Multi-Product Study

Fix more bugs.
Fix them faster.

We watched 57,205 teams for 90 days. The ones using Tracing, Replays, and Seer fix bugs faster — and actually know which code caused them.

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

More context = more bugs fixed, faster.

Teams fixing at least one bug

9 in 10 teams fix issues with Seer — vs. 6 in 10 with errors alone.

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

51% more teams fix bugs when they have full context.

Bugs traced to the exact line of code

Seer pinpoints the broken code 11× more often.

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

Seer finds the guilty code 11× more often than errors alone.

Bugs fixed per developer

With full context — 3× more fixes per person.

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

Devs with Seer fix 3× more bugs than devs with errors alone.

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.

Get demo