9 posts tagged "cost-optimization".
Every post tagged "cost optimization" in the journal. Tag archives are auto-generated from post frontmatter -- one entry per unique tag across non-draft posts. Currently 9 posts share this tag. Use the category filter above to scope by editorial type.
Every legit way to get LLM compute for $0 in 2026: free API tiers, trial credits, student offers, and startup grants up to $350k — verified, with the catch on each.
Showing 8 of 9 posts — page 1 of 1
Anthropic's compaction API summarizes an agent's history when it hits a token threshold. How it works, the billing pass you don't see, and when it backfires.
Anthropic's context editing clears stale tool results from an agent's window, cutting token use up to 84%. How it works, the config, and the prompt-cache catch.
Which LLM APIs are actually free to call in 2026 — not free chat apps or expiring trial credits. Verified free tiers, real rate limits, and the data catch.
The $/M input/output sticker hides cache-write premiums, a tokenizer tax, context surcharges, invisible reasoning tokens, and more. Ten verified costs, with proofs.
In 2026 the biggest lever on your LLM bill isn't which model. It's how hard the model thinks: reasoning tokens bill as output, and most models think by default.
There is no single cheapest LLM API. The winner depends on your workload shape. Verified $/M prices for classification, chat, coding, RAG, batch, and reasoning.
Caveman mode strips filler from Claude's output. The 65% headline is real for output tokens. Here's what it actually saves on your monthly bill.
Anthropic charges 10% of input for cache reads, with a 1.25x write fee. OpenAI auto-caches above 1024 tokens. The math changes which LLM is cheapest -- here is when.