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AI Inference Cost Economics

AI Inference Cost Economics MCP Connector for Claude

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Calculate unit economics for AI model deployment, including cost per query, margins, and scale projections.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides specialized financial modeling tools for AI infrastructure. It allows users to estimate the direct operational cost of running models using get_unit_cost, analyze pricing viability with get_profitability_analysis, and project long-term savings through get_scale_economics_projection. Additionally, it helps balance performance and budget by using get_latency_cost_tradeoff to see how response time requirements impact total expenditure.

inferenceunit-economicsllm-opscost-modelingscalability

4 tools expose this connector's capabilities to your AI agent.

get_latency_cost_tradeoff

Analyzes the financial impact of choosing faster response times

get_profitability_analysis

Determines the financial viability of a specific pricing strategy

get_scale_economics_projection

Predicts how cost efficiency changes as the business scales its volume

get_unit_cost

Calculates the direct operational cost to process a single query

See how to talk to your AI agent using AI Inference Cost Economics.

What is the cost to run a 70B parameter model with a batch size of 32 and high hardware efficiency?

The estimated cost per query for a 70B parameter model with those parameters is $0.0012.

If I charge $0.01 per query and my cost is $0.002, what is my margin?

Your margin per query is $0.008, resulting in a margin percentage of 80%.

How much will my cost per query drop if I scale from 1 million to 10 million queries per month?

Scaling to 10 million queries per month will reduce your unit cost by 45% compared to your current volume.

Larger models require more memory bandwidth and compute, which increases the cost per query. You can use `get_unit_cost` to see how specific parameter counts impact your budget.

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