Most AI caching today focuses on things like:
- Embeddings
- Retrieved documents
- Prompt templates
- Final responses
All of these are valuable.
But they have one thing in common.
They optimize around the reasoning process.
The model still has to read the same context, connect the same ideas, and perform much of the same reasoning on every request.
It reminds me of the early evolution of web infrastructure.
At first, servers recomputed everything.
Eventually, we realized that the computation itself often had value—and caching it became one of the biggest performance improvements.
I wonder if AI infrastructure is heading in a similar direction.
Instead of asking:
"What output should we cache?"
Maybe the better question is:
"What understanding is worth preserving?"
Imagine reusing synthesized understanding instead of repeatedly reconstructing it from the same documents.
That's the direction we've been exploring with Coalent - treating context as something that can be intelligently reused rather than rebuilt from scratch every time.
If you're curious, Coalent is live : https://coalent.ai
Curious how others are thinking about caching beyond embeddings and response caching. What approaches have you found promising?
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