Self-Hosted LLM Cost Calculator

Our Self-Hosted LLM Cost Calculator provides a comprehensive comparison between paying per token via major APIs (like OpenAI's GPT, Anthropic's Claude, or Google's Gemini) and managing the infrastructure yourself. To use it, you simply input your expected usage metrics.

You must specify:

• Average Token Count per Request: Estimate the typical input and output length (e.g., 2,000 tokens).
• Estimated Monthly Volume: How many requests do you anticipate making?
• Hardware Specs: Input your available GPU memory (e.g., 24GB A100) and desired latency targets.

The calculator then models the total cost, factoring in API rates, plus the depreciation, electricity, and maintenance costs associated with running open models like Llama or Mistral on your own hardware or cloud GPU instance.

To get the most accurate comparison from our calculator, think holistically about your application's needs. Optimization is key to reducing both API spend and hardware requirements.

• Quantization: If you are self-hosting, look into 4-bit quantization (e.g., using GGUF formats). This drastically reduces the VRAM needed to run models like Llama 3, allowing you to use less expensive hardware while maintaining high performance.
• Caching: If your application asks the same factual questions repeatedly, implement a simple database cache layer before calling the LLM. This saves tokens and computational cycles instantly.

Always benchmark latency goals (e.g., < 500ms response time). A theoretically cheaper API or self-hosted option is useless if it takes too long for your end-users to tolerate.