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Vercel — Engineering Performance

VercelEngineering capacity
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+93Eff. engineering capacity added

56 engineers now deliver what 149 would have in Apr 2025.

+31 vs the previous 90 days

Effective engineersReal engineers
Apr 2025Dec 2025Sep 2026
Where the work wentBiggest shift: Features up 4 points
32%
Features
14%
Maintenance
28%
Tests
8%
Docs
18%
Fixes

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

+166.6%

1.54 → 4.12 ETV

Active engineers

+16.7%

48.0 → 56.0

Features

+6.5pp

25.7% → 32.2%

vs. 500 OSS index

1.3x

1.7x → 1.3x · +28% 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 April 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
149engineer-equivalents
+93 engineers above real headcount
Real engineers
56engineers
active in the trailing 90 days
Capacity vs pre-AI
2.7x
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 28% above the index (4.12 vs 3.22 ETV/dev/mo). At the start of the tracked period the gap was 69% above.

Vercel
4.12ETV / dev / mo
+1.11 (+36.9%) past 90d
500 OSS Performance Index
3.22ETV / dev / mo
+0.99 (+44.4%) past 90d

Behind the numbers

Aug

Written summary of the work completed each month.

During August 2026, the team maintained steady delivery across 422 commits, shipping core enhancements across Turbopack, AI SDK, and Next.js caching architecture. Overall output aligned closely with the 5-month average (+1%), with Docs score showing a candidate-material increase of +89% (18 vs 9 5-month average) and Maintenance dropping -35% (19 vs 29 5-month average). Delivery performance remained steady in line with recent run rates.

Highlights

  • Introduced export-name mangling in Turbopack for reduced bundle sizes in a84bc8de (Tobias) and enabled it by default in production canary builds in 6f44fd34 (Tobias)
  • Implemented granular cache key generation based on code hashes and runtime envs for Next.js use-cache in e3790aa9 (Niklas) and exposed durableUseCacheEntries in 3d85af96 (Niklas)
  • Expanded AI SDK provider capabilities, including video response formats for Gemini in ca29e9b7 (Aayush), Azure AI Foundry/Cognitive Services custom base URLs in 993e9004 (Gregor), and Anthropic reasoning budget support in Bedrock in 051a41d5 (Lars)
  • Optimized Turbopack persistence lookups by storing short keys in key order within SST blocks in 33a5d542 (Luke), improving lookup speeds by up to 30%
  • Added support for Cargo root packages and expanded repository lockfile package metadata across single-package repositories in e732034b (Anthony) and 2c84c66d (Anthony)

Observations

  • Docs score rose +89% above the 5-month average (18 vs 9 5-month average), meeting the provisional 30% candidate-material threshold
  • Maintenance score dropped 35% compared to the 5-month average (19 vs 29 5-month average), while total output remained steady (+1% vs 155 5-month average)
  • Next.js App Router root sync I/O testing showed instability, requiring test deactivation in 0a2bd1d5 (Janka) and a revert in 357e514c (Janka) to preserve CI pipeline health
  • Event log handling in the world and world-vercel subsystems required multiple reliability fixes for duplicate attr_set events in 855e4799 (Peter) and lost payload error handling in 1c44cc8c (Peter)
  • Turbopack chunk collision risks on large module graphs were mitigated by widening the chunk identity hash from 7 to 13 base38 characters in cbe4687b (Tobias)

Based on 422 commits95.2 ETVUpdated Sep 8, 2026, 8:17 AM

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

10.58 ETV

per engineer per month

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

+124.8%

Work mix

Features

40%

Maint

25%

Tests

10%

Docs

4%

Fixes

21%

5 devs163 ETV+1207% since Q2 2025Repository report

workflow

Avg performance

5.31 ETV

per engineer per month

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

+67.3%

Work mix

Features

24%

Maint

7%

Tests

35%

Docs

19%

Fixes

15%

9 devs145 ETV+139% since Q4 2025Repository report

ai

Avg performance

4.82 ETV

per engineer per month

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

+46.0%

Work mix

Features

31%

Maint

6%

Tests

38%

Docs

6%

Fixes

18%

11 devs161 ETV+146% since Q2 2025Repository report

next.js

Avg performance

2.91 ETV

per engineer per month

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

+26.6%

Work mix

Features

29%

Maint

17%

Tests

28%

Docs

6%

Fixes

20%

19 devs167 ETV+14% since Q2 2025Repository report

vercel

Avg performance

0.75 ETV

per engineer per month

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

-38.4%

Work mix

Features

42%

Maint

8%

Tests

32%

Docs

5%

Fixes

13%

22 devs55 ETV+2028% since Q2 2025Repository report

sandbox

Avg performance

0.49 ETV

per engineer per month

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

+16.4%

Work mix

Features

45%

Maint

3%

Tests

35%

Docs

8%

Fixes

8%

6 devs9 ETV+30% since Q1 2026Repository report

swr

Avg performance

0.29 ETV

per engineer per month

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

Work mix

Features

30%

Maint

6%

Tests

61%

Docs

2%

Fixes

2%

2 devs2 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

100%

Tests

0%

Docs

0%

Fixes

0%

1 dev0 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

−62%

If performance per engineer more than doubled, each unit of engineering performance now costs approximately 62% 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

+93 engineers

At today's productivity, the current 56-person team delivers the performance equivalent of 149 engineers at the baseline 90-day rolling window (ending 2025-04-01). That's roughly 93 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.

swr92.5%7.5%
sandbox88.7%11.3%
vercel79.5%20.5%
workflow77.9%22.1%
ai75.2%24.8%
next.js63.0%37.0%
turborepo52.3%47.7%
sdk0.0%100.0%
Total68.1%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. Eff. Capacity Added 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.

Quarter
Q2'25550%−10 engineers
22% Features
Q3'2562−4%−9 engineers
25% Features
+18%
Q4'2567−12%−4 engineers
32% Features
+18%
Q1'2670−54%+55 engineers
34% Features
+99%
Q2'2664−60%+68 engineers
28% Features
+6%