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#AI costs

Artificial Intelligence

Prompt Compression, Explained: Lossy vs Lossless, and When Each Is Safe

Prompt compression means sending a language model fewer tokens for the same job. Lossy methods such as LLMLingua drop words or summarise; lossless methods remove only repeats and noise, so the model sees the same content. Here is how each works, what the papers claim, where quality suffers, and which one to use for chats, documents and coding agents. Checked 23 September 2026.

SEP 2026 · 11 MINREAD →

Artificial Intelligence

LLM Cost Optimization: Eleven Ways to Reduce Your OpenAI and Claude API Costs, in Order of Effort

The biggest savings on an LLM API bill come from four levers: a smaller model for routine work, prompt caching, a cap on output length, and the Batch API for anything that can wait. Here are all eleven levers we know, ranked by effort, with the typical saving for each and a worked example that takes one workload from $1,200 to $124 a month on prices checked 23 September 2026.

SEP 2026 · 13 MINREAD →

Artificial Intelligence

LLM Caching Explained: Semantic Caching vs Prompt Caching vs Response Caching

Semantic caching reuses a stored AI answer when a new question means the same thing as an old one. It is one of three kinds of LLM caching, next to the provider's prompt caching and exact-match response caching. Here is what each one does, when each pays, what they cost with prices checked on 23 September 2026, and which kind Gate is.

SEP 2026 · 12 MINREAD →