#token-optimization
5 posts tagged with #token-optimization
Every article below is hand-written, technically reviewed, and focused on token-optimization. Posts cover real-world architecture decisions, code-level implementation patterns, and trade-offs you'll only discover after shipping production systems.
AI and Machine Learning Agent Per-Task Cost Calculation [2026]: Retries, Tools, Caching
A spreadsheet-ready expected-cost model for agent workflows that includes retries, tool-call fanout, context growth, and caching. Plus hard budgets you can actually enforce.
Developer Tools AI Agent Cost Per Task [2026]: Token Budgets & Break-Even Math
Concrete per-task cost breakdown for Aider, Claude Code, and OpenHands — covering token overhead per PR, monthly burn at team scale, and the break-even formula for hosted APIs vs local models.
Developer Tools OpenCode vs Claude Code Token Overhead: 4.7x Gap Tested [2026]
Claude Code sends 33,000 tokens before reading your prompt. OpenCode sends 7,000. Here's the cache economics, the multiplier stack, and the break-even math for teams.
AI and Machine Learning Reduce LLM API Costs 60%: 6 Techniques [2026]
A technique-by-technique playbook with real cost math for cutting LLM API bills in production — covering semantic caching, prompt compression, model routing, batch APIs, and context tiering with 2026 pricing.
AI and Machine Learning Netflix Headroom: How to Cut AI Agent Costs 10x in Production [2026]
Netflix open-sourced Headroom — a context optimization layer that slashes LLM inference costs by up to 10x. Here's how the architecture works and how any team can apply the same patterns.