AI Pricing
Retrieves current pricing data for AI models and services from providers like OpenAI, Anthropic, and Google via the MCP protocol. Supplies structured details on per-token costs, fine-tuning rates, and compute fees. AI developers and ML engineers use it to estimate expenses and compare options in LLM-based projects.
Overview
The AI Pricing MCP server (@lulzasaur9192/mcp-ai-pricing) delivers programmatic access to up-to-date pricing for AI models and APIs from major providers including OpenAI, Anthropic, Google Cloud AI, and others. It returns structured JSON data on inference costs, training fees, and usage-based pricing through Model Context Protocol tool calls, enabling precise cost calculations without browser lookups.
Key Capabilities
The server exposes MCP tools for pricing queries, including:
- Fetching input/output token rates for language models (get_model_token_pricing)
- Retrieving costs for embeddings, image generation, and audio processing (get_feature_pricing)
- Comparing equivalent models across providers (compare_pricing)
Responses include units (e.g., $/1M tokens), tiers, and effective dates for accuracy.
Use Cases
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Expense Forecasting: Query get_model_token_pricing('gpt-4o') to project costs for a production chatbot handling 10M tokens daily.
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Model Selection: Use compare_pricing('text-generation', providers=['openai', 'anthropic']) to pick between GPT-4o-mini and Claude Haiku based on $/performance.
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Fine-Tuning Budgets: Retrieve get_feature_pricing('fine-tuning', model='llama-3') for planning custom model training on Meta or Mistral.
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Vendor Switch Analysis: Track rate changes over time to evaluate migrating from GPT-3.5 to newer low-cost alternatives.
Who This Is For
- AI developers integrating paid APIs into apps
- ML engineers optimizing inference costs
- Product and finance teams monitoring AI vendor spend
- Consultants quoting AI solution prices to clients