Sentry vs Honeybadger: Hands-On Error Tracking Comparison for 2026

My third test release made checkout more than a thousand times slower, and nothing in the code threw an error about it. Sentry's trace named the database query responsible. Honeybadger has no tracing, yet the timestamps on its breadcrumbs left a 2.4-second gap in the same spot, and one query over my app's logs counted 44 slow-query warnings, every one from the new release.

Sentry is a broad monitoring platform that covers errors, tracing from the browser to the database, logs, profiling, session replay and an AI debugger called Seer. Honeybadger is a leaner error tracker whose plans, starting with a free one, also include a log and event store, uptime checks and status pages.

Those slow-query warnings existed only because my app writes them. Honeybadger shows you what your app chooses to report, and Sentry measures what your app does whether it reports anything or not. That split decides most of the sections below.

Honeybadger still took several rounds. Its error pages lay out the shopper and the request body in labelled boxes, and its groups stayed intact through a code change that gave Sentry two duplicate issues. Its $26 Team plan also covers logs, five uptime monitors and a status page.

I connected both tools to the same bookstore app in October 2026, planted the same bugs and shipped three releases to each. You'll see how they compare on setup, error capture, debugging, grouping, session replay, release tracking, performance, logs, uptime, AI and pricing.

Sentry vs Honeybadger: Results at a glance

I reached these verdicts on one Node.js app with my own configuration, so read them as the outcome of a single setup.

How I tested Sentry and Honeybadger

The test app is monitoring-lab, a bookstore I wrote to fail on demand. An Express 5 API on SQLite serves a Vite storefront, next to a flaky fake price service and a sales report job that fails once a minute.

Each tool plugs in through one small adapter on the server and one in the browser, so both received the same failures from the same code.

The planted problems fall into a few groups:

• Server crashes: a TypeError when a shopper enters an unknown coupon at checkout, which I call the coupon error below, and another when a book is missing.
• Server failures the app reports itself: an out-of-stock error, a 503 from the price service, an unhandled promise rejection from a recommendation call and the failing report job.
• Storefront errors: a crash on the wishlist button and four API requests that fail with different status codes.
• Slow spots: a slow book page, a CPU-heavy search, slow price lookups and a cover image that never loads.

A script played three shoppers, alice, bob and carol, through the API and in headless Chromium.

The three releases

I shipped three versions of the app to each tool.

The third release mattered most, because it hid two problems of different kinds.

The payment error was a capture problem, and it threw an exception both tools could see. The slow query was a measurement problem: checkout went from milliseconds to seconds and threw nothing at all.

That release shows whether a tool can only report failures or can also explain a slowdown nobody reported.

SDKs and test limitations

A few conditions shape the results:

• Seer had my GitHub repository. Honeybadger has no built-in AI to run against it, so the AI section isn't a head-to-head.
• Traffic ran separately for each tool. Event counts below aren't comparable one to one.
• My storefront reports production and my server reports development. That matters for Honeybadger, whose deploy settings work per environment.
• My test repository has a single commit. Features that blame a commit can't work properly here.
• Only Node.js and Express were tested. Grouping, instrumentation and profiling behave differently in other SDKs.
• Some features weren't tested. I didn't test uptime monitors, check-ins, status pages or alert emails in either tool, or Honeybadger's AI integrations.

Setup and onboarding

Both tools accepted the browser and the server in a single project, and both reached first data within minutes.

Each had one setup detail that could trip you up, and they point in opposite directions.

Sentry: One DSN, with every product switched on

During project creation, Sentry detected my GitHub repository and suggested Express and Browser JavaScript based on it.

The onboarding page then showed a snippet with my DSN already filled in, and a choice of what to collect:

Errors, logs, metrics, tracing and profiling all start switched on, so you collect a lot from the first request, including data you may not want to pay for yet.

The browser SDK sends to the same project and DSN as the server. The platform you pick only changes the snippet you're shown.

Honeybadger: One API key, and a development trap

Honeybadger's setup page put my API key inside the install snippet and showed a "Waiting for data…" panel until the first error arrived.

Honeybadger ignores the development environment by default. My Express server ran in development, so it sent nothing until I added reportData: true. The setup page does mention this, but it's easy to skip past when you test locally.

The same key worked for the browser build, and it also worked for uploading source maps and reporting deploys through the API on my trial.

Setup comparison

Verdict: Tie. Both tools put the whole app in one project quickly. Honeybadger's development default and Sentry's long list of products switched on at once are both worth a minute of attention.

Error capture

Error capture asks the simplest question: did the tool see every failure I planted?

Both did, including the unhandled promise rejection that some error trackers miss.

Sentry: 11 issues, with replay counts on browser rows

After the first release, Sentry's issues list held 11 issues from the server, the browser and the background job:

Browser rows carry a replay count, such as "▷ 7" on the wishlist error, so you can tell which issues come with a recording before you open them.

The unhandled rejection, "Recommendation engine timed out", arrived as its own issue with 6 events. The sales report job showed up too.

The broken cover image created no issue. It surfaced only as a 404 in a replay's Network tab, which you'll see in the replay section.

Honeybadger: 8 groups and one side effect

Honeybadger's list for the same release held 85 notifications in 8 groups:

Each row shows the environment, a one-hour sparkline, a count, an assignee picker and a resolve toggle. Browser rows say "production" and server rows say "development".

The lower group count comes from grouping, which the grouping section explains. Every planted error is in there, the recommendation timeout included.

There was one side effect. After Honeybadger's request handler reported the rejection, Express tried to answer the request a second time and my app logged ERR_HTTP_HEADERS_SENT. The server kept running, and the rejection was still reported.

Honeybadger didn't report the broken cover image either.

Error capture comparison

The counts come from separate traffic runs, so compare the groups and ignore the event totals.

Verdict: Tie. Neither tool missed a planted error. Sentry's only extra was the cover image, and it found that one through a replay.

Debugging an error

A captured error is only useful if the page around it tells you why it happened.

I compared source maps, server code, user and request context, breadcrumbs and whether each tool could connect a failed browser request to the server error behind it.

Sentry: Source lines on both sides, and a trace between them

I uploaded source maps with sentry-cli, using its inject and upload commands. The wishlist crash then pointed at the original file:

../../src/pages.js:122:13 is shown with wishlist.items.push(book.id); highlighted and my deliberate-bug comment above it. Sentry also offers "Set up Code Mapping" on the frame, which needs the repository connection.

Breadcrumbs on the same error tell you what the shopper did first:

The fetch to /books/2, the click with its CSS selector and my app's own "add to wishlist clicked" entry come right before the exception.

On the server, Sentry showed the code around each frame. Here's the payment error from the third release:

The lines around server.js:173 include the paymentAttempts % 5 check that fails every fifth payment, so the bug is readable without leaving the error page. A GitHub icon on the frame opens the file in the repository.

The bigger difference is the link between browser and server. When checkout fails, the storefront records one error and the server records another. Sentry connected them:

The Trace Preview on the browser's POST /checkout 500 says "1 other issue appears in this trace" and lists the server's coupon TypeError. The server issue shows the same link in reverse.

Opening the trace shows the request from the click down to the database:

The 12 spans run from the navigation and the "place order" click through the HTTP client and server spans to the handler and its db.query. The root lasted 64.90 ms, and web vital chips sit in the header.

The server error page also carried the browser side's recording:

The storefront passed its replay ID in the baggage header, so the coupon crash shows alice's session inline, next to the request body {"bookId":6,"coupon":"FREEBOOKS","quantity":1}. Further down that page you'll find Logs and Application Metrics inline, with values like browser.web_vital.lcp at 33.8 ms.

Honeybadger: Clear context, but no server code

Honeybadger accepted my source maps through its API with the project key. The storefront error then pointed at the right line:

src/api.js:29:28 is highlighted among lines 27 to 31, with the calling frame in pages.js:259 underneath.

On the server, Honeybadger gave a position with no code around it:

The frame reads server.js:150:45 behind a [PROJECT_ROOT] prefix. The location line, "development on MacBookPro.lan after deploy 1.0.0", does tie the error to a deploy.

Context is where Honeybadger shines. The coupon crash carried the shopper and her request in separate labelled sections:

You can see that carol hit the error and that she sent book 8 with the coupon save10. The "Email user" button lets you contact her from the error page, and the environment section below lists every HTTP header.

Browser breadcrumbs arrived without any extra code:

Clicks with selectors, "Page changed" navigations and a breadcrumb for each request sit next to my app's own "api call" and "navigation" entries.

Server breadcrumbs mostly stayed with their own request:

Only "checkout started" and "book found" from the failing checkout appear here. On a later payment error, though, a "Honeybadger Notice" breadcrumb from 51 seconds earlier slipped in from another request.

Both halves of a failed checkout sit in the same Honeybadger list, with nothing joining them. Honeybadger doesn't trace requests, so you match the browser error to the server error yourself.

Debugging comparison

Verdict: Sentry. Server source lines and the trace from a browser failure to its server cause save real time. Honeybadger's context layout is the easier one to read, but it leaves you to join the two halves of a failure by hand.

Honeybadger had both halves of the failed checkout and no way to join them, and Sentry spread the joins across a Trace Preview, a trace view and a replay panel. I also tested the same scenario in Better Stack, where one menu on the server exception holds every related view:

"View logs", "View spans", "View release" and "View session replays" all start from the crash, and "Investigate with AI SRE" sits at the bottom. The session itself puts both sides on one timeline:

Notice the 30 frontend spans and 7 backend spans in one session, so the shopper's clicks and the server work behind them line up. One concession: the browser tag is in beta, and my storefront stack stayed minified, because its docs don't cover browser source maps yet.

Follow one error from the click to the database

Error grouping

Grouping decides how many rows land in your list, and whether one row means one bug.

It can fail by merging different problems into one row or by splitting one problem across several. Each tool did one of those in my test.

Honeybadger: Four different failures in one group

My storefront sends every API call through one helper, which throws ApiError from src/api.js:29. Four different requests failed through it: a 404 on order tracking, a 500 at checkout, a 500 loading a book and a 502 from the price lookup.

Honeybadger groups by error class and the top backtrace line, so all four became one group:

The title reads "UnhandledPromiseRejectionWarning: ApiError: GET /orders/tracking…", yet the same group holds the checkout, book and price failures, and the title changes to whichever came last. The action bar above it includes Merge, Copy for LLM and Resolve on Deploy.

The same rule worked well on the third release. The new payment error includes the amount in its message, such as "rejected charge of 88 USD", and Honeybadger ignores the message:

All 7 notices share one group at server.js:173:15, linked "after deploy 1.2.0". The out-of-stock error behaved the same way, holding "The Phoenix Project", "Observability Engineering" and "Refactoring" in one group.

Sentry: Separate browser issues, duplicates after a code change

Sentry kept the four storefront failures as four issues, as the first issues list showed. Its trouble came in the third release:

The new payment error is one issue with 13 events, 3 users and 4 replays. Below it, a second coupon TypeError and a second out-of-stock error sit next to the originals.

The 1.2.0 checkout code changed the server stack, so Sentry opened new groups for errors it already knew. The browser's POST /checkout failed with 500 group went the other way: it's grouped by the browser stack, so the payment failures joined the old coupon group and took over its title.

Both tools let you correct the defaults. Honeybadger has a Merge action on every error, and Sentry supports custom fingerprint rules. Our Sentry vs Raygun comparison shows a tool splitting the same bug by amount and URL instead.

Error grouping comparison

Verdict: Mixed. Honeybadger stayed stable through the code change but hid four problems behind one shared helper. Sentry kept those apart and then duplicated two server errors once their stacks moved.

Real user monitoring and session replay

Replay shows what a shopper did before an error, and real user monitoring shows how fast pages loaded for them.

Only Sentry offers either, so this section is short on the Honeybadger side.

Sentry: Replays with AI summaries and a Web Vitals score

Sentry recorded every browser session at the default masking. Each replay opens on an AI summary:

The four steps match what the shopper did, and the error step, "Attempted to add book to wishlist and encountered a TypeError", is in red. Asterisks hide the page text unless you change the default, and the header totals up errors, rage clicks and dead clicks.

The Network tab is where the broken cover image finally appeared:

Each request has its method, status, path, type, size and duration, and the cover image shows up as a 404 img request. That was the only place either tool recorded it.

Sentry's Web Vitals dashboard scored the storefront overall:

The score is 90, with P75 LCP at 92 ms, INP at 8 ms, CLS at 0.0007 and TTFB at 2.51 s.

The per-page view was less helpful for my app:

Because the storefront routes with hashes like #/books/2, Sentry names every transaction /. That leaves a single Pageload row and a single Navigation row, and you'd need custom transaction names to isolate the slow book page.

Honeybadger: No replay and no RUM

Honeybadger's browser build captures errors and breadcrumbs. I saw no replay or browser performance product anywhere in its navigation.

You get the clicks and requests just before an error, and nothing about page speed.

Real user monitoring comparison

Verdict: Sentry. It's the only one of the two with replay or web vitals. Hash-routed apps need custom transaction names before its page view is useful.

Honeybadger can't tell you which page is slow, and Sentry folded every hash route into /. I also put that question to Better Stack's RUM Insights, a question box that hands your data to its AI SRE:

It ranked the hash routes one by one: /#/orders came out slowest, with LCP p50 714 ms, p75 1948 ms and p90 3663 ms over 8 loads. It then explained why:

Notice that it tied the browser timings to backend traces, named my CPU-heavy /search with 191 of 191 requests over a second, and ruled out the database. It wasn't perfect: it cost $0.62, called the slow price lookups handler code when they wait on I/O, and missed the slow book page.

See page speed and backend traces in one place

Release tracking and regressions

Release tracking should answer two questions: what did this deploy break, and did a fixed bug come back?

For the second release, I marked the coupon error as fixed in both tools and shipped it unfixed.

Sentry: Releases from the SDK and a Regressed label

Sentry reads the release from the SDK's release value, so there's no deploy step. Release 1.0.0 appeared on its own:

It shows 100% adoption, a 95.691% crash-free rate, 39 crashes and 11 new issues, with a banner offering "Add Release Health".

Resolving asks when the fix takes effect:

The options are a single commit, any existing release, the current semver release, or the release that follows. I chose "The next release", and the banner read "resolved starting with a release after 1.0.0".

When 1.1.0 sent the coupon error again, only that issue reopened, and the list said so:

"Regressed" sits under the trend graph, where the other rows say "New" or nothing.

The activity feed recorded it too, with one odd line:

It reads "Regressed in 1.1.0 compared with 1.1.0 based on SemVer", where you'd expect "compared with 1.0.0". The latest event came from an API shopper, so "A replay isn't available for this event" is expected.

The release page gives the regression its own tab:

The tabs read All Issues 11, New Issues 0, Unhandled 6, Regressed 1 and Resolved 0, and Regressed lists only the coupon error.

The overview for the same release adds health numbers and an empty commits panel:

The crash-free rate fell to 95.328%, and the failing transactions table leads with POST /checkout at 33 failures. Commits and Files Changed read 0, because I hadn't run sentry-cli releases set-commits yet.

Before 1.2.0, I ran set-commits --auto. The payment error page then showed a Suspect Commit, but my test repository has one commit holding all the code, and 1.2.0's bug sits behind an environment flag. In my setup, the suspect commit couldn't point at anything useful.

Honeybadger: Deploys by API and a silent reopen

Honeybadger ties errors to versions once you report deploys. I sent them through its API with the project key:

Each row has a Compare link to a GitHub comparison between revisions, which works only if your repository has matching tags. Mine had none.

Resolving uses a status toggle:

The toggle reads "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 part of that job.

Two minutes after I resolved it, the coupon error came back:

The status reads "We won't alert you again until the error is resolved", and the location line says "after deploy 1.1.0". In the list, the row looked like any other unresolved error, with no reopened label.

The History tab didn't record the comeback either:

It lists "Marked as resolved", the first occurrence and the previous deployment, and nothing about the return. You can work it out from the timestamps, since the latest occurrence is at 10:12 and the resolve at 10:10.

For the third release, I turned on "Resolve errors on deploy", which resolves every open error whenever you report a deploy. The server's errors cleared at once:

Only the two browser errors remain, because the setting works per environment and my storefront reports "production". One release needed two deploy calls.

After the second deploy and the 1.2.0 traffic, the list filled up again:

Every old error that still happened is back as Unresolved next to the new payment error. None of the rows tells you which ones returned and which one is new.

Release tracking comparison

Verdict: Sentry. It reads releases without deploy calls and marks a returning bug in the list, the activity feed and the release. Honeybadger's resolve-on-deploy gives each release a clean list, at the cost of knowing what came back.

Honeybadger reopened the coupon error without saying so, and Sentry flagged it but then opened duplicate issues for two old errors in the bad release. I also ran the same releases through Better Stack, where the comeback was visible straight from the list:

Only the coupon error carries the red "Reoccurred" status, so you see the regression before opening anything. Its page tells the story in order:

Notice "Error reoccurred, 3 minutes ago" above "resolved the error, 6 minutes ago". The coupon error also stayed one error across all three releases, and the payment error held 10 exceptions in one group, with no deploy calls. Sentry does have one thing it lacks: a separate Regressed tab per release.

Know the moment a fixed bug comes back

Performance monitoring

Some bad deploys never throw. Requests keep succeeding, just much more slowly.

In the third release, a slow SELECT book for checkout query dragged checkout from milliseconds to seconds. The two tools came at it from different ends.

Sentry: A trace that names the query

Sentry times every step inside a request, and a single 1.2.0 checkout was enough to show what happened:

The request took 2.26 s, and the SELECT book for checkout span took 1.79 s of it. The new "charge card" span ran for 459 ms and failed.

Trace Explorer showed that this wasn't one unlucky request:

The p95 for POST /checkout was 5.29 ms on 1.0.0, 3.63 ms on 1.1.0 and 5.93 s on 1.2.0, and the line jumps exactly at the new release.

Sentry's profiler was less useful here:

The slowest functions, buildReport and withSpan, sit around 1 ms each. A profile link from an early checkout trace failed with "Failed to fetch profiles", and my slow query waits instead of burning CPU, so a CPU profile wouldn't show it anyway.

Honeybadger: Clues in breadcrumbs and logs

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. The stray "Honeybadger Notice" from 51 seconds earlier is also on this list.

The second is Insights, Honeybadger's store for logs and events. My app logs a warning whenever a query is slow, so I counted warnings by message and revision with BadgerQL:

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 on 1.2.0.

That's the right diagnosis in one query, and it worked only because my app writes those warnings. An app that doesn't log slow queries would show Honeybadger nothing, and the Insights http: true setting gave me no request durations that I could find.

Performance comparison

Verdict: Sentry. It measured the slowdown and named the query without any help from my code. Honeybadger reached the same answer only because the app already logged it.

Honeybadger saw the slow query only through warnings my app happened to log, and Sentry kept those warnings on a separate tab of its trace. I also opened the same failing checkout in Better Stack, starting from the payment error itself:

"View spans" showed POST /checkout at 2.19 s, the SELECT book for checkout span at 1.53 s and "charge card" at 660 ms, with every middleware under a millisecond. "View logs" then opened Live tail for the same request:

Notice the slow-query warning beside ERROR POST /checkout failed, so the measurement and the app's own explanation sit one click apart. The Service column was empty, because the Sentry SDK I used doesn't set a service name.

Read traces and logs for the same request

Logs and events

Logs fill the gaps that errors and traces leave, from job output to business events.

Both tools store logs. They differ in how you get answers out of them.

Sentry: Logs tied to each trace

Sentry's Logs explorer held 129 lines from my app in the hour:

A colored dot marks each line's severity, and the report job's "sales report built" and "sales report failed" lines arrive once a minute.

Each trace also has a Logs tab:

The browser's "checkout partly failed" and the server's "POST /checkout failed" appear in order for that one request, each next to its error. I didn't run aggregate queries over Sentry's logs, so I can't compare that part.

Honeybadger: Insights and BadgerQL

Insights is included in Honeybadger's plans, and it holds logs, error notices and console lines in one place:

My adapter's log events and the SDK's console captures both appear here, so every log line was stored twice with console capture on.

BadgerQL handled more than warnings. One query summed my custom metrics:

It returned 55 orders, 1026.76 in checkout revenue and 11349.08 in report revenue, without another tool. The editor closes brackets and quotes for you as you type, which got in my way more than it helped.

Logs and events comparison

Verdict: Mixed. Sentry is better when you want the logs for one failing request. Honeybadger is better when you want to ask a question of all your logs at once.

Uptime, status pages and alerts

Honeybadger's plans include uptime monitors, check-ins for scheduled jobs and status pages, and Insights is built to hold uptime and check-in data alongside logs.

Even the free Developer plan includes one uptime monitor and one status page. The Team plan raises that to five monitors checked every two minutes.

I didn't test any of it. My sales report job could have sent check-ins, but that wasn't part of this run.

Sentry's pricing snapshot showed no status pages. It also lists cron and uptime monitoring, which I didn't test either.

I didn't check alert emails in either tool. Honeybadger's toggle promises an alert when a resolved error returns, and I can't confirm that it sent one.

Operational monitoring comparison

Verdict: Not compared. Honeybadger includes more of this on every plan, but I can't speak to how well any of it works.

AI-assisted debugging

This section isn't a head-to-head. Sentry has a built-in AI that I tested with my repository connected, and Honeybadger has no built-in AI at all.

Sentry: Seer, tested with my repository connected

I set Seer up with my GitHub repository before the second release:

One repository is connected, the automation stops after a PR is drafted, and Auto-Iterate on PRs is on.

On the coupon error, "Start Analysis" streamed its reasoning and returned a root cause in about 15 seconds:

It's correct: "The checkout handler accesses .discount on an unvalidated coupon lookup". It points to line 150, explains that FREEBOOKS returns undefined because COUPONS contains only SAVE10 and HALFOFF, and quotes lines from the repository.

Its plan was sensible too:

Its plan returns a 400 for coupons it doesn't recognize, adds optional chaining on line 150 and deletes my deliberate-bug comment. I didn't click "Yes, write a code fix", because it can draft a PR on the repository. Starting the plan also made me the issue's assignee.

When the coupon error regressed in 1.1.0, Seer's earlier work was still there:

The header says "Regressed", and the sidebar keeps the root cause and plan from 1.0.0 with an "Open Autofix" button.

On the new payment error, Seer took about 20 seconds:

It found the module-level paymentAttempts counter and the BAD_DEPLOY gate on version 1.2.0, and its reproduction steps were right. It explained the error only, and said nothing about the slow queries in the same release.

Seer Agent, Sentry's chat assistant in beta, took the broader question: "We just deployed monitoring-lab@1.2.0. Checkout feels slower and there's a new error. What changed compared to 1.1.0, and what is the root cause?"

It answered in 51.1 seconds, with no cost shown. Its chart put the checkout p50 at 1.7 ms before and 3.77 s after, and its count of 3 new issue groups includes the two duplicates.

The explanation under the numbers went wrong:

It said the latency is "driven by the upstream timeout on failed payment attempts". The trace shows the SELECT taking 1.79 s and "charge card" 459 ms, so the query is the bigger cost.

I asked which spans inside checkout got slower:

Its table is accurate and shows SELECT book for checkout rising from 0.1 ms to 2,640 ms. The reasoning still blamed payment retries and claimed that removing the charge card code "should restore all spans", although the queries run first and are slow on their own.

Honeybadger: Copy for LLM and an MCP server, untested

Honeybadger leaves the analysis to an assistant you already use. The "Copy for LLM" button in its error action bar copies an error for pasting into a chat.

Its MCP server lets assistants such as Claude Code or Cursor search your errors and run BadgerQL queries. I tested neither, so I can't say how well they work.

AI capabilities comparison

Verdict: Not compared. Seer was accurate on both code bugs and wrong about the slowdown, even with my repository. Honeybadger has nothing built in to test against it.

Honeybadger leaves the investigation to you, and Seer Agent misread the slowdown even with my code and a follow-up question. Better Stack's AI SRE got the identical prompt, with no access to the repository:

It saw the book query's p50 go from 0.13 ms to 2.6 s as the remaining statements held at roughly 0.1 ms, which let it clear the database host and label the slowdown a regression in the checkout code. Then it handled the second cause separately:

Notice the new "charge card" span failing 10 of 48 calls, all on 1.2.0, linked straight to the new payment error. It took about four minutes against Seer's 51 seconds and showed a cost of $0.49, but it was the only AI in my test that kept the two causes apart.

Hand your next bad deploy to AI SRE

Pricing

Sentry and Honeybadger price for different buyers. Sentry sells depth with usage quotas per product, while Honeybadger bundles more kinds of monitoring into each plan.

I took these numbers from both vendors' pricing pages on October 23, 2026. Both companies change their plans often, so check before you buy.

Free plans and trials

Entry-level paid plans

One level up, Sentry Business costs $89 monthly or $80 a month billed annually. Honeybadger Business costs $80 monthly or $880 a year, keeps errors for 180 days and checks uptime every minute.

What costs extra

Comparing the actual cost

On the entry plans, the two are nearly the same price, at $26 a month on annual billing for Sentry and $26 a month on monthly billing for Honeybadger.

What you get for that money differs. Honeybadger's Team plan covers errors, logs, five uptime monitors and a status page, so a small team may need nothing else.

Sentry's Team plan gives you tracing, replay and profiling quotas, and those decided the debugging and performance sections. To match Honeybadger's coverage, you'd still need a status page product, and Seer is a separate purchase.

The reverse also holds. If you need traces or replays with Honeybadger, you'll buy another tool for them. If you'd rather have errors, logs, uptime and on-call in one product, see how Sentry fares against an all-in-one platform.

Verdict: Honeybadger for small teams that value coverage. Sentry costs about the same at entry level and is the better buy if you'd pay for tracing and replay anyway.

Full feature comparison

This table combines everything I observed in the test.

Which should you choose?

The choice comes down to whether you want a tool that measures your app or one that organizes what your app tells it.

Choose Sentry if your releases go wrong by getting slower

Sentry earned its place in the sections where the cause wasn't written down anywhere. Its trace named the slow query in the bad release, and its Trace Preview joined each failed browser request to the server crash behind it.

It also marked the returning coupon error in three places, showed server code on the error page and attached the shopper's replay to server errors. Seer read my repository and got both code bugs right.

The trade-offs are real. Sentry opened duplicate issues when a code change moved two stacks, its page view lumped my hash routes into one row, and Seer Agent blamed the wrong step for the slowdown. You'll also need other products for status pages.

Our Sentry vs Rollbar comparison shows how Sentry fares against a more traditional error tracker.

Choose Honeybadger if you want errors, logs and uptime on one small bill

Honeybadger suits a small team that wants a quiet error list and a lot of coverage for $26 a month. It caught every planted error, laid out user context clearly and kept its groups stable through a code change.

Insights is the standout. One BadgerQL query found the slow checkout query by release, and another summed my order revenue, as long as my app had logged the data.

You give up tracing, replay and web vitals entirely. Its shared-helper grouping hid four failures in one row, its history didn't record the regression, and its development default can leave you wondering why nothing arrives.

If you're weighing it against a broader platform, our Honeybadger comparison covers one that adds tracing and replay.

Final thoughts

The real decision is who does the measuring. Honeybadger is a tidy, generous error tracker that reports what your code tells it, with logs, uptime and a status page included. Sentry measures your app whether your code says anything or not, and it asks for more setup and more attention in return.

Most teams don't log every slow query they'll ever have. My bad release slowed checkout from milliseconds to seconds, and Honeybadger found the cause only because I'd planted a warning for it.

If your code logs its own slow spots, Honeybadger will find them. If it doesn't, only one of these tools will.

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