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.
Detecting Eval Contamination: When Tests Drain Your AI Budget
When it comes to managing AI budgets, one of the most frustrating and costly issues is eval contamination. This occurs when test data or evaluation scripts inadvertently drive up AI costs, often without the knowledge of engineering or finance teams. A typical example of eval contamination is when a…
Prompt Caching vs Semantic Caching: When to Use Each
When working with Large Language Models (LLMs), caching is a crucial strategy for reducing costs and improving performance. However, many teams struggle to choose between two fundamentally different caching strategies: prompt caching and semantic caching. The wrong choice can result in wasted…
MCP Attribution: Track Costs Across Multi-Agent Workflows
The multi-agent cost blindspot is a pervasive issue in AI development, where the lack of visibility into costs across different agents and workflows leads to unexpected bills and budget overruns. A typical example is a conversational AI platform that uses multiple agents to process user requests…
5 Types of AI Waste Draining Your LLM Budget
Most engineering teams using Large Language Models (LLMs) have no idea how much of their budget is being wasted on unnecessary costs. A $5,000/month OpenAI bill tells you nothing about whether it's due to context bloat, model overkill, or cache misses. For instance, a company like Meta uses LLMs to…
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