AI Cost Model

Unlike traditional software, AI systems have inherently variable costs. Running an inference on a model costs money per token. Fine-tuning a model costs money per iteration. Storing embeddings costs money per vector. You need a cost model that maps business operations (a user interaction, a customer interaction, a processing job) to actual dollars spent.

A basic cost model is simple: OpenAI charges 0 per million input tokens and $30 per million output tokens. You can estimate that an average query uses 500 input tokens and generates 200 output tokens, costing roughly $0.008 per query. At 10,000 queries per day, that's $80 per day, $2,400 per month. That's your cost model.

Reality gets complicated. Different models have different costs. GPT-4 is expensive; GPT-3.5 is cheaper. Using Claude is different from using a self-hosted open-source model. Batch processing has different economics than real-time inference. You might need to understand the cost per operation across different model choices and make real-time routing decisions based on cost versus quality.

Then there's the strategic layer. You have a token budget as a business. If you allocate 10 billion tokens per month across all AI operations, that becomes a constraint. Some operations are high-value (customers will pay for premium features). Some are low-value (internal analysis). Your cost model informs resource allocation. Should you spend tokens optimizing internal data analysis, or should you allocate them to customer-facing features?

This gets into what researchers call "cost-to-completion," which asks: if I want a model to solve a problem (write code, analyze documents, generate recommendations), what's the minimum cost to achieve acceptable quality? Sometimes it's cheaper to use a smaller, faster model multiple times than a large, slow model once. Sometimes you want to use a cheap model for the first pass, then escalate to an expensive model for edge cases.

Advanced cost models factor in infrastructure expenses too. If you're running models on your own hardware, you need to amortize infrastructure costs across the inferences you're running. If you're using cloud APIs, costs are more transparent but potentially more expensive at scale. Some companies build hybrid models where they use APIs for low-volume, high-latency operations and self-hosted models for high-volume, latency-sensitive operations.

The competitive dynamics are brutal. As your product scales, cost per operation can become your most important metric. A product that's profitable at 10x scale might be catastrophically unprofitable if you don't optimize costs. Companies are desperately trying to figure out whether their AI-powered product is actually sustainable.