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Compare / Sentry

Sentry catches the exception. Unravel keeps its whole story.

Sentry is built around the error: stack traces and alerting it does superbly. Unravel treats that exception as one event inside the trace that caused it, with the query, the job and the user's journey attached.

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The honest split

What Sentry does well, and where it stops.

Credit where it's due

Error tracking

What Sentry is good at

Sentry is excellent at error tracking: it captures exceptions, groups them, attaches stack traces and breadcrumbs, and alerts you fast. Its alerting and release-tracking are mature, and it has a broad multi-language footprint. If your core need is “tell me when something throws, and show me the stack,” Sentry does that well.

The gap it leaves

Unravel

Where Unravel goes further

An exception is rarely the whole story; the N+1 above it, the job that dispatched it, and what the user did just before usually are. Sentry's performance tracing is sampled and organized around the error; Unravel is debugger-first and organized around the trace. The exception is one event inside a full causal chain: the queries it ran, the job it came from, the websocket frames it pushed, the same user's earlier requests. All of it is kept, and none of it is metered by volume.

At a glance

Sentry vs Unravel.

What each tool is built to do. Not a feature-checkbox race; the two answer different questions.

Center of gravity
Sentry

The exception: stack trace and grouping

Unravel

The trace - the exception is one event inside it

Context around an error
Sentry

Breadcrumbs and sampled performance spans

Unravel

The full causal chain: queries, jobs, sockets, user journey

Async work
Sentry

Captured, but not the spine of the product

Unravel

Jobs, scheduled tasks and Reverb traced end to end

Pricing model
Sentry

Priced per event / quota on volume

Unravel

Priced on retention and seats - volume never metered

AI agents
Sentry

MCP centred on issues and errors

Unravel

MCP over the full causal trace: spans, SQL, jobs, payloads

If Sentry's alerting is part of your workflow, keep it. Unravel has its own rules on new exceptions and error spikes, but the two coexist. Point both at the app and use Unravel when you need the full trace behind an error.

FAQ

The questions people actually ask.

Straight answers on how Unravel and Sentry compare.

Is Unravel a Sentry alternative for Laravel?

Yes, for teams whose main need is debugging rather than error cataloguing. Unravel detects exceptions and can alert on them, but instead of centering the product on the stack trace it reconstructs the full causal trace that led to the error: queries, jobs, websockets and the user's journey included.

Does Unravel do error tracking like Sentry?

It captures exceptions and attaches each one to the failing trace, and it has alerting rules for new exceptions and error spikes (Slack, email, webhook). Sentry's error-grouping and release tooling are more specialized; Unravel's strength is the surrounding context, not the exception catalogue itself.

Can I use Unravel alongside Sentry?

Yes. They capture independently and don't conflict. Many teams keep Sentry for its alerting and use Unravel when an error needs its full causal chain: the N+1 above it, the job that dispatched it, what the user did before.

What does Unravel show around an exception that Sentry doesn't center on?

The trace that caused it: the normalized query (and whether it was an N+1), the queued job or scheduled task it ran inside, the websocket frames in play, and the same user's earlier requests. One causal story rather than an error with breadcrumbs.

Compare further

How Unravel sits next to the rest.

Monitoring

Unravel vs Laravel Nightwatch

Laravel Nightwatch is monitoring: aggregates and charts across the whole app, priced per event. Unravel is a production debugger, built to take a single incident apart, and it never meters trace volume.

CompareLocal debugging

Unravel vs Laravel Telescope

Laravel Telescope is the debugger you already love locally. Unravel keeps that depth, runs safely against production, and ties the same signals into one causal trace, with history you can search.

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