In the race to create faster, more responsive digital experiences, API caching is one of the most powerful (and underutilized) tools available. Most of us think of image or video caching when performance comes up, but there’s a less visible, equally impactful area that often gets overlooked: API caching.

In this blog post, we’ll break down what API caching is, why it matters, how it differs across REST and GraphQL, and how you can start using it more strategically to improve user experience, scalability, and cost-efficiency. Plus, we’ll share how Harper makes it easier to implement caching at the API level, even at the edge.
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What Is API Caching?

At its core, API caching is the act of storing API responses closer to where the requests happen so that repeated requests don’t need to hit the origin every time. Instead of recalculating or re-fetching data from a database, a cached response is returned instantly from the memory of a nearby node.

The result? Faster response times, reduced load on your infrastructure, and a better experience for your end users.

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Why API Caching Often Gets Ignored

Caching APIs can be tricky. Unlike static images or documents, API responses often feel dynamic and personalized, so it’s easy to assume they can’t be cached. But that’s a misconception.

Even highly dynamic APIs often have components that can be cached safely and effectively. Think product catalogs that only update every few seconds or user profile data that doesn’t change often.

When you treat APIs as cacheable content, you unlock a new level of performance optimization—especially for modern apps that rely heavily on APIs to render frontend components.

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REST vs. GraphQL: Different Shapes, Different Challenges

RESTful APIs are structured and predictable. Each endpoint corresponds to a specific dataset or action. This consistency makes it easier to define caching rules: you know what response to expect and when it might become stale.

GraphQL, on the other hand, is more flexible. Clients can query exactly the data they need, which means the same endpoint can produce a wide variety of responses. This dynamism makes GraphQL caching more complex since variations in query shape can affect how the data is stored and retrieved.

However, that doesn’t mean it’s impossible. It just requires smarter caching strategies—like caching at the field or resolver level or building normalized caches that assemble responses from stored data fragments.

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Edge Caching: Getting Data Closer to Users

Performance is all about proximity. The closer your data is to your users, the faster it gets to them. Edge caching makes this possible by distributing your cache across multiple geographic nodes.

Harper takes this a step further by allowing you to deploy your API layer along with the cached data to the edge. Instead of just caching the responses, you can move the entire API application logic closer to users. This is especially powerful for global apps that need low-latency access no matter where a user connects from. 

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Consistency and Replication

With distributed systems, data consistency is key. Harper uses eventual consistency for replication, which means that updates propagate across the system asynchronously. In practice, replication across nodes happens in under 100ms—fast enough for most use cases, especially read-heavy applications.

Replication also applies to caches. That means a response cached in Europe can be available in North America almost instantly without waiting for North American servers to make separate calls to the origin. 

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Active vs. Passive Caching Strategies

There are two main approaches to caching:

• Passive caching: A response is cached the first time a client requests the data, meaning that the first call is slow, with subsequent requests being fast. 
• Active caching: Responses are proactively pushed into the cache based on predicted usage, scheduled updates, or pushed as part of the origin update process.

Harper supports both models, giving you flexibility depending on your application’s behavior and traffic patterns. For example, you can pre-warm the cache for a product launch or for long-tail content that you want to boost search running on by improving your core web vitals.

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Harper’s Approach to API Caching

Harper isn’t just a cache. It can serve as your API gateway, your cache layer, and your data source—all in one. Whether you're using our built-in RESTful API or defining a custom GraphQL schema, Harper can:

• Cache responses automatically
• Replicate data and cache across regions
• Serve content at the edge
• Act as the system of record for structured data

This makes it easier to reduce complexity in your stack while improving performance across the board.

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Final Thoughts

Caching isn’t just about shaving milliseconds off image loads. It’s about creating resilient, fast, scalable experiences that users can rely on. API caching is a critical part of that puzzle.

If you’ve been treating APIs as uncacheable, it’s time to rethink your approach. Start small: look at high-volume endpoints, assess how often they really change, and experiment with caching strategies that match your data patterns.

Curious what parts of your API can be cached? Contact us for a quick cache-readiness review.

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