Cognocient Blog
AI Cost Intelligence
Practical writing on controlling AI spend, attributing LLM costs, and making your AI budget work harder — new posts every Monday, Wednesday, and Friday.
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Context Window Optimization: Stop Paying for Tokens You Don't Need
Most teams building multi-turn applications with Large Language Models (LLMs) face a daunting challenge: context window optimization. As users interact with their application, the context window grows exponentially, leading to a significant increase in token usage and, subsequently, costs. For…
Runaway Agent Loops: How to Stop a $50K Weekend Surprise
The Monday morning story is one that many engineers and finance leads dread: walking into the office to discover a massive, unexplained spike in AI costs over the weekend. For one team, this story became a harsh reality when they found out that their AI agent had run amok, racking up a staggering…
Presenting AI Costs to the Board: A Template That Works
Presenting AI costs to the board is a daunting task, especially when the numbers are unclear and the ROI is uncertain. A typical board meeting may involve a CFO trying to justify a $100,000 monthly AI spend, only to be met with skepticism and concerns about the lack of transparency. The board wants…
Model Routing: Automatically Choosing the Cheapest Model That Works
The use of Large Language Models (LLMs) like GPT-4 has become increasingly prevalent in various applications, from chatbots to content generation. However, the cost of using these models can be prohibitively expensive, especially when using the most advanced models like GPT-4 for every task. For…
AI Cost Visibility for Engineering Managers
The engineering manager's AI cost problem is a pressing concern that affects not only the team's budget but also their ability to deliver projects on time. A typical engineering team using Large Language Models (LLMs) can spend upwards of $10,000 per month, with some teams spending as much as…
AI Budget Planning for 2026: A Finance Team Playbook
Most finance teams struggled to set accurate AI budgets in 2025, resulting in an average of $15,000 in unexpected costs per quarter. This was largely due to the lack of visibility into AI spend, making it difficult to forecast costs and plan accordingly. The primary challenge was that AI costs are…
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