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…
How to Build a Budget-Aware AI Feature (With Code)
The cost of running Large Language Models (LLMs) can quickly spiral out of control, with many teams facing unexpected bills of $5,000 or more per month. This is often because they have no clear way to attribute costs to specific features or departments, making it impossible to identify areas where…
FOCUS for AI: Exporting LLM Costs to Your FinOps Platform
The FOCUS standard for AI cost management has become a crucial aspect of financial operations (FinOps) for companies relying on Large Language Models (LLMs). Without a standardized framework, tracking and attributing LLM costs to specific features, departments, or projects can be a daunting task…
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…
How SaaS Companies Are Controlling AI Costs at Scale
The cost curve problem is a harsh reality for many SaaS companies: as their revenue grows, their AI spend grows even faster. A typical SaaS company might see their AI costs increase by 25% every quarter, while their revenue only grows by 15%. This disparity can quickly add up, with some companies…
The AI Efficiency Score: One KPI to Rule Your AI Budget
Most finance teams struggle to understand the true impact of their AI spend, with a staggering $10,000 per month being a common budget for Large Language Model (LLM) costs. This lack of visibility is exacerbated by the fact that AI spend alone tells the board nothing useful, as it does not account…
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…
Semantic Caching: How to Cut OpenAI Costs by 40%
Most teams using Large Language Models (LLMs) like OpenAI have no idea they are wasting up to 40% of their budget on redundant queries. A $2,000/month OpenAI bill tells you nothing about which features are burning your budget, and teams often realize too late that they are paying for the same query…
AI ROI for Finance Teams: Turning Spend into Business Outcomes
Most finance teams struggle to understand the true cost of their Large Language Model (LLM) investments, with a staggering $15,000 per month spent on average without clear visibility into the return on investment (ROI). This lack of transparency leads to difficult conversations with the board, as…
Your AI spend, broken down in 2 minutes
10-day free trial. No credit card required.
Start free trial →