» Tag
cost optimization
20 postsYou Don't Need an LLM to Cluster LLM Traces: Seldon's Trace Audit
Seldon's Trace Audit clusters LLM gateway traces into reusable program groups using deterministic features and DBSCAN—no LLM needed, near-perfect accuracy.
Kimi K3 benchmark: Chinese AI is cheap at coding, not planning
Benchmark testing shows Chinese AI models are pricey planners but 19x cheaper coders than Western rivals, based on real pass/fail test runs.
Claude Code's Prompt Cache Cost Depends on How You Launch It
Claude Code prompt-cache costs vary by launch path: measurements reveal a large fixed overhead and a subagent fan-out cost multiplier.
CommitBrief — AI code reviews, right in your terminal
A provider-agnostic, local-first CLI that reviews your staged changes, a historic range, or a whole GitHub pull request. Zero telemetry, no server. Free and open source.
commitbrief.comQwen 3.8-Max and Claude Opus 5 show why benchmark scores don't predict cost
Qwen 3.8-Max and Claude Opus 5 benchmarks reveal why price-per-token no longer predicts real cost, and why cost-per-successful-task now matters more.
Persistent AI Agent Memory Is a Write-Path Cost Problem
AI agent memory costs are driven by write-path LLM calls, not retrieval. Engineering levers to cut cost and latency without sacrificing quality.
Wattage: An Offline Token-Cost Profiler and CI Gate for AI Agents
Wattage profiles AI agent token spend from OTel traces, prices waste in dollars, and gates CI on cost regressions — open-source and offline.
A Multi-Agent AI Game Cost $1,847 in One Weekend — Here's Why
A two-agent AI game cost $1,847 in one weekend. Breakdown of why multi-agent LLM costs multiply, plus the fixes that cut expensive calls.
Code Mode Cuts Agent Costs by 99.2%, Measured in Production
A production benchmark shows code-execution-based agent workflows cut token costs by 99.2% versus raw sequential MCP tool calls.
How I Cut Agent Token Costs 60% With a 4-Layer Infrastructure Fix
An engineer's 4-layer infrastructure fix — span tracing, tiered caching, prompt compression, and complexity routing — cut agent token bills by 60%.
7 lessons from building a hard spending cap for LLM APIs
Why LLM API bills silently explode: retry storms, inconsistent token reporting, streaming blind spots, and race conditions in spending caps.