MCP For Claude Marketplace
AI Human Feedback Cost Calculator

AI Human Feedback Cost Calculator MCP Connector for Claude

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Calculate RLHF and RLAIF cost structures, quality impact, and automation potential.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides tools to model the financial and operational costs of training AI models through human and AI feedback. Use rlhf_cost to establish baseline human-only costs, quality_impact to account for auditing and quality control overhead, and automation_potential to estimate savings when transitioning to RLAIF. You can also use resource_planning to determine the necessary workforce size and time requirements for your annotation tasks.

rlhfrlaifcost-modelingai-trainingannotation

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

quality_impact

Calculates how quality requirements and auditing increase the total budgetary requirement

resource_planning

Determines the required human workforce size based on time constraints

rlhf_cost

Determines the baseline total cost for a human-only feedback loop

automation_potential

Estimates the cost savings if a portion of the human feedback is replaced by AI feedback (RLAIF)

See how to talk to your AI agent using AI Human Feedback Cost Calculator.

What is the baseline cost for 10,000 annotations at $0.50 each with a complexity of 1.5?

The total RLHF cost for 10,000 annotations is $7,500.00, with an effective cost per unit of $0.75.

If my current human-only cost is $5,000 and I can automate 50% of tasks with AI at 10% of the human cost, what are my savings?

By transitioning to a hybrid model, your potential savings would be $2,250.00, resulting in a total hybrid cost of $2,750.00.

How many annotators do I need for 5,000 annotations if each person can do 100 per unit of time?

To complete 5,000 annotations, you would need 50 annotators to finish in 1 time unit, or 1 annotator would take 50 time units.

You can use the `rlhf_cost` tool. Provide the total annotation volume, the base cost per annotation, and a complexity multiplier to get the total cost and effective cost per unit.

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