Report card

Do AI agents pick Vercel KV?

How often agents choose Vercel KV when a developer needs caching & kv — measured across Claude, GPT, Gemini.

Vercel KV

#2 of 5 caching
8%Pick Rate
95% CI 5%14%

When developers ask an AI agent for caching, Vercel KV is picked 8% of the time — ranking #2 of 5 measured across Claude, GPT, Gemini.

Picked 8% Named, not picked 11% Never surfaced 80%

Beaten by Redis (59%).

Awareness

418K package downloads / week

Thin presence in the data models learn from — an awareness problem. Get into more code, docs, and developer discussion.

0 docs in FineWeb (10BT sample) · 1.9K URLs in Common Crawl

In the raw web crawl, but thin in the filtered corpus models actually train on — an awareness gap at the source.

AdoptionDid the pick convert to installs? Public Adoption = npm downloads of your packages. The proof half of the funnel.

1.4M installs / month ▼ 10% MoM

Installs are declining. Worth pairing with the Pick Rate trend to find the leak.

Get the full report

The per-model and per-surface breakdown for Vercel KV — where it wins, where it loses, and to whom — plus an alert when the Pick Rate moves.

About Vercel KV

A durable, Redis-compatible key-value store built into the Vercel platform.

Vercel KV is a managed key-value database that exposes a Redis-compatible API, supporting strings, sorted sets, lists, hashes, and sets. It is aimed at developers building on Vercel who need persistent data storage without managing their own Redis infrastructure. Values are stored durably and accessed over a REST API, with automatic JSON serialization handled by the client.

Where to start

Install the @vercel/kv package, pull your environment variables with the Vercel CLI to get KV_REST_API_URL and KV_REST_API_TOKEN, then import the default kv client and start reading and writing data.

Install

Links and summary verified from public sources.

The rest of the Caching & KV ranking

← Full Caching & KV leaderboard · How this is measured