Our story
Why we built COGScontrol
The problem
As AI-powered products grew in popularity, engineering teams found themselves with a new challenge: understanding and controlling AI costs. Unlike traditional infrastructure costs, AI spending is distributed across multiple providers, priced by usage metrics like tokens and API calls, and often difficult to attribute to specific products or teams.
Finance teams were asking questions that engineering couldn't answer: "What did it cost to serve our enterprise customers last month?" "Which feature is driving our OpenAI bill?" "Are we on track to hit our AI budget?"
The insight
We realized that AI cost management needed a purpose-built solution. Traditional cloud cost tools focus on infrastructure - VMs, storage, network. But AI costs are fundamentally different: they're driven by API calls to external providers, measured in tokens and requests, and need to be classified across multiple business dimensions.
We needed a tool that could ingest data from OpenAI, Anthropic, AWS Bedrock, and cloud providers - all in one place. A tool that could automatically classify costs and let you set budgets on the dimensions that matter to your business.
The solution
COGScontrol was born from this need. We built a platform that gives AI-first companies complete visibility into their AI spending, the ability to classify and allocate costs automatically, and the tools to set meaningful budgets and catch anomalies before they become problems.
Today, teams use COGScontrol to answer the questions that matter: "How much did we spend on Claude vs GPT-4 this month?" "What's the cost-per-query for our enterprise tier?" "Are we on track with our Q4 AI budget?"