Comparison guide
Compression vs caching
Prompt compression and caching are complementary cost optimization strategies. Understanding the difference helps you choose the right approach for each use case.
When to use caching
Caching stores and reuses LLM responses for identical queries. It is ideal when: the exact same query repeats often, the response is deterministic, and response freshness is not critical. Caching saves 100% of token costs for cached queries.
When to use compression
Compression reduces the input size of every query, including unique ones. It is ideal when: every query is different, context is long but noisy, and latency reduction is a goal. Compression saves 60-85% on every query.
Using both together
The optimal stack: check cache first (for identical queries), compress before sending (for unique queries), and cache the compressed result (for near-identical queries). This maximizes cost savings without sacrificing quality.
Frequently asked questions
Which saves more money?
Caching saves more per cached query (100%), but compression saves on every query (60-85%). Combined, they maximize total savings.
Do they work with streaming?
Yes. Cache the streamed response, compress before streaming. Both work seamlessly with streaming.
Build with less context
Put compression in front of your next LLM call.
Use the hosted API or run SuperCompress locally. Keep the evidence, drop the token waste, and measure the savings before it reaches your model.