Multi-Product Study

More Context.
More Fixes.
Better Fixes.

We studied 57,205 customers over 90 days. The TL;DR: Teams using more than just error monitoring resolve more issues and link more fixes to specific code changes.

The Study

57,205
Customers analyzed
3.6M
Issues resolved
10
Languages & frameworks
90
Days of observation
Holds across all 10 languages & frameworks
Holds across all levels of customer spend ($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 customers 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

Issues connected to the commit

Root cause identified at the code level

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

11.6× more issues had code-level root cause identified 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

Customers Studied

Free and hobby accounts excluded. 90-day window. Minimum 50 distinct issues detected per customer. Median customer 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 of a subset of our beta users using Seer (AI debugger) within a 90-day period. 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 customer sizes. Caveat: mature teams self-select into multi-product adoption.

One platform, not four tools.

Code breaks, fix it faster with Sentry.