Cloudflare — Engineering Performance
104 engineers now deliver what 303 would have in Apr 2025.
▼ −26 vs the previous 90 days
Performance snapshot
Today's rolling 90-day reading for Cloudflare, compared with the start of the series. Pick a window to move that comparison point.
Avg. perf / dev / mo
+191.2%
0.42 → 1.22 ETV
Active engineers
−15.4%
123.0 → 104.0
Features
+4.1pp
27.5% → 31.6%
vs. 500 OSS index
0.38x
0.46x → 0.38x · −62% below
Engineering capacity
Effective engineers behind Cloudflare, against its pre-AI baseline. Each subject has its own: Cloudflare's is 0.42 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.
Cloudflare vs. 500 OSS Performance Index
Per-engineer ETV for Cloudflare 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: Cloudflare is 62% below the index (1.22 vs 3.22 ETV/dev/mo). At the start of the tracked period the gap was 54% below.
Behind the numbers
Written summary of the work completed each month.
Delivery remained steady and in line with the recent run rate during 2026-08 across 511 commits, characterized by heavy investments in testing infrastructure and automated schema maintenance. Candidate-material shifts compared to the 5-month average included a +71% increase in Tests score (44 vs 25 average), alongside decreases in Features (-41%) and Fixes (-52%). Key accomplishments included the rollout of durable job queues in the lifecycle subsystem and extensive test suite modularization for webstreams.
Highlights
- Introduced a durable job queue architecture in the lifecycle subsystem with push-based job dispatch via
JobDriverand an OOM circuit breaker in 8ffb3ad1 (Matt) - Expanded webstreams test infrastructure with modular suites covering data volumes 414dba4a (James), draining readers 77613e82 (James),
structuredClonetransfers fcf6a557 (James), and R2 consumption patterns 1740f457 (James) - Updated OpenAPI specifications across
openapi.jsonandopenapi.yamlto support IPv6 SMTP validation 461cfd1c (cloudflare-schema-publisher[bot)], Cloudforce One binary-storage 6f3c2e6c (cloudflare-schema-publisher[bot)], and DDoS/DNS protection endpoints 73ae2a1c (cloudflare-schema-publisher[bot)] - Added official developer documentation guides for deploying Flask and FastAPI applications on Python Workers in ad2c5e17 (Hood)
Observations
- Tests score rose +71% compared to the 5-month average (44 vs 25 average), driven primarily by test refactoring and coverage expansion across the webstreams subsystem
- Features score decreased 41% compared to the 5-month average (19 vs 33 average), as commit activity centered largely on schema updates and testing harnesses
- Fixes score dropped 52% compared to the 5-month average (8 vs 17 average), reflecting lower recorded rework across runtime components
- Commit volume reached 511 commits vs the 373-commit 5-month average (+37%), largely inflated by automated OpenAPI schema synchronizations and routine daily release bumps like 8865ba50 (Workers) and e9dda596 (Workers)
- Streams API testing underwent significant architectural cleanup, absorbing legacy monolithic files like
streams-test.jsinto isolated test suites in 65d10471 (James) and ffae63f9 (James)
Based on 511 commits38.5 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.
agents
Avg performance
5.89 ETV
per engineer per month
Avg. dev performance / month (90-day MA)
-44.6%
Work mix
Features
32%
Maint
11%
Tests
37%
Docs
9%
Fixes
11%
workers-sdk
Avg performance
1.38 ETV
per engineer per month
Avg. dev performance / month (90-day MA)
+69.8%
Work mix
Features
28%
Maint
21%
Tests
32%
Docs
7%
Fixes
11%
pingora
Avg performance
1.10 ETV
per engineer per month
Avg. dev performance / month (90-day MA)
+0.7%
Work mix
Features
55%
Maint
14%
Tests
12%
Docs
1%
Fixes
18%
workerd
Avg performance
0.97 ETV
per engineer per month
Avg. dev performance / month (90-day MA)
+181.3%
Work mix
Features
36%
Maint
8%
Tests
43%
Docs
5%
Fixes
9%
sandbox-sdk
Avg performance
0.83 ETV
per engineer per month
Avg. dev performance / month (90-day MA)
-51.8%
Work mix
Features
47%
Maint
6%
Tests
38%
Docs
4%
Fixes
5%
foundations
Avg performance
0.73 ETV
per engineer per month
Avg. dev performance / month (90-day MA)
-53.8%
Work mix
Features
78%
Maint
15%
Tests
4%
Docs
2%
Fixes
2%
cloudflare-docs
Avg performance
0.30 ETV
per engineer per month
Avg. dev performance / month (90-day MA)
-21.6%
Work mix
Features
17%
Maint
14%
Tests
4%
Docs
60%
Fixes
6%
cloudflared
Avg performance
0.24 ETV
per engineer per month
Avg. dev performance / month (90-day MA)
+27.5%
Work mix
Features
4%
Maint
42%
Tests
47%
Docs
9%
Fixes
0%
telescope
Avg performance
0.20 ETV
per engineer per month
Avg. dev performance / month (90-day MA)
-56.4%
Work mix
Features
25%
Maint
13%
Tests
16%
Docs
12%
Fixes
34%
workers-rs
Avg performance
0.04 ETV
per engineer per month
Avg. dev performance / month (90-day MA)
-89.1%
Work mix
Features
31%
Maint
35%
Tests
19%
Docs
8%
Fixes
8%
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
−66%
If performance per engineer more than doubled, each unit of engineering performance now costs approximately 66% 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
+199 engineers
At today's productivity, the current 104-person team delivers the performance equivalent of 303 engineers at the baseline 90-day rolling window (ending 2025-04-01). That's roughly 199 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.
| sandbox-sdk | 89.3% | 10.7% |
| foundations | 83.5% | 16.5% |
| workerd | 83.2% | 16.8% |
| cloudflare-docs | 80.9% | 19.1% |
| agents | 79.4% | 20.6% |
| pingora | 67.8% | 32.2% |
| workers-sdk | 67.5% | 32.5% |
| cloudflared | 59.3% | 40.7% |
| workers-rs | 55.6% | 44.4% |
| telescope | 52.5% | 47.5% |
| Total | 77.3% | 22.7% |
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 | |||||
|---|---|---|---|---|---|
| Q2'25 | 134 | 0% | +36 engineers | 29% Features | — |
| Q3'25 | 145 | −2% | +43 engineers | 32% Features | +11% |
| Q4'25 | 140 | −3% | +42 engineers | 28% Features | −3% |
| Q1'26 | 148 | −38% | +152 engineers | 36% Features | +65% |
| Q2'26 | 136 | −49% | +202 engineers | 28% Features | +13% |