#cost
15 posts
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Stop Paying Frontier-Model Prices for Work a Cheaper Model Handles
Find the agent calls where a cheaper model would likely hold, priced in dollars against your own trace history, so you stop paying frontier rates for mechanical work.
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Instrument Your AI Agent, Then Find Where the Money Goes
Patch your provider client in one line so the TokenJam SDK captures every LLM call to a local, on-disk trace, then run local analyzers that turn those traces into priced savings across your self-built agent.
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Half Your System Prompt Isn't Doing Any Work
System prompts quietly accumulate dead-weight tokens you re-pay on every call, and TokenJam's Trim lever scores which tokens carry little significance so you can see what to cut.
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The Prompt-Caching Discount Most Agents Leave on the Table
Prompt caching gives roughly 30-60% off the repeated prefix tokens your agent re-sends every call, and TokenJam measures your current cache usage and recommends where to place cache_control.
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Why Model Autorouting Savings Need a Proof Step
Model autorouting to a cheaper, smaller, or open-source model shows a big savings number before any work is redone. That figure is a prediction of your AI spend, not a result. Here's why LLM cost savings from an autorouted swap stay a hypothesis until you replay it on your own tasks and measure whether quality holds or regresses.
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What Actually Costs Money in an Agent Loop
A mechanism-level breakdown of where tokens get spent every turn an agent runs: input, output, cache reads vs cache writes, context bloat, tool overhead, fan-out, and retries.
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Quota, Not Cost: Why /cost Is the Wrong Number on Claude Max
Claude Pro and Max subscribers should track quota, their usage against the plan window, not dollar cost, and /cost misleads them because it prices tokens against an API rate card they never pay.
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Where Does Your Claude Code Quota Actually Go?
TokenJam is a local-first tool that reads your on-disk Claude Code transcripts and shows where a Pro or Max subscription's quota is spent per turn: re-reading context versus doing real work.
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Introducing TokenJam Bench: Benchmarks & Evaluations for Agents and LLMs
TokenJam Bench is an open-source tool to benchmark and evaluate LLMs and agents. Run a candidate model against an original on real, executable task suites and get a measured pass-rate, confidence intervals, and a holds-or-regressed verdict. Local, no signup.
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The problem with TokenMaxxing
TokenMaxxing is fun because someone else pays for it. Here's why the subsidy is ending, what Fable 5 just signaled, and how to find your own multiple.
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What is an agent loop?
Agent loops: the program that prompts your agent for you, checks its own work, and decides when to stop. The lineage from ReAct to orchestration, and why the loop is now the expensive part.
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Cost dashboards tell you the bill. They don't tell you what to change.
The gap between reporting agent cost and recommending what to do about it. Why an honest recommendation needs to be validated against the user's own data, and the recent research that makes that validation cheap.
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Where Your AI Agent Bill Goes: 5 Token Waste Patterns
Where your AI agent bill actually goes: the 5 token-waste patterns (context bloat, runaway loops, model overspend, and more) and the research that fixes each.
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Subsidized AI Is Ending: The Agent Cost Numbers Are Now Real
Uber burned its annual AI budget in 4 months; one team hit $1.3M in 30 days. The real agent-cost numbers, plus the June billing changes that end the subsidy.
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What is AI Agent Token Economics?
Agent token economics: understanding where tokens are spent, why agent costs spike unpredictably, and the optimization patterns (model cascading, prompt compression, semantic caching) for reducing spend without losing quality.