opposite problem, with one bug split by amount and URL.
Verdict: it depends on how your code throws. Honeybadger stayed calmer across the code change, while Sentry kept separate problems apart in the shared request helper.
Both tools tie errors to versions, but Honeybadger needs deploys to be reported. I sent them through its API with the project key, and each deploy appeared in a list with a Compare link:
The Compare link opens a GitHub comparison between two revisions, which works only if your repository has tags with those names. Mine didn't. Sentry picks up versions from its SDK without a deploy call.
For the second version, I marked the coupon crash as fixed in both tools, then shipped it unfixed.
Resolving works differently in each. Sentry's Resolve menu asks when the fix takes effect:
You can pick the next release, any named release or a single commit. Honeybadger has a simple Status toggle:
"Resolved. We'll alert you if this error happens again." There's no option to resolve for a future version, though a Resolve on Deploy button and a project-wide setting cover that use, as you'll see below.
Both tools reopened the coupon crash as soon as the unfixed version sent it. Sentry marks the comeback in the issue list:
The "Regressed" flag stands out from the rest of the list. In Honeybadger, the error page simply went back to Unresolved:
The status reads "We won't alert you again until the error is resolved", and the location line says "after deploy 1.1.0". Its row in the list carried no reopened label, and the error's History tab didn't record that it had come back:
The tab lists "Marked as resolved", the first occurrence and the previous deployment, and nothing about the error returning. You can still work it out from the timestamps, since the latest occurrence came two minutes after I resolved it.
Honeybadger's "Resolve errors on deploy" setting resolves every open error whenever you report a deploy. Errors that still happen come back as Unresolved, so the list after a release holds only what that release produced. With it on, the third version's deploy cleared the server's errors at once:
Only the two browser errors remain, because the setting applies per environment and my storefront reports "production". I had to send a second deploy for the browser. Once traffic ran, the list filled up again:
Every old error that still occurred is back as Unresolved, next to the new payment error. None of the rows shows which ones returned and which one is new.
Verdict: Sentry. It reads versions without deploy calls and labels regressions clearly. Honeybadger's resolve-on-deploy is a neat way to get a clean list per release, but you lose the history of what returned.
Honeybadger reopened the coupon crash without saying so anywhere, neither in the list nor in its History tab. Better Stack recorded the return in both places. The list showed a red "Reoccurred" status, and the error's activity feed told the story in order:
"Error reoccurred, 3 minutes ago", "resolved the error, 6 minutes ago" and "Error appeared". Releases came from the SDK, so I sent no deploy calls and didn't need one per environment.
In the third release, checkout became more than a thousand times slower without throwing anything. The two tools approach a problem like that from different directions.
Sentry's tracing records how long every step of a request takes. One checkout from the bad release told the whole story:
The request lasted 2.26 s, and the SELECT book for checkout query took 1.79 s of that. A new payment step ran for 459 ms and failed. A per-version chart in Trace Explorer then showed it wasn't a one-off:
The 95th percentile checkout time was 3.63 ms on version 1.1.0 and 5.93 s on 1.2.0. Sentry also keeps your logs, and each trace has a Logs tab:
The browser's and the server's log lines for that request appear in order, each next to its error.
Honeybadger has no tracing, but two features got me close.
The first is breadcrumb timing. Each breadcrumb on an error shows how long before the error it happened:
"checkout started" came 2.96 s before the error and "book found" 0.55 s before, so about 2.4 s passed while the app loaded one book. That points at the book query, if you know to look.
The second is Insights, Honeybadger's store for logs and events, queried with a language called BadgerQL. My app logs a warning whenever a query is slow, so I counted warnings by message and version:
The query filter level::str == "warn" | stats count() as count by message::str, revision::str returned 44 "slow query: SELECT book for checkout" warnings and 36 "payment gateway slow to respond, retrying" warnings, all from version 1.2.0. That's the right diagnosis, and it took me one query.
It worked only because my app writes those warnings. An app that doesn't log slow queries would show Honeybadger nothing, and I couldn't find request durations from the Insights HTTP setting in this run.
Insights is still useful beyond this case. It held my app's logs, error notices and custom metrics in one place, and one BadgerQL query summed my checkout revenue and order counts:
The results include 55 orders and 1026.76 in checkout revenue, with no extra tooling. Two small annoyances: the editor closes brackets and quotes for you, which fights you as you type, and with console capture on, each log line was stored twice.
Verdict: Sentry for finding slowdowns, since it measures them whether or not your code logs anything. Honeybadger for querying logs and events you already emit, which its plans include at no extra cost.
Honeybadger found the slow query only because my app happened to log it, and Sentry kept logs and traces on separate tabs of the trace. Better Stack had both views one click from the payment error. The trace waterfall showed the slow query inside the failing checkout:
Then "View logs" opened Live tail filtered to the same request, where the slow-query warning sat beside the error:
You get the measurement Honeybadger lacks and the logs Sentry keeps on another tab, from the same error.
This section is short, because only one tool competes. Honeybadger has no session replay and no real user monitoring, so you can't see what a shopper did or how fast their pages loaded.
Sentry records sessions in the browser and writes an AI summary of each one:
The summary's four steps matched what the shopper did, with the error step marked in red. Sentry's Web Vitals dashboard scored my storefront 90, though my hash-based routes all showed as one page. Replays also attach to server errors, so the coupon crash on the server came with the recording of the browser session that triggered it.
Verdict: Sentry, uncontested. Plans include 50 replays a month, and you pay for more.
Honeybadger has no built-in AI debugger. What it offers is a "Copy for LLM" button that copies an error for pasting into your own assistant, and an MCP server that lets assistants such as Claude Code or Cursor search your errors and run BadgerQL queries. I tested neither, so I can't rate them.
Sentry's Seer, with my repository connected, was accurate on both crashes it analyzed. For the coupon crash, it found in about 15 seconds that FREEBOOKS isn't a valid coupon and that the code reads a discount from an empty lookup. For the payment error, it found the version flag and the counter that fails every fifth charge:
The cause is read straight from the code, with the relevant lines quoted. Seer Agent, Sentry's chat assistant in beta, was less reliable when I asked why the bad release was slower:
It quoted the right timings, 1.7 ms to 3.77 s at the median, and blamed the payment retries, which the trace shows run after the slow query.
Verdict: Sentry is the only one of the two with an AI that analyzes errors for you, and Seer did well on code bugs. Honeybadger's route is to hand the error to an assistant you already use.
Honeybadger leaves the investigation to you, and Sentry's Seer Agent misread the slowdown even with my code. Better Stack's AI SRE took the same question about the bad release, without repository access, and worked through releases, errors, traces and logs on its own:
It found the book query's median climbing from 0.13 ms to 2.6 s as the remaining queries held steady, and it ruled out the database server. Separately, it traced the new payment step's 10 failures in 48 calls to the new error. It took about four minutes and showed its cost of $0.49, and it was the only AI in my tests that got both causes right.
These are the published prices on October 23, 2026, from each vendor's pricing page. Honeybadger and Sentry both adjust plans often, so check before you buy.
| Sentry Developer | Honeybadger Developer | |
|---|---|---|
| Users | 1 | 1 |
| Errors a month | 5,000 | 5,000 |
| Error retention | Not published | 15 days |
| Logs | 5 GB | Insights: 50 MB a day, kept 7 days |
| Tracing | 5M spans | ✘: not offered |
| Session replay | 50 replays | ✘: not offered |
| Uptime monitoring | Not shown in this snapshot | ✔: 1 monitor, every 5 minutes |
| Status pages | ✘: not included | ✔: 1 |
| Sentry Team | Honeybadger Team | |
|---|---|---|
| Price | $29 monthly, $26 a month billed annually | $26 monthly, $286 a year billed annually |
| Users | Unlimited | Unlimited |
| Errors a month | 50,000 | 50,000 |
| Error retention | Not published | 90 days |
| Logs | 5 GB | Insights: 100 MB a day |
| Tracing | 5M spans | ✘: not offered |
| Session replay | 50 replays | ✘: not offered |
| Uptime monitoring | Not covered in this snapshot | ✔: 5 monitors, every 2 minutes |
| Trial | 14-day Business trial | 30 days |
One level up, Sentry Business costs $89 monthly or $80 a month annually. Honeybadger Business costs $80 monthly or $880 a year, keeps errors for 180 days and checks uptime every minute.
| Need | Sentry | Honeybadger |
|---|---|---|
| More errors | Pay-as-you-go budget | Optional overage billing per notification |
| More logs | $0.50 per GB over 5 GB | Logging and Performance add-on, priced in the plan calculator |
| Tracing and replay | Quotas included, overage billed as used | ✘: not offered |
| Profiling | From $0.25 per UI profiling hour | ✘: not offered |
| AI | Seer sold separately, no public price | No built-in AI; MCP server and Copy for LLM |
For a small team, Honeybadger gives you more operational coverage for the money: errors, logs, uptime checks and a status page on one $26 plan. Sentry's $26 annual-rate Team plan gives you deeper debugging instead, with tracing, replay and profiling, but no status page. If you'd rather have errors, logs, uptime and on-call in one product, see how Sentry fares against an all-in-one platform and how Honeybadger compares.
Verdict: Honeybadger for small teams that value breadth over depth. Sentry if you'd pay for tracing and replay anyway.
| Feature | Sentry | Honeybadger |
|---|---|---|
| Browser and server in one project | ✔: one DSN | ✔: one API key |
| Development environment reported by default | ✔: yes | ✘: needs reportData |
| Uncaught promise rejections, Node | ✔: caught | ✔: caught |
| Source maps on a trial | ✔: CLI upload | ✔: API upload with the project key |
| Server source lines | ✔: shown | ✘: file and position only |
| Request body and user context | ✔: shown | ✔: shown, plus Email user |
| Browser to server link | ✔: shared trace | ✘: same project, no link |
| Grouping rule | Stack-based, configurable | Error type and code location |
| Releases | ✔: from the SDK | ✔: deploys reported by API |
| Resolve for a future version | ✔: next release, a release or a commit | ✘: toggle, or resolve on deploy |
| Regression shown in the list | ✔: Regressed | ✘: back to Unresolved |
| Regression recorded in history | ✔: activity feed | ✘: not recorded |
| Resolve everything on deploy | ✘: not found | ✔: per environment |
| Request tracing | ✔: browser to database | ✘: not offered |
| Logs and event queries | ✔: logs linked to traces | ✔: Insights with BadgerQL |
| Session replay | ✔: with AI summaries | ✘: not offered |
| Web vitals | ✔: dashboard | ✘: not offered |
| Uptime, check-ins, status pages | Cron and uptime monitors, untested | ✔: included, untested |
| Built-in AI | ✔: Seer, tested | ✘: Copy for LLM only |
| MCP server | ✔: exists, untested | ✔: exists, untested |
Choose Honeybadger if you're a small team that wants one inexpensive tool covering errors, searchable logs, uptime checks and a public status page, and your app already logs the things you'd want to know about. Its context, its deploy-based workflow and its plain interface make daily triage quick. If your code sends many failures through one shared helper, plan to merge or split groups early.
Choose Sentry if your hardest bugs span the browser and the server, or if your releases go wrong by getting slower. It measured the slowdown that Honeybadger could only hint at, and it linked each browser failure to its server cause. You'll pay for that with a busier interface, and you'll need other tools for uptime and status pages. My Sentry vs Rollbar comparison covers how Sentry fares against a more traditional error tracker.
All of this ran on Node.js. Both vendors support many more languages, and features like grouping and Sentry's profiling vary between SDKs.
The decision comes down to who does the measuring. Honeybadger is a tidy, generous error tracker that shows you what your app chooses to report, plus logs, uptime and a status page for very little money. Sentry measures your app whether or not it reports anything, and charges more for the privilege.
If your code logs its own slow spots, Honeybadger will find them. If it doesn't, only one of these tools will.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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