Sentry tells you an error happened. Struct tells you where, why, and what to do about it.

Struct and Sentry Seer are aimed at the same problem: when production breaks, an AI - not an engineer - should do the first pass of the investigation. Where they differ is scope. Seer is Sentry’s AI debugger, and it's good at what it does: root-causing and fixing errors using the data already inside Sentry. Struct investigates across your entire production stack - Sentry plus code, Datadog, infra/CloudWatch, GitHub, logs, traces, and past investgiations - and posts a cited root-cause hypothesis to Slack within minutes of any alert firing.

Struct is designed to work with Sentry

Sentry is an error-monitoring platform - you add its SDK to your application, and it captures errors, crashes, and slow transactions as they happen, with full stack traces, and groups them into issues so you know exactly what broke in your code. Struct doesn't replace that: it's a complementary agentic layer that ties together all your production context.

Struct ingests your Sentry issues the moment they fire and uses them as a primary input, then keeps going, correlating across Datadog metrics, cloud infrastructure, GitHub deploy history, and any other tool an on-call engineer would normally open mid-incident. Most Struct customers keep Sentry and add Struct on top for cross-stack investigation (though they usually do turn off Seer because it becomes redundant).

What dimensions matter in a production RCA tool?

Investigation surface

Struct: Reads the alert payload, then queries Sentry errors + stack traces, Datadog metrics + logs + traces, cloud infrastructure, GitHub deploy diffs, and any other connected system. Correlates findings into one hypothesis with citations to specific log lines, metric anomalies, and commits, and lets you fix simple issues in one click.

Sentry Seer: Investigates using the telemetry Sentry already collects - errors, spans, and profiles, with application logs and custom metrics still rolling out in alpha. It's good at code-level root cause for problems Sentry can see. It does not autonomously investigate an infra alarm, a Datadog monitor, or a CloudWatch metric that never became a Sentry issue.

Model quality

Struct: Uses frontier models for the entire investigation — reading telemetry, correlating across tools, and writing the hypothesis.

Sentry Seer: Seer's core root-cause/autofix pipeline runs on Sentry's own models over Sentry-scoped data. Sentry added a Claude-powered agent in Jan 2026, but that frontier-model handoff is scoped to drafting code fixes and PRs after the root cause is found — not to the cross-stack diagnosis itself.

Trigger

Struct: Every alert fires an investigation automatically, with intelligent auto-deduping (even after grouping, Sentry is often prone to duplicating alerts). The engineer wakes up to a Slack message containing the hypothesis, not an empty channel.

Sentry Seer: Automation is gated on Seer's actionability score — you configure a minimum threshold per project for auto root-cause, code-gen, or PRs. Issues below that bar wait for an engineer to open the issue and click "Find Root Cause." Great for repetitive, high-confidence bugs; manual for the long tail.

Pricing model

Seer bills $40 per active contributor per month (an active contributor = anyone with 2+ PRs to a Seer-enabled repo), on top of a Sentry Business ($80/mo) or Enterprise plan. For a small team with a lot of alerts where only a handful of engineers contribute code, that can be pretty cheap. But it scales with headcount, and it only covers Sentry-visible work. Struct uses credit-based pricing, and that spend buys investigation across the whole stack, not just the errors Sentry captured.

Seer is an in-product debugger. Struct is a cross-stack on-call investigator.

The framing matters. Seer is a great tool for fixing a bug that already surfaced as a Sentry error. Struct is a great tool for an engineer who just got paged and doesn't yet know whether the cause is code, a bad deploy, a saturated queue, or an infra failure three services away.

Seer's strengths are real: it's built directly into Sentry, integration is trivial if you already run Sentry, and setup is fast. The limit is the boundary - Seer can only investigate what Sentry already sees. Most production incidents don't respect that boundary.

When should I choose Sentry Seer?

If the bulk of your incidents are application errors that already land in Sentry, your team lives in the Sentry UI, and you want AI fixes and PRs wired directly into your existing error workflow, Seer is a strong, low-friction add-on. The seat-based price can be very economical for small contributor counts, and the actionability-scored autofix is useful for repetitive, well-defined bugs.

When should I choose Struct over Sentry Seer?

Struct is the right pick if:

• You already run Sentry (and maybe Seer) and engineers are still spending 30 minutes per alert correlating across logs, metrics, infra, and deploys.

• Your incidents regularly cross out of Sentry — into Datadog, CloudWatch, queues, or recent deploys — where Seer can't follow.

• You want auto-investigation on every alert, not just issues above a fixability threshold.

• You want pricing that scales with workload, not engineer headcount.

• Your engineers' biggest complaint is "we get the Sentry alert and still spend 30 minutes figuring out what actually broke."

How do most teams run both Sentry and Seer?

Arcana (Series B fintech, 40 engineers) uses Sentry, but replaced Seer with Struct:

• Sentry captures the error; Struct investigates it.

• Struct's investigation report lands in Slack minutes after the error — correlating it with Datadog, infra, and the deploy that introduced it.

• The engineer reads one cited diagnosis directly in Slack.

Bottom line

Seer is an AI debugger inside Sentry - fast to enable, seat-priced, and effective on errors Sentry already captured. But it investigates within Sentry's data boundary, gates automation on a proprietary fixability score, and reserves frontier models for the code-fix step. Struct is a cross-stack on-call investigator: frontier models end to end, automatic on every alert, Slack-native, with a cited root-cause hypothesis across your whole stack in ~2 minutes. Most teams keep Sentry and add Struct.

Information about Sentry Seer is based on publicly available data as of June 2026 and may not reflect the most current features or pricing. All third-party trademarks referenced on this page are the property of their respective owners. Struct is not affiliated with, endorsed by, or sponsored by Sentry.