AI SaaS Cost Analyzer

AI SaaS Cost Analyzer MCP Connector for Claude

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Decomposes AI feature costs into actionable unit economics.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides deep analytical insights into the unit economics of AI-driven software. It connects AI agents to your cost data, allowing them to calculate exact profitability per feature. Using tools like get_feature_unit_economics and analyze_infrastructure_overhead, agents can determine cost per use, cost per user, and how shared infrastructure costs are distributed. You can also use compare_feature_efficiency to find expensive outliers or simulate_optimization_impact to predict how techniques like caching or prompt compression will improve your margins.

unit-economicssaascost-analysisai-infrastructureprofitability

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

analyze_infrastructure_overhead

Answers how much of the shared cloud infrastructure is being consumed by specific AI workloads

compare_feature_efficiency

Compares the cost-efficiency of multiple features to identify "expensive" outliers

get_feature_unit_economics

Calculates the core cost metrics for a specific feature

simulate_optimization_impact

Predicts how much profit would increase if specific cost-saving measures were applied

See how to talk to your AI agent using AI SaaS Cost Analyzer.

What is the cost per use for feature 'image-gen-v2'?

The cost per use for 'image-gen-v2' is $0.045.

Compare the efficiency of 'chat-bot' and 'summarizer'.

The 'summarizer' is more efficient with a cost per use of $0.01, while 'chat-bot' costs $0.05 per use.

How much profit will I gain if I apply 20% prompt compression to 'text-analyzer'?

Applying 20% prompt compression to 'text-analyzer' is projected to increase profit by $1,250.00.

It aggregates direct API costs, compute, and storage, then adds a proportional share of infrastructure overhead using `get_feature_unit_economics`.

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