Redis is a data store that keeps everything in memory rather than on disk. Reading from memory is orders of magnitude faster than reading from a database - a database query that takes 200 milliseconds can return in under a millisecond from Redis. This speed difference is what makes Redis useful. It sits in front of slower systems and stores the results of expensive operations so they only need to be computed once. The second time the same data is needed, it comes from Redis instantly instead of being recalculated or re-fetched.

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Performance is a feature. When every millisecond counts, Redis is the answer.

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The most common use of Redis is caching - storing the results of database queries or API calls so they do not need to be repeated. A product page that requires twenty database queries to build can be cached in Redis after the first request. Every subsequent request for the same page gets the cached version in milliseconds rather than waiting for twenty queries to run. When the data changes, the cache is cleared and rebuilt on the next request. For products with high traffic and data that does not change on every request, caching with Redis can reduce database load dramatically and make the product feel significantly faster to users.

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Redis also handles use cases beyond caching. Session storage - keeping track of logged-in users - is fast and reliable in Redis. Rate limiting - preventing any single user or IP address from making too many requests in a short period - can be implemented atomically in Redis without race conditions. Job queues - lists of background tasks waiting to be processed - work naturally as Redis data structures. In AI products we use Redis to cache responses from language models, so a question that has been asked before returns instantly from cache rather than making a new API call. This reduces both response time and API costs.

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What this means for your product:

• Pages and API responses that load faster because results are cached in memory

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• Database load reduced because common queries are served from cache

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• Session management that is fast and works correctly across multiple server instances

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• Rate limiting and job queues handled without additional infrastructure

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Chips:

Redis · Caching · Sessions · Rate Limiting · Pub/Sub · Job Queues · In-memory · LLM Cache

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