#Caching

Articles about Caching — exploring patterns, best practices, and real-world implementations in production systems.

11 posts tagged with caching. ← All posts

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Pratik Dhanave · ·5 min read

Caching at the Gateway

Model calls are slow and expensive, and a surprising fraction of them are repeats or near-repeats. Caching at the gateway turns those into instant, free responses — and because the gateway sees all traffic, it's the one place a cache benefits every application at once. Beyond exact-match caching, semantic caching catches queries that mean the same thing in different words, which is where the real savings live.

Model calls are slow and expensive, and a surprising fraction are repeats or near-repeats. Caching at the gateway turns those into instant, free responses — and because the gateway sees all traffic, it's the one place a cache benefits every app at once. Beyond exact-match caching, semantic caching catches queries that mean the same thing in different words, where the real savings live.

#AI Gateway#Caching#LLM Infrastructure#Performance#AI Engineering#LLMs
Pratik Dhanave · ·5 min read

Caching: Not Paying Twice for the Same Work

A large share of what an AI system processes is repeated — the same system prompt, the same documents, the same questions — and caching is how you stop paying full price for work you have already done.

A large share of what an AI system processes is repeated — the same system prompt, the same documents, the same questions. Caching is how you stop paying full price for work you have already done.

#AI Cost Optimization#AI Engineering#LLMs#Caching#FinOps#Prompt Engineering
Pratik Dhanave · ·15 min read

Caching

The highest-leverage tool for latency and scale — and the source of its hardest problem, invalidation. Where caches live, the patterns for filling them, how they evict, and why keeping them correct is the part that stays hard.

Caching as the highest-leverage latency tool — and its hardest problem: where caches live, the patterns (cache-aside/read-through/write-through/write-behind), eviction, and invalidation including cache stampede, penetration, and hot keys.

#System Design#Caching#Performance#Architecture#Distributed Systems
Pratik Dhanave · ·9 min read

Caching Pitfalls and Practice

Caching giveth performance and taketh away your sanity. The same technique that makes systems fast introduces a whole category of subtle, intermittent, hard-to-debug problems — stale data that appears randomly, a cache that collapses under load at the worst moment, bugs that only happen when the cache is cold or full. This closing post catalogs the pitfalls that bite real systems, and distills the practical wisdom of the series: cache deliberately, expect the failure modes, and remember that the two genuinely hard things are still hard.

Caching giveth performance and taketh away your sanity. The same technique that makes systems fast introduces subtle, intermittent, hard-to-debug problems — stale data appearing randomly, a cache collapsing under load, bugs that only happen when the cache is cold. This catalogs the pitfalls and the practical wisdom.

#Caching#Performance#Backend Engineering#Reliability
Pratik Dhanave · ·8 min read

Web and CDN Caching

Every time a web page loads instantly on a repeat visit, or a video streams smoothly from halfway around the world, caching is the reason. The web is layered with caches — in your browser, at CDN edge servers near you, in front of origin servers — all applying the same caching principle to make the internet fast. And remarkably, much of it is coordinated by a few HTTP headers that let servers tell caches exactly what to store and for how long. Understanding web and CDN caching is understanding how the internet stays fast at global scale.

Every time a web page loads instantly on a repeat visit, or a video streams smoothly from across the world, caching is the reason. The web is layered with caches — browser, CDN edge, origin — all applying the same principle, much of it coordinated by a few HTTP headers.

#Caching#Web#Performance#CDN#Backend Engineering
Pratik Dhanave · ·9 min read

Distributed Caching

A cache inside a single application process is easy — but it doesn't scale, and every instance of your app has its own separate copy. The moment you run many application servers, you want a shared cache they all use, which means a cache that lives across the network on its own machines: a distributed cache like Redis or Memcached. This unlocks scale and sharing, but introduces the distributed-systems problems that a local cache never had. Understanding distributed caching is understanding how caching works at real scale.

A cache inside a single process is easy — but it doesn't scale, and every instance of your app has its own separate copy. Run many servers and you want a shared cache across the network on its own machines: a distributed cache like Redis or Memcached. This unlocks scale and sharing, but introduces distributed-systems problems.

#Caching#Distributed Systems#Performance#Backend Engineering
Pratik Dhanave · ·8 min read

Caching Patterns

Knowing to cache is one thing; wiring the cache into your application correctly is another. Should the application manage the cache itself, or should the cache sit transparently in front of the source? Should writes go to the cache, the database, or both — and in what order? These questions have standard answers — the caching patterns — and choosing the right one shapes your consistency, performance, and complexity. Getting the pattern right is how caching goes from "store some stuff" to a coherent, correct design.

Knowing to cache is one thing; wiring the cache into your application correctly is another. Should the application manage the cache itself, or should it sit transparently in front of the source? Should writes go to the cache, the database, or both? These questions have standard answers — the caching patterns.

#Caching#Performance#Design Patterns#Backend Engineering#Databases
Pratik Dhanave · ·8 min read

Cache Invalidation

This is the hard one. "There are only two hard things in computer science: cache invalidation and naming things" names it directly — cache invalidation is genuinely, notoriously difficult. The moment you cache a copy of data, you've created a second source of truth that can drift from the first, and keeping them in sync (or deciding how much drift you'll tolerate) is a problem with no clean, universal solution. Understanding why it's hard, and the strategies for managing it, is the difference between caching that helps and caching that causes baffling bugs.

This is the hard one. 'There are only two hard things in computer science: cache invalidation and naming things' names it directly. The moment you cache a copy of data, you've created a second source of truth that can drift from the first, and keeping them in sync has no clean universal solution.

#Caching#Performance#Backend Engineering#Distributed Systems
Pratik Dhanave · ·8 min read

Eviction Policies

A cache is a small space pretending to be a big one, and the pretense only works if it's clever about what to keep. When a bounded cache fills up, every new item forces out an old one — and which one you evict determines your hit rate, which determines whether the cache is worth having at all. Eviction policies are the algorithms that make this choice, and understanding them (especially the workhorse, LRU) is essential to building caches that actually stay effective.

A cache is a small space pretending to be a big one, and the pretense only works if it's clever about what to keep. When a bounded cache fills up, every new item forces out an old one — and which one you evict determines your hit rate. Eviction policies are the algorithms that make this choice.

#Caching#Performance#Algorithms#Backend Engineering
Pratik Dhanave · ·8 min read

Caching Fundamentals

A cache lives or dies by one number: its hit rate. Every cache access is a small bet — that the data will be there (a hit, served fast) rather than missing (a miss, served slow, plus the overhead of caching it). Whether caching helps at all comes down to how often that bet pays off, and understanding hits, misses, and hit rate — and what you should and shouldn't cache — is the foundation of using caches effectively. Get these fundamentals right, and the rest of caching makes sense.

A cache lives or dies by one number: its hit rate. Every cache access is a small bet — that the data will be there (a hit, served fast) rather than missing (a miss, served slow). Whether caching helps at all comes down to how often that bet pays off.

#Caching#Performance#Backend Engineering#Computer Science
Pratik Dhanave · ·8 min read

Why Caching Exists

There's an old joke that there are only two hard things in computer science: cache invalidation and naming things. It's a joke because caching is everywhere and sounds simple — just keep a copy of stuff you'll need again — and it's true because getting caching right is genuinely, surprisingly hard. Caching is one of the most universal and powerful ideas in computing, appearing at every layer from CPU to CDN, and understanding why it exists and when it helps is foundational to building fast systems.

There's an old joke that there are only two hard things in computer science: cache invalidation and naming things. It's a joke because caching sounds simple, and true because getting it right is genuinely hard. Caching is one of the most universal ideas in computing, appearing at every layer from CPU to CDN.

#Caching#Performance#Backend Engineering#Computer Science

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.