Augment collects observability platforms that can make coding-assistant usage, quality, and cost easier to compare.
It turns token spend from an abstract bill into something teams can inspect alongside latency, traces, evaluations, and workflow outcomes.
Useful tokenmaxxing starts with instrumentation: track where assistants burn context, where they save review time, and where routing should change.
Treat it as a vendor-curated tool map, not a neutral ranking; the value is the observability checklist it implies.
BerriAI/litellm
An OpenAI-compatible gateway and SDK for calling many model providers with budgets, logging, load balancing, guardrails, and cost tracking.
langfuse/langfuse
Open-source LLM engineering platform for observability, traces, metrics, evals, prompt management, datasets, and playground workflows.
promptfoo/promptfoo
A CLI and CI workflow for testing prompts, agents, and RAG systems across models, with evals and red-team style checks.
AI accountability startup Lanai debuted Token Tuner, a beta that scores each employee's efficiency by matching token usage and model choice to task complexity — peers burned 10x the tokens for half the efficiency in one beta.
Gourav Singla details what an LLM app needs instrumented when it returns HTTP 200 and still fails: per-workflow token logging, finish-reason tracking, and tiered alerts that catch cost anomalies before the invoice explains them.
Mercury launched Agent Cards through Mercury Spend: virtual cards an AI agent spends from inside company-set rules, with transactions outside them declined automatically and no way for the agent to raise its own limit.