Q4 2025 · Asia-Pacific
2 e-commerce infrastructure startups based in Asia-Pacific ranked by GitHub engineering acceleration. Filtered from our broader E-commerce Infrastructure sector rankings.
| # | Company | Stage | Geo | Commits (14d) | Change | Contributors | Contrib. Growth | New Repos | Signal |
|---|---|---|---|---|---|---|---|---|---|
| 1 | bagisto A Free and Opensource laravel eCommerce framework built for all to build and scale your business. View signal profile → | Pre-seed | APAC | 19 | +999% | 4 | +0% | 2 | Deploy frequency spike |
| 2 | juspay Design to simplify. Revolutionizing digital payments. View signal profile → | Growth | APAC | 10 | +43% | 55 | +0% | 4 | Infrastructure buildout |
Sorted by commit velocity change (14-day window, descending). Data last updated Q4 2025. Geography from GitHub org profiles.
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In Q4 2025, bagisto leads e-commerce infrastructure startups in Asia-Pacific with 19 commits over a 14-day window (+999% change) and 4 active contributors. Across all 2 tracked Asia-Pacific-based startups in this sector, the average 14-day commit velocity is 15 commits. The dominant signal pattern is "Deploy frequency spike", which typically indicates accelerated shipping cadence, often seen before a public launch or major release.
Asia-Pacific accounts for 2 of the e-commerce infrastructure startups in our tracking dataset for Q4 2025. This geographic view filters the broader sector rankings to help investors focused on Asia-Pacific-based deal flow identify engineering acceleration patterns within their target geography. Regional concentrations often reflect local regulatory environments, talent pools, and investor networks that shape startup trajectories differently from global averages.
We derive startup geography primarily from the GitHub organization profile location field, supplemented by a manually curated enrichment database of known startup headquarters. This means startups without a public GitHub location may appear in our global sector rankings but not in geographic filters. The geographic classification uses broad regions (Asia-Pacific, etc.) rather than city-level granularity to provide meaningful sample sizes for comparison.