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Vercel — Engineering Performance

VercelEngineering capacity
+113engineers above headcount

50 engineers now deliver what 163 would have in Q1 2025.

Effective engineersReal engineers
Apr 2025Dec 2025Sep 2026
Where the work wentBiggest shift: Fixes down 5 points
Features30%Maintenance16%Tests30%Docs8%Fixes16%

Performance snapshot

Today's rolling 90-day reading for Vercel, compared with the start of the series. Pick a window to move that comparison point.

Avg. perf / dev / mo

+226.5%

1.54 → 5.04 ETV

Active engineers

+4.2%

48.0 → 50.0

Features

+4.6pp

25.7% → 30.3%

vs. 500 OSS index

1.3x

1.7x → 1.3x · +31% above

Engineering capacity

Effective engineers behind Vercel, against its pre-AI baseline. Each subject has its own: Vercel's is 1.54 ETV / dev / mo, its first reading in Q1 2025. Per-engineer ETV divided by that gives a capacity multiple, and that multiple applied to the engineers active in the trailing 90 days turns it into engineer-equivalents. The line is the real headcount, so the gap between line and area is what the leverage is worth. Because each baseline is its own, every subject opens at 1.0x on its first day: multiples measure improvement and are not comparable between subjects.

Effective engineers
163engineer-equivalents
+113 engineers above real headcount
Real engineers
50engineers
active in the trailing 90 days
Capacity vs pre-AI
3.3x
per engineer, vs 1.54 pre-AI

Vercel vs. 500 OSS Performance Index

Per-engineer ETV for Vercel against the pooled 500 OSS Performance Index. Both lines are 90-day rolling averages scaled to a 30-day month, so they share one axis and can be read against each other at any point. Pick a window to zoom the chart to it. Latest reading: Vercel is 31% above the index (5.04 vs 3.85 ETV/dev/mo). At the start of the tracked period the gap was 68% above.

Vercel
5.04ETV / dev / mo
+1.82 (+56.5%) past 90d
500 OSS Performance Index
3.85ETV / dev / mo
+1.66 (+75.8%) past 90d

Behind the numbers

Aug

Written summary of the work completed each month.

In August 2026, the team recorded 422 commits with 51 Features, 43 Tests, 25 Fixes, 19 Maintenance, and 18 Docs, reflecting steady delivery in line with the recent run rate. Key accomplishments included enabling export-name mangling in Turbopack, expanding AI SDK provider capabilities across Bedrock and Azure, and refining Next.js cache and prerendering mechanics. Under the provisional evaluation threshold, Docs score rose 89% above the 5-month average while Maintenance score decreased by 35% compared to the 5-month average.

Highlights

  • Added export-name mangling to Turbopack (a84bc8de (Tobias)) and enabled it by default in canary production builds (6f44fd34 (Tobias)) to reduce bundle sizes.
  • Optimized turbo-persistence key lookups in SST blocks without hashes (33a5d542 (Luke)), improving lookup performance by up to 30%.
  • Enhanced the AI SDK Core with strict tool choice validation and error handling in 8b6b756a (Aayush), and exposed parsed structured output to onEnd/onFinish stream callbacks in 6669d691 (Lars).
  • Introduced granular cache keys incorporating runtime hashes and manifests for use-cache in Next.js via e3790aa9 (Niklas) and exposed durableUseCacheEntries configuration in 3d85af96 (Niklas).
  • Expanded Turborepo lockfile package metadata support for single-package repositories across major package managers in 2c84c66d (Anthony) and added root Cargo workspace support in e732034b (Anthony).

Observations

  • Docs score saw a notable increase to 18 vs the 9-point 5-month average (+89%), driven by extensive documentation updates for AI SDK providers and Turborepo features.
  • Maintenance score dropped to 19 compared to the 29-point 5-month average (-35%), while overall output (157) remained steady relative to the 5-month average of 155 (+1%).
  • CI test flakiness required repeated intervention in root sync IO tests, including skipping in 0a2bd1d5 (Janka) and reverting an attempt to re-enable them in 357e514c (Janka).
  • Refactoring in Next.js internal architecture included isolating the image optimization transform pipeline into modular utilities (1cb20a1d (Steven), d4c838a7 (Steven)) and migrating Turborepo CLI parsing to usage-rs (712e0e4a (Anthony)).

Based on 422 commits95.2 ETVUpdated Sep 30, 2026, 4:01 PM

Repositories

Where each repository stands: average performance per engineer per month over the last 90 days, with the rolling 90-day curve behind it. The range picks the window (past 90d): it sets how much of the curve you see, the Δ across it, and the span the work mix is measured over. Ranked highest first.

turborepo

Avg performance

22.17 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

+303.0%

Work mix

Features

35%

Maint

28%

Tests

16%

Docs

3%

Fixes

18%

4 devs202 ETV+1207% since Q2 2025Repository report

ai

Avg performance

5.41 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

+55.3%

Work mix

Features

33%

Maint

7%

Tests

38%

Docs

8%

Fixes

15%

10 devs163 ETV+146% since Q2 2025Repository report

workflow

Avg performance

4.76 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

+36.1%

Work mix

Features

23%

Maint

6%

Tests

39%

Docs

17%

Fixes

14%

10 devs143 ETV+139% since Q4 2025Repository report

next.js

Avg performance

4.18 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

+78.5%

Work mix

Features

28%

Maint

19%

Tests

31%

Docs

5%

Fixes

18%

17 devs213 ETV+14% since Q2 2025Repository report

sandbox

Avg performance

0.61 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

+26.1%

Work mix

Features

50%

Maint

2%

Tests

35%

Docs

7%

Fixes

6%

6 devs11 ETV+30% since Q1 2026Repository report

vercel

Avg performance

0.37 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

-67.0%

Work mix

Features

36%

Maint

10%

Tests

34%

Docs

7%

Fixes

14%

17 devs28 ETV+2028% since Q2 2025Repository report

swr

Avg performance

0.26 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

+228.4%

Work mix

Features

40%

Maint

6%

Tests

51%

Docs

3%

Fixes

0%

1 dev1 ETV−47% since Q3 2025Repository report

sdk

Avg performance

0.01 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

-98.3%

Work mix

Features

0%

Maint

63%

Tests

38%

Docs

0%

Fixes

0%

2 devs0 ETV+1316% since Q2 2025Repository report

Performance Composition

Each month's output split by type of work: Features (new value), Maintenance (sustaining systems), Tests, Docs, and Fixes (rework). The yellow line is output per engineer, so when it rises each engineer is delivering more, whatever the team size did. Unit: Engineering Throughput Value (ETV).

Cost per Performance Unit

−69%

If performance per engineer more than doubled, each unit of engineering performance now costs approximately 69% less than at the baseline 90-day window (ending 2025-04-01). Treat this as a direction, not a price: the exact figure depends on fully-loaded engineer cost, but which way it moved is not in doubt.

Effective Capacity Added

+113 engineers

At today's productivity, the current 50-person team delivers the performance equivalent of 163 engineers at the baseline 90-day rolling window (ending 2025-04-01). That's roughly 113 engineers worth of capacity added through productivity gains, not hiring.

CapEx vs OpEx

Each month's work split two ways. CapEx (capitalizable investment) is the work that builds the asset: Features, plus the Tests that prove it works and the Docs that explain it. OpEx (operating expense) is keeping it running: Maintenance and Fixes. The yellow line is the CapEx share, so a rising line means more of the month went into building new rather than sustaining what exists. Unit: Engineering Throughput Value (ETV).

Hours per Repository

Trailing 90-day window (64 working days). Org-level capacity is allocated to each repo by its share of org performance, then split CapEx / OpEx by that repo's own Features + Tests + Docs vs Maintenance + Fixes mix.

swr
CapEx %
93.7%
OpEx %
6.3%
sandbox
CapEx %
91.6%
OpEx %
8.4%
workflow
CapEx %
80.0%
OpEx %
20.0%
ai
CapEx %
78.3%
OpEx %
21.7%
vercel
CapEx %
77.6%
OpEx %
22.4%
next.js
CapEx %
63.6%
OpEx %
36.4%
turborepo
CapEx %
53.7%
OpEx %
46.3%
sdk
CapEx %
37.5%
OpEx %
62.5%
Total
CapEx %
68.1%
OpEx %
31.9%

Feature Contribution

Who shipped each month's new feature work, as a share of that month's total. Bands of similar width mean new value is coming from across the team; one band that stays wide means most of it rests on the same person. Named engineers shipped the most Features over the period. Everyone else is grouped as Others.

Quarterly Summary

Engineers and the Features / Maintenance / Tests / Docs / Fixes mix for each quarter. Cost / Perf Unit is what one unit of performance costs against Q2'25. Engineers vs headcount is measured against this org's own pre-AI baseline instead, its first reading in the index, so it agrees with the capacity tile and chart above.

Q2'25—
Engineers
54
Cost / Perf Unit
0%
Engineers vs headcount
−9 engineers
Work mix
22% Features
Q3'25+18%
Engineers
61
Cost / Perf Unit
−4%
Engineers vs headcount
−8 engineers
Work mix
25% Features
Q4'25+18%
Engineers
67
Cost / Perf Unit
−10%
Engineers vs headcount
−4 engineers
Work mix
32% Features
Q1'26+99%
Engineers
69
Cost / Perf Unit
−54%
Engineers vs headcount
+56 engineers
Work mix
34% Features
Q2'26+6%
Engineers
63
Cost / Perf Unit
−60%
Engineers vs headcount
+69 engineers
Work mix
28% Features