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