Vercel Sandbox Storage Doubles to 64 GB Across All Plans

Vercel has quietly doubled the disk space available inside every Vercel Sandbox, moving the limit from 32 GB to 64 GB. The change covers all sandbox types: those built from a Vercel Managed Image, those using a custom image, and even older sandboxes configured with the now-deprecated runtime property. According to Vercel, the extra space is aimed at teams dealing with large repositories, bloated dependency installs, build artifacts, and storage-heavy AI agent tasks.

What happened

Detail	Value
Previous storage limit	32 GB
New storage limit	64 GB
Applies to	All Vercel Sandboxes
Sandbox types covered	Managed Images, custom images, deprecated runtime property

Vercel has increased the default storage allocation inside its Sandbox environment (an isolated, ephemeral compute container used for builds, previews, and AI agent tasks) from 32 GB to 64 GB. Every sandbox gets this automatically, with no changes needed on the developer’s side.

Why it matters

The 32 GB cap was a real constraint for projects with large monorepos or dependency graphs that easily tip past 20 to 30 GB once node_modules, native binaries, and cached build artifacts stack up. At 64 GB there is meaningful breathing room before disk pressure starts killing builds.

The more interesting angle is AI agent workloads. Vercel specifically calls out “storage-intensive agent tasks” as a target use case. AI coding agents and retrieval pipelines often download model weights, vector indices, or large datasets at runtime, the kind of workloads that were bumping against the old ceiling. Doubling storage makes Vercel Sandboxes a more credible environment for running those jobs without external storage workarounds.

Our take

This is a small, useful, no-drama update. Vercel did not raise a price or introduce a new tier; they just doubled a resource. Teams who were not hitting the old limit will not notice. Teams who were hitting it will notice immediately.

The callout to agent tasks is telling. Vercel is clearly positioning Sandboxes as infrastructure for AI workloads, not just web builds. If you are building anything that runs an agent loop or processes large files inside a Vercel environment, this is worth testing now rather than after you hit a wall in production.

If you are evaluating where to run automated AI workflows, our AI integration work regularly surfaces these infrastructure tradeoffs, and the difference between 32 GB and 64 GB can be the line between a working pipeline and one that fails silently on large inputs.

For context on how ephemeral compute environments are being used in AI agent pipelines more broadly, see our coverage of Claude Opus 4.6 running in an isolated sandbox during a real security exercise.

What to do about it

1. Check your current sandbox disk usage in Vercel logs; if you were nowhere near 32 GB, no action needed.
2. If you had workarounds in place to keep artifacts under 32 GB (cleaning caches mid-build, externalising data), test whether you can remove them and simplify your build config.
3. If you run AI agent jobs inside Vercel Sandboxes, re-test workloads that previously failed or required external storage to confirm they now complete within the new limit.
4. Review the Vercel Sandbox image documentation to understand what is included in Managed Images versus custom images, so you know which layers are consuming your allocation.

Source: Vercel Blog