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.
Integrating Cognocient with LangChain, CrewAI, and AutoGen
The integration challenge of tracking AI costs with frameworks like LangChain, CrewAI, and AutoGen is a significant pain point for many engineering teams. These frameworks abstract the API call, making it difficult to track costs and attribute spend to specific features or departments. For…
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…
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…
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…
Pre-Call Budget Enforcement: Why Post-Hoc Alerts Are Too Late
Most engineering teams have experienced the frustration of receiving a massive bill from their Large Language Model (LLM) provider, only to realize that a single feature or department has blown through their entire budget. A $2,500/month OpenAI bill tells you nothing about whether it's the chatbot…
How to Attribute LLM Costs by Feature in 2 Minutes
Most teams have no idea which feature is burning their AI budget. A $2,000/month OpenAI bill tells you nothing about whether it's the chatbot, the search feature, or the nightly batch job. This lack of visibility is a major problem, as it makes it impossible to optimize AI spend or make informed…
Your AI spend, broken down in 2 minutes
10-day free trial. No credit card required.
Start free trial →