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.
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…
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…
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…
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…
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