// the blog
Keeping AI costs under control
Field notes on LLM cost control - spend caps, per-user limits, spike alerts, and monitoring Claude and GPT bills before the invoice lands.
- FundamentalsJul 28, 2026 · 7 min
How LLM API Pricing Actually Works (Tokens, Explained)
A plain-English breakdown of how LLM API pricing works: what a token is, why input and output are billed differently, and how to turn a rate card into a real cost estimate.
- ComparisonsJul 27, 2026 · 8 min
Best Tools to Monitor and Cap LLM Costs (2026)
A buyer's guide to LLM cost management tools in 2026: the categories that exist, what to look for in each, and how observability platforms differ from real spend enforcement.
- MonitoringJul 26, 2026 · 7 min
Monitoring OpenAI API Costs in Production
How to monitor OpenAI API cost in real time: capture usage per call, attribute spend by feature and user, and catch a spike before it reaches the monthly invoice.
- ControlsJul 25, 2026 · 6 min
Token Usage Limits: Capping Tokens Across Claude and GPT
Token usage limits are the most direct lever on LLM cost. How to bound input and output tokens across Claude and GPT - per call, per user, and per run.
- ComparisonsJul 24, 2026 · 6 min
Provider Usage Limits vs. a Real Spend Cap
OpenAI and Anthropic usage limits look like cost protection but work differently from a real spend cap. Here's what each one does, where provider limits fall short, and when you need both.
- Model strategyJul 23, 2026 · 7 min
Claude Opus vs. Sonnet vs. Haiku: A Cost Guide
A practical cost comparison of Claude Opus, Sonnet, and Haiku: how their per-token prices differ, what that means for real workloads, and how to route work to the cheapest model that fits.
- MonitoringJul 22, 2026 · 6 min
AI Budget Alerts: Catch Cost Spikes Before the Invoice
AI budget alerts turn a monthly surprise into a same-minute page. How to detect LLM spend anomalies, set thresholds that don't cry wolf, and route alerts to Slack.
- Model strategyJul 21, 2026 · 6 min
Prompt Caching: How Much It Actually Saves
Prompt caching can cut LLM input cost dramatically - but only for the right workloads. How prompt caching works, what it really saves, and the mistakes that quietly bust the cache.
- MonitoringJul 20, 2026 · 6 min
Cost Attribution: Tracking LLM Spend by Feature and User
Cost attribution turns a monthly LLM total into an answerable question: who and what spent it. How to tag every call and roll spend up by feature, model, and user.