Practical guides on reducing AI costs and optimizing token usage.
The cheapest advice in AI cost tooling is "use a smaller model." It is also the most dangerous. Before erabot recommends a model swap, it replays your real inputs through both models and lets a judge decide. On our own audits, it rejected 12 of 31 proposed swaps.
Every AI cost tool on the market operates at runtime — it reports the bill after the code ships. A 2026 review of the category admitted the gap out loud: no tool does code-level, pre-deployment analysis. That gap is the whole product.
Stanford research finds re-sent context accounts for 62% of agent inference bills. That waste does not live in your model choice — it lives in your code, and it compounds on every step of every agent run. Here is where it hides and how to find it.
We pointed erabot’s agentic auditor at the chat path of a widely-used open-source enterprise RAG platform. It read 18 files, cleared two false positives, and surfaced two cross-file cost bugs a line-level linter can’t see: a frontier model doing bulk summarization, and a prompt cache silently busted by message ordering.
How we caught our own circular evaluation, rebuilt the ground truth independently, and engineered detection recall from 0.42 to 0.73 with measurements instead of vibes — including a detection pass we built, measured at P=0.14, and deleted.
We replayed real traffic through model downgrades across five task classes. Blanket "use a cheaper model" advice was wrong for three of them, and on two the cheap model was more expensive. Data inside.
LibreChat, Dify, Flowise, MetaGPT, OpenHands, Onyx and more, run through the erabot agentic audit: 29 root-cause findings, 58 machine-generated patches, and a quality gate that rejected 12 of our own agent's suggestions.
We open-sourced the tree-sitter detector and Typer CLI behind erabot.ai under MIT. Install it with pip, run it locally, read the code on GitHub. Here is what we kept open, what stays closed, and why.
Long context windows from Claude and GPT-4 seem like a superpower — until you see the bill. Learn why context length is the biggest hidden cost driver in AI applications.