Guides
How to spend less on AI, and keep it safe.
What actually drives a model bill, the techniques that lower it (starting with those that never change an answer), and how to keep sensitive data out of prompts. Written by the team that builds the Tessarac AI Cost Optimizer.
AI Cost Optimizer
- How to reduce LLM API costs: a practical checklist A practical checklist for lowering what you spend on OpenAI, Anthropic, Gemini and other model APIs: measurement, caching, routing, batching and governance. Read the guide
- What is an LLM gateway, and when do you need one? An LLM gateway sits between your applications and AI model providers. What it does, what to look for, and when one pays for itself. Read the guide
- LLM model routing: sending each request to the least costly model that can answer it Most requests do not need your most capable model. How difficulty-based model routing works, where it goes wrong, and how to route without hurting quality. Read the guide
- Prompt caching explained: Anthropic, OpenAI and Gemini compared How prompt caching works on each major provider, what has to stay identical for a cache hit, and how to structure prompts so repeated context costs less. Read the guide
- LLM batch APIs: the discount for work nobody is waiting on Anthropic, OpenAI and Google all price batch requests below real-time ones. What the batch tiers trade away, which workloads fit, and how to use them safely. Read the guide
- Semantic caching for LLMs: answering repeat questions without a model call Response caching answers identical requests; semantic caching also answers questions that mean the same thing. How it works, where it is safe, and the traps. Read the guide
- AI data loss prevention: keeping sensitive data out of LLM prompts Card numbers, credentials, customer records and source code end up in prompts. How data loss prevention for LLM traffic works and how to roll it out. Read the guide
- AI costs out of control? Budgets, attribution and visibility for LLM spend When the AI line on the invoice grows faster than anyone can explain, start with attribution and budgets. How to see who spends what, and cap it safely. Read the guide