Prompt caching explained: Anthropic, OpenAI, and Gemini in 2026
Prompt caching cuts LLM API costs up to 90%, but Anthropic, OpenAI, and Gemini implement it differently. Here's how each vendor's billing actually works.
Tag · infra
Prompt caching cuts LLM API costs up to 90%, but Anthropic, OpenAI, and Gemini implement it differently. Here's how each vendor's billing actually works.
Route each prompt to the cheapest model that handles it well. When quality falls short, escalate silently. Here's the pattern with working Node.js code.
Add OpenRouter model fallbacks to a Node.js app: setup, the models array, response.model tracking, and four pitfalls that catch you on week two.
Semantic caching reduces LLM API spend by 20-70% in production. Here's how embedding-based, prompt-hash, and hybrid caching each break in practice.
Four production patterns for LLM rate limits: jitter, token pre-checks, circuit breakers, and provider failover. Backoff alone won't save you in 2026.
Prompt caching cuts LLM costs 90% on Anthropic and 50% on OpenAI, but only when your workload fits. Here's the exact break-even math per provider.
One model going down shouldn't take your AI feature with it. Here's how to build a fallback chain using LiteLLM, OpenRouter, and LLMTest.