Stop comparing RAG and CAG. I wish I knew how each contributes to context before spending hours trying to get one do the job of other.
Most teams are still trying to squeeze costs out of their RAG pipeline.
But the smartest teams aren't just optimising,
they're re-architecting their context.
They know itโs not about RAG vs. CAG.
Itโs about knowing how to leverage each, intelligently.
It's about Context Engineering.
๐ง๐ต๐ฒ "๐ฃ๐ฎ๐-๐ฃ๐ฒ๐ฟ-๐ค๐๐ฒ๐ฟ๐" ๐ฃ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ:
Retrieval-Augmented Generation (RAG)
RAG is powerful, giving LLMs access to dynamic data.
But from a cost perspective, itโs a โpay-per-drinkโ model.
Every single query has a cost attached:
โข ๐๐ผ๐บ๐ฝ๐๐๐ฒ ๐๐ผ๐๐: API calls to an embedding model.
โข ๐๐ป๐ณ๐ฟ๐ฎ๐๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ ๐๐ผ๐๐: Hosting a vector database and a retriever.
โข ๐ฃ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ ๐๐ผ๐๐: Latency and irrelevant results degrade user experience, which costs you users.
ย ย
Optimising RAG helps, but you're still paying for every single lookup.
๐ง๐ต๐ฒ "๐ฃ๐ฎ๐-๐ข๐ป๐ฐ๐ฒ, ๐จ๐๐ฒ-๐ ๐ฎ๐ป๐" ๐ฆ๐ผ๐น๐๐๐ถ๐ผ๐ป:
Cache-Augmented Generation (CAG)
CAG flips the cost model on its head.
Itโs built for efficiency with scoped knowledge.
Instead of fetching data every time, you:
โ Preload a static knowledge base into the model's context.
โ Compute and store its KV cache just once.
โ Reuse this cache across thousands of subsequent queries.
The result is a massive drop in per-query costs.
โข ๐๐น๐ฎ๐๐ถ๐ป๐ด ๐ณ๐ฎ๐๐: No real-time retrieval latency.
โข ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฎ๐น๐น๐ ๐๐ถ๐บ๐ฝ๐น๐ฒ: Fewer moving parts to manage and pay for.
โข ๐๐ป๐ณ๐ฟ๐ฎ-๐น๐ถ๐ด๐ต๐: The most expensive work (caching) is done upfront, not on every call.
Itโs Not RAG vs. CAG. Itโs RAG + CAG.
The most cost-effective AI systems don't choose one.
They use a hybrid approach, like the teams at ๐ ๐ฎ๐ป๐๐ ๐๐.
The goal is to match the data's nature to the right architecture.
This is ๐๐ผ๐ป๐๐ฒ๐ ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด: strategically deciding what knowledge is cached and what is retrieved.
โ Use CAG for your static foundation:
This is for knowledge that doesn't change often but is frequently accessed. Pay the upfront cost to cache it once and enjoy near-zero marginal cost for every query after.
โ Use RAG for your dynamic layer:
This is for information that is volatile, real-time, or user-specific. You only pay the retrieval cost when you absolutely need the freshest data.
The Bottom Line
Stop thinking in terms of "RAG vs. CAG."
Start thinking like a ๐๐ผ๐ป๐๐ฒ๐ ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ.
By building a static foundation with CAG and using RAG for dynamic lookups, you create a system that is not only powerful and fast but also dramatically more cost-effective at scale.
RAG isn't dead, and CAG isn't a silver bullet. They are two essential tools in your cost-optimisation toolkit.
If you're building an AI stack that's both smart and sustainable, this is for you.
โป๏ธ Repost to share this strategy.
โ Follow Shivani Virdi for more.
Most teams are still trying to squeeze costs out of their RAG pipeline.
But the smartest teams aren't just optimising,
they're re-architecting their context.
They know itโs not about RAG vs. CAG.
Itโs about knowing how to leverage each, intelligently.
It's about Context Engineering.
๐ง๐ต๐ฒ "๐ฃ๐ฎ๐-๐ฃ๐ฒ๐ฟ-๐ค๐๐ฒ๐ฟ๐" ๐ฃ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ:
Retrieval-Augmented Generation (RAG)
RAG is powerful, giving LLMs access to dynamic data.
But from a cost perspective, itโs a โpay-per-drinkโ model.
Every single query has a cost attached:
โข ๐๐ผ๐บ๐ฝ๐๐๐ฒ ๐๐ผ๐๐: API calls to an embedding model.
โข ๐๐ป๐ณ๐ฟ๐ฎ๐๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ ๐๐ผ๐๐: Hosting a vector database and a retriever.
โข ๐ฃ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ ๐๐ผ๐๐: Latency and irrelevant results degrade user experience, which costs you users.
ย ย
Optimising RAG helps, but you're still paying for every single lookup.
๐ง๐ต๐ฒ "๐ฃ๐ฎ๐-๐ข๐ป๐ฐ๐ฒ, ๐จ๐๐ฒ-๐ ๐ฎ๐ป๐" ๐ฆ๐ผ๐น๐๐๐ถ๐ผ๐ป:
Cache-Augmented Generation (CAG)
CAG flips the cost model on its head.
Itโs built for efficiency with scoped knowledge.
Instead of fetching data every time, you:
โ Preload a static knowledge base into the model's context.
โ Compute and store its KV cache just once.
โ Reuse this cache across thousands of subsequent queries.
The result is a massive drop in per-query costs.
โข ๐๐น๐ฎ๐๐ถ๐ป๐ด ๐ณ๐ฎ๐๐: No real-time retrieval latency.
โข ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฎ๐น๐น๐ ๐๐ถ๐บ๐ฝ๐น๐ฒ: Fewer moving parts to manage and pay for.
โข ๐๐ป๐ณ๐ฟ๐ฎ-๐น๐ถ๐ด๐ต๐: The most expensive work (caching) is done upfront, not on every call.
Itโs Not RAG vs. CAG. Itโs RAG + CAG.
The most cost-effective AI systems don't choose one.
They use a hybrid approach, like the teams at ๐ ๐ฎ๐ป๐๐ ๐๐.
The goal is to match the data's nature to the right architecture.
This is ๐๐ผ๐ป๐๐ฒ๐ ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด: strategically deciding what knowledge is cached and what is retrieved.
โ Use CAG for your static foundation:
This is for knowledge that doesn't change often but is frequently accessed. Pay the upfront cost to cache it once and enjoy near-zero marginal cost for every query after.
โ Use RAG for your dynamic layer:
This is for information that is volatile, real-time, or user-specific. You only pay the retrieval cost when you absolutely need the freshest data.
The Bottom Line
Stop thinking in terms of "RAG vs. CAG."
Start thinking like a ๐๐ผ๐ป๐๐ฒ๐ ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ.
By building a static foundation with CAG and using RAG for dynamic lookups, you create a system that is not only powerful and fast but also dramatically more cost-effective at scale.
RAG isn't dead, and CAG isn't a silver bullet. They are two essential tools in your cost-optimisation toolkit.
If you're building an AI stack that's both smart and sustainable, this is for you.
โป๏ธ Repost to share this strategy.
โ Follow Shivani Virdi for more.