Guides
Practical, engineering-grade guides on LLM API costs, optimization strategies, and provider migration — written for teams running real workloads at scale. All guides are updated regularly against official provider pricing and API documentation.
How to Read LLM API Pricing Pages Without Getting Burned
Decode per-token units, input/output asymmetry, prompt caching rates, batch discounts, and hidden fees before they show up on your invoice.
9 min read
Batch API: The 50% LLM Discount You're Probably Not Using
What batch processing is, which providers offer it, when it applies, and the exact dollar savings at 10K, 100K, and 1M jobs per month.
10 min read
Prompt Caching ROI: When Anthropic's 90% Discount Actually Pays Off
Cache write vs read pricing, ephemeral vs extended lifetimes, break-even math, and a worked example showing $400/month saved on a single system prompt.
11 min read
5 Token Counting Myths That Cost Engineering Teams Real Money
Why "1 token = 4 chars" is wrong for code, why Claude and GPT tokens aren't interchangeable, and why output tokens are more expensive than input tokens.
9 min read
Migrating from OpenAI to Anthropic Without Breaking Production
SDK swap, request/response shape changes, prompt conventions, tool use format, streaming events, caching wiring, and a safe gradual rollout pattern.
12 min read
Looking for model cost comparisons? See the GPT-4o vs Claude 3.5 Sonnet comparison or use the token counter to measure your actual prompt sizes.