Articles, tutorials, and guides tagged Cost Optimization.
Reasoning traces are the feature everyone turned on this month and nobody planned for. Handled wrong, they quietly triple your token bill, dump intermediate reasoning full of customer data into your logs, and break your tool loop in a way that looks like the model got dumber. Here is how to treat extended thinking as something you manage at the boundary, not something you print.
LLM spend is per request and wildly variable, so a single runaway agent or one heavy tenant can externalize its cost straight onto your margin. Cheaper models and caching lower the average, but nothing stops the bill. A per-tenant spend ledger with the cap enforced before the call does.
A single tool call can dump a whole file, a full API response, or a thousand log lines straight into the context window. The agent needed one field. Now every turn after that re-pays for the whole blob. Offloading the payload to a store and keeping only a handle in context fixes both the cost and the window.
Most of what your agent gets asked, it has already answered. A semantic cache reuses those answers on near-identical questions, cutting cost and latency, as long as you build the guardrails that stop it from serving the wrong one.
Sending every request to your most expensive model is the fastest way to burn budget. Here is a practical routing and cascade pattern that sends each request to the cheapest model that can actually handle it.