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Caching is one of the oldest tricks in software engineering, and it works just as well on AI requests as it does on database queries. If the same (or a similar) prompt is going to hit the model more than once, you shouldn't be paying full price for it every time.
Why This Matters
There are actually two layers of caching worth understanding here, and they solve slightly different problems.
Prompt caching happens at the provider level: if part of your request context is identical to a previous request (a long system prompt, a repeated document), the provider can reuse that processed context and charge you a lower rate for those cached tokens instead of the full input price. This is especially valuable for multi-turn conversations or repeated large contexts.
Application-level caching is something you build yourself: if a user asks a question your app has already answered recently, serve the stored answer instead of calling the model again at all.
- Prompt caching reduces cost per call when context repeats
- Application caching eliminates the call entirely for genuinely repeated questions
- Combined, they compound: fewer calls, and the calls you do make cost less
A Simple Framework
- Identify repeated context — system prompts, reference documents, long instructions that don't change between requests
- Use a stable session or affinity identifier for multi-turn conversations, so related requests can reuse cached context
- Build an application-level cache (even something simple, like a lookup keyed on normalized user input) for genuinely repeated queries
- Set a sensible cache expiration so stale answers don't linger forever
- Track your cache hit rate — a low hit rate means your caching strategy needs adjusting
Example
Before: A support chatbot resends its full 2,000-token system prompt and product documentation on every single message in a conversation, even though none of that content changed since the first message.
After: The same chatbot reuses a consistent session identifier across the conversation, letting cache-aware routing recognize and discount the repeated context — while a lightweight application cache also catches near-identical FAQ questions before they ever reach the model.
> Tip: For multi-turn conversations, set your own identifier — like a session or task ID — and reuse it consistently across related requests. This is what allows a router to recognize "this is the same unit of work" and maximize cache reuse.
Common Mistakes
- Never reusing session identifiers, so every request looks "new" to the caching layer
- Caching answers indefinitely, leading to stale or outdated responses
- Assuming prompt caching alone solves everything, without building any application-level cache for repeated questions
- Forgetting that caching behavior can vary by model, so results won't be identical across providers
DigitalOcean covers this in detail in its Inference features guide, including how the Inference Router applies cache-aware routing automatically.
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Caching is the closest thing to "free money" in token optimization — it costs you a little engineering time upfront and pays for itself continuously afterward.