Treats token usage as a cost signal that needs accountability, not a trophy for the loudest internal dashboard.
The cloud-cost framing is important because LLM usage is becoming an operating expense that finance and engineering both need to understand.
This is tokenmaxxing through an AI FinOps lens: track ownership, find waste, and treat token burn like any other cloud cost that can drift.
Use it to support pages about AI FinOps, token waste, and cost accountability rather than culture or productivity scoreboards.
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.
Portkey-AI/gateway
An AI gateway for routing across LLMs with guardrails, provider abstraction, and an OpenAI-compatible API surface.
A practitioner reading of June's CNCF news: 66% of orgs running GenAI inference do it on Kubernetes, DRA went GA, gang scheduling landed natively, and Nvidia and Google donated their DRA drivers — self-hosted inference is complete.
Ramp closed a $750M round at a $44B valuation and is launching AI token spend management, procurement agents, and accounting agents on top of $1B+ annualized revenue and 70,000+ customers.
North introduced Noros, a FinOps agent designed to answer cloud-cost questions in real time and route them through specialized analysis agents.