| | --- |
| | title: GPUandAPIcostestimator |
| | emoji: 🌍 |
| | colorFrom: indigo |
| | colorTo: green |
| | sdk: gradio |
| | sdk_version: 5.29.0 |
| | app_file: app.py |
| | pinned: false |
| | license: mit |
| | short_description: A comprehensive calculator for computational usage |
| | --- |
| | |
| | Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
| |
|
| | # Cloud GPU vs API Cost Comparison Tool |
| |
|
| | [](https://huggingface.co/spaces/delightfulrachel/GPUandAPIcostestimator?duplicate=true) |
| |
|
| | ## Description |
| | A comprehensive calculator to compare the costs between self-hosted cloud hardware (AWS, GCP) and managed API endpoints (OpenAI, Anthropic, TogetherAI) for running LLMs like LLAMA, Claude, DeepSeek and GPT. |
| |
|
| | This tool helps ML engineers and developers make informed decisions about deploying large language models by: |
| |
|
| | 1. Comparing cloud GPU hardware costs vs managed API costs |
| | 2. Calculating breakeven points for different usage patterns |
| | 3. Considering factors like model size, compute hours, token volume |
| | 4. Providing recommendations based on your specific workload |
| |
|
| | ## Features |
| | - Cost comparison across major cloud providers (AWS, GCP) |
| | - API pricing from leading LLM providers (OpenAI, Anthropic, TogetherAI) |
| | - Support for different model sizes (7B to 180B parameters) |
| | - Advanced options like reserved instances and spot pricing |
| | - Breakeven analysis to determine when cloud becomes cheaper than API |
| | - Visual comparison charts and detailed recommendations |
| |
|
| | ## Why Use This Tool? |
| | ### For ML Teams & Engineers |
| | - Make data-driven decisions between building inference infrastructure or using APIs |
| | - Understand cost implications for different model sizes and workloads |
| | - Optimize existing LLM deployment costs |
| | - Plan budgets for AI projects more accurately |
| |
|
| | ### For Management & Decision Makers |
| | - Visualize cost comparisons between build vs buy options |
| | - Understand the financial impact of different deployment strategies |
| | - Get clear recommendations based on your specific usage patterns |
| | - Make informed decisions about AI infrastructure investments |
| |
|
| | ## How It Works |
| | The tool considers several factors in its calculations: |
| | - **Compute Hours**: How many hours per month your model will run |
| | - **Token Volume**: How many tokens (input/output) you'll process monthly |
| | - **Model Size**: Memory requirements for different parameter counts |
| | - **Hardware Specs**: GPU types, memory, and pricing for different cloud instances |
| | - **API Pricing**: Current rates from major LLM API providers |
| | - **Advanced Options**: Discounts available through reservations or spot instances |
| |
|
| | ## Usage |
| | 1. Set your usage parameters (compute hours, tokens, model size) |
| | 2. Adjust advanced options if needed |
| | 3. Click "Calculate Costs" to see the comparison |
| | 4. Review the recommendation and cost analysis |
| |
|
| | ## About |
| | This tool helps you make data-driven decisions about whether to build your own infrastructure or leverage managed APIs for your LLM deployments. |
| |
|
| | Perfect for teams evaluating deployment options, budgeting for ML projects, or optimizing existing infrastructure costs. |
| |
|
| | ## Author |
| | Rachel Abraham at The Marmalade Group LLC | Data last updated: May 2025 |
| |
|
| | ## SDK Version |
| | sdk_version: 4.15.0 |
| | |
