LLM Cost Estimator
Estimates costs for LLM API usage by converting token counts into dollar amounts using a curated pricing catalog from major providers. Operates without needing access to billing APIs. Developers and AI engineers use it to forecast expenses for inference runs, batch processing, and model fine-tuning in production planning.
Overview
The LLM Cost Estimator MCP server provides a straightforward way to calculate projected spending on large language model APIs. Users input token counts for prompts and completions, and it applies rates from a maintained catalog covering providers like OpenAI, Anthropic, Google, and others. This enables cost predictions before actual usage, aiding in budget allocation without integrating billing systems.
Key Capabilities
- Token-based cost calculation: Converts input/output token counts into estimated USD costs using current pricing data from a transparent catalog.
- Provider support: References rates for multiple LLM vendors, updated periodically for accuracy.
- No external dependencies: Functions independently, avoiding API keys or billing access for providers.
Use Cases
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Project budgeting: An AI developer estimates costs for a customer support chatbot handling 1 million tokens daily across OpenAI GPT-4 and Claude models to set monthly budgets.
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Experiment planning: ML engineers calculate expenses for fine-tuning runs with 500k training tokens on various providers to select cost-effective options.
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Production scaling: Teams forecast inference costs for deploying a RAG system processing 10M tokens weekly, comparing providers like Gemini and Llama APIs.
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Cost optimization audits: Analyze historical token logs from logs to retroactively estimate spend and identify high-cost patterns.
Who This Is For
- AI/ML developers building LLM-powered applications who need preemptive cost insights.
- Engineering managers tracking inference budgets in cloud environments.
- Data scientists experimenting with multiple providers for model selection.
- Product teams integrating cost projections into financial planning for AI features.