Multi-Product Study

More Context.
More Fixes.
Better Fixes.

We studied 57,205 paid organizations over 90 days. Teams using more than just error monitoring resolve more issues and link more fixes to specific code changes.

The Study

57,205
Paid organizations analyzed
3.6M
Issues resolved
10
Languages & frameworks
90
Days of observation
Holds across all 10 languages & frameworks
Holds across all ARR tiers ($1–$120K+)
Strongest impact on small teams (1–5 users)

The Story

With more context, developers fix more issues with more precision.

Active Resolution

% of orgs resolving at least one issue

Errors
59.3%
Tracing
72.1%
Replays/Logs
79.5%
Seer (AI)
89.8%

+51% relative lift from errors-only to full context

Code-Linked Fixes

Fixes tied to the exact code change

Errors
3.9%
Tracing
6.5%
Replays/Logs
11.5%
Seer (AI)
45.4%

11.6× more code-linked fixes with Seer vs. errors-only

Developer Productivity

Issues resolved per team member (90d median)

Errors
1.0
Tracing
1.0
Replays/Logs
1.5
Seer (AI)
3.1

3.1× baseline with full context + Seer

Behind the Numbers

57,205

Paid Organizations Studied

Free and hobby accounts excluded. 90-day window. Minimum 50 distinct issues detected per org. Median org size: 6 members.

10

Languages and Frameworks

JavaScript (42%), Python (18%), PHP (11%), Node.js (9%), .NET (5%), Java (5%), Ruby (4%), iOS (3%), Go, Elixir.
Every finding holds across every stack.

1,862

Seer: Early but Real

1,862 paid orgs using Seer (AI debugger) so far. The 45.4% code-linked and 3.1 issues/member numbers are real, but early adopters aren't representative of the full population.

≠

The data is real. So is the caveat.

Small teams (1–5) see the sharpest lift: resolution goes from 59.3% to 89.8% with full context. But the pattern holds across all ARR tiers. Caveat: mature teams self-select into multi-product adoption.

One platform, not four tools.

Code breaks, fix it faster with Sentry.

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