LLM API pricing guides

// guides

• Batch APIs vs Real-Time Calls: When Half-Price LLM Inference Is Worth the Wait
  How LLM batch APIs work, what they typically cost compared with real-time calls, and which workloads should move to batch processing.
• Context Windows Explained: Do You Really Need 1 Million Tokens?
  What an LLM context window is, how it affects cost, when long context beats RAG, and how to keep long prompts affordable.
• How LLM API Pricing Works: Tokens, Input vs Output and Hidden Costs
  A plain-English guide to LLM API pricing: what a token is, why output costs more than input, long-context tiers, caching, batch discounts and reasoning tokens.
• How to Estimate the Monthly Cost of an LLM App (Step by Step)
  A practical method to estimate the API cost of a chatbot, RAG app or AI agent before launch: count tokens per request, multiply by volume, add overhead.
• Open-Weights vs Proprietary LLMs: What They Really Cost to Use
  How the pricing of open-weights models like Llama, Qwen, Gemma or GLM differs across inference hosts, and when they beat proprietary APIs on cost.
• Prompt Caching Explained: How Much Can It Save on LLM API Costs?
  What prompt caching is, how cached input is billed, when it pays off and how to structure prompts to get the cache hit rate up.