SaaS AI API Compute
Predict monthly inference operating costs instantly. Compare high-logic LLMs to ultra-fast inference models, accounting for context caching discounts.
Tip: Change one assumption at a time to compare result deltas.
LLM Operations
Estimate production inference costs for AI products.
Intelligence Tier
Token Architecture
Choose an intelligence tier to view raw inference bills and potential margins.
How to use this calculator
The AI Compute Economics calculator reveals what wrapping an LLM actually costs at scale. It compares default inference (Input/Output token pricing) across standard models, while 'Context/SaaS' mode models advanced caching parameters used heavily by Anthropic and Google Gemini to slash RAG (Retrieval-Augmented Generation) costs.
1. Select your LLM Tier
Choose an ecosystem benchmark: Deep analytical (GPT-4o, Sonnet), or massive-scale ultra-fast logic (Llama 3, 4o-mini).
2. Load Token Constraints
Define an average prompt (input size) and expected generation length (output). Note: output tokens are frequently 3x-10x more expensive than inputs.
3. Scale Requests
Set your application's daily volume expected to hit the API endpoints.
4. Apply SaaS RAG Architecture
In 'Context/SaaS' mode, utilize Prompt Caching discounts (up to 90% cheaper). Model the price markup charged to your end-user to visualize margin.
Frequently Asked Questions
Why does Llama 3 on Groq reflect almost negligible costs?
Groq built LPUs (Language Processing Units) purely optimized for inference rather than relying on standard Nvidia GPUs. Llama 3 8B operations run extremely cheap on their hardware.
What is 'Context Caching'?
Instead of re-parsing massive 100k+ token documents every time a user chats with your app, OpenAI, Anthropic, and Gemini allow you to 'cache' the system prompt. They charge a deep discount (e.g. 50% to 90% off) for referencing cached tokens, dropping the cost of RAG drastically.
Why distinguish between Input & Output?
It computationally requires far more resources to linearly generate the next word (Output generation) than to ingest and embed a massive chunk of predefined text (Input parsing).