CostLynx Insights
Analysis for CTOs, platform engineers, and FinOps leaders operating multi-provider AI systems.
When your platform runs GPT-4.1, Claude Opus 4, and Gemini 2.5 Pro simultaneously, provider-level dashboards stop being useful. Here is how to govern inference cost across a heterogeneous model estate.
Token cost is an infrastructure metric. Cost-per-workflow is a business metric. Here is how to build the bridge — and how to set margin guardrails before a feature ships.
LLM spend can increase by 50x in minutes — a prompt injection, a runaway retry loop, or a misconfigured context window. Here is how to detect it before the invoice arrives.
Retrieval-Augmented Generation has four distinct cost layers, each with different optimization leverage. Most teams measure only one of them.
AI spend is now large enough to require the same internal financial controls as cloud infrastructure. Here is how to implement chargeback and showback without building a separate cost allocation system from scratch.
How providers charge for tokens and what it means for your bill.
Practical ways to control AI spend without slowing down product.
Optimize context length, caching, and model choice to cut costs.
Break down AI spend by team, project, and environment.
Catch spikes and misconfigurations before they hit the budget.