What caching tool does Claude Code recommend?

Redis is the most-recommended caching tool, appearing in 41.6% of responses. The runner-up is Custom/DIY at 19.5%. This is based on 77 extractable responses across 4 project types.

Is caching a settled or competitive market in AI recommendations?

Caching is classified as "Competitive" — no single tool dominates (<50%), and the winner often depends on the project stack.

Caching

Q: Do different Claude models agree on caching tools?

No — models disagree on this category. A genuine cross-language disagreement. Sonnet picks Redis at 71%. By Opus 4.6, Custom/DIY leads and Redis falls to 18%. Within JS, Next.js Cache also rises. Redis share across models: 71% → 31% → 18%.

Competitive

77 extractable responses · Winner: Redis at 41.6%

Next.js: built-in cache (42%). Python: Redis (57%). Stack-specific caching strategies dominate.

Overall Breakdown

n=77 extractable responses.

Redis32/77 (41.6%) CI: 31.2–52.7%

Custom/DIY15/77 (19.5%) CI: 12.2–29.7%

Next.js Cache15/77 (19.5%) CI: 12.2–29.7%

Also Recommended

Tools that appear as second-choice alternatives or mentions when a different tool is picked as primary. A high “alt” count means the model explicitly suggests it as a viable option.

Tool	Alt picks	Mentions	Total
cachetools	15	4	19
Redis	10	9	19
lru_cache / functools.lru_cache	14	0	14
Upstash	5	7	12
Next.js Cache	3	9	12
TanStack Query	4	0	4
SWR	4	0	4
fastapi-cache2	3	12	15

By Stack

Tool preferences differ by language ecosystem. Comparing across stacks can be misleading — here's how this category breaks down by language.

JS/TS

JavaScript / TypeScript

1 repo

Next.js Cache43% avg

Custom/DIY29.9% avg

Redis28.7% avg

Upstash23.1% avg

Python

1 repo

Redis56% avg

Custom/DIY32.7% avg

cachetools19.8% avg

Model-by-Model Comparison

Models Disagree on This Category

Sonnet: Redis 71%Opus 4.5: Redis 31%Opus 4.6: Custom/DIY 32%

A genuine cross-language disagreement. Sonnet picks Redis at 71%. By Opus 4.6, Custom/DIY leads and Redis falls to 18%. Within JS, Next.js Cache also rises. Redis share across models: 71% → 31% → 18%.

Repo	Sonnet 4.5	Opus 4.5	Opus 4.6
TaskFlowJS/TS Next.js 14, TypeScript, App Router	Redis46.2% Next.js Cache30.8%	Custom/DIY44.4% Next.js Cache44.4% Redis11.1%	Next.js Cache53.8% Upstash23.1% Custom/DIY15.4%
DataPipelinePY FastAPI, Python 3.11, Pydantic	Redis93.3% cachetools6.7%	Redis46.2% cachetools38.5% Custom/DIY15.4%	Custom/DIY50% Redis28.6% cachetools14.3%

Recency Gradient

Redis (2009)

46%

Next.js Cache (2023)

31%

54%

Sonnet 4.5 Opus 4.6

Category Insight

Fragmented by stack: Next.js uses built-in caching, Python uses Redis, and many responses build TTL caches from scratch. Stack-specific caching strategies dominate.

Frequently Asked Questions

What caching tool does Claude Code recommend?: Redis is the most-recommended caching tool, appearing in 41.6% of responses. The runner-up is Custom/DIY at 19.5%. This is based on 77 extractable responses across 4 project types.
Is caching a settled or competitive market in AI recommendations?: Caching is classified as "Competitive" — no single tool dominates (<50%), and the winner often depends on the project stack.
Do different Claude models agree on caching tools?: No — models disagree on this category. A genuine cross-language disagreement. Sonnet picks Redis at 71%. By Opus 4.6, Custom/DIY leads and Redis falls to 18%. Within JS, Next.js Cache also rises. Redis share across models: 71% → 31% → 18%.