AI Inference Cost & Performance Simulator
Estimate costs, latency, and throughput for self-hosting LLMs.
Deploying LLMs? Our simulator helps you understand the trade-offs between cost, latency, and throughput. Configure your model, GPUs, and traffic to find the most cost-effective setup for your AI application, moving beyond simple cost estimation to true performance simulation.
AI Inference Cost Calculator
Estimate the monthly cost of deploying and scaling a large language model for real-time inference.
About This Tool
The AI Inference Cost & Performance Simulator is an essential tool for any business or developer deploying machine learning models at scale. While training gets much of the attention, the ongoing cost of running a model in production (inference) is often the largest expense. This tool demystifies that cost by allowing you to simulate different scenarios. By inputting your model's size, expected traffic, and hardware, you can get a clear estimate of your monthly operational expenditure. More importantly, it helps you understand the critical trade-off between latency (how fast a single user gets a response) and throughput (how many users you can serve). This is crucial for pricing your SaaS product, securing a budget for a new AI feature, or choosing between a pay-per-use API and self-hosting. It empowers you to build a sustainable and profitable AI application by simulating its core cost and performance drivers from day one.
How to Use This Tool
- Enter your expected average traffic in Queries Per Second (QPS) and the average number of tokens your model will generate per query.
- Use the slider to set the size of your model in billions of parameters.
- Select the GPU model you plan to deploy on from the dropdown.
- Set your target GPU utilization to leave a buffer for traffic spikes.
- Click "Calculate Inference Costs" to see the required VRAM, number of GPUs, estimated latency, and monthly cost.
In-Depth Guide
Simulation Step 1: Fitting the Model in VRAM
The first constraint in AI inference is VRAM (Video RAM). A model's weights must be loaded into the GPU's memory. A simple rule of thumb for a model using 16-bit precision (FP16/BF16) is that it requires roughly 2 gigabytes of VRAM for every billion parameters. So, a 7-billion parameter model needs about 14GB of VRAM. Our simulator estimates this for you. If the required VRAM exceeds the GPU's available VRAM, you must choose a larger GPU or use quantization.
Simulation Step 2: Throughput vs. Latency Trade-off
Once the model fits, the next simulation is speed. Throughput (measured in tokens/second) and latency (the time for the first token) are in constant tension. Maximizing throughput with large batches is great for offline jobs and lowers cost, but it increases latency. For a real-time chatbot, you must minimize latency, which often means using smaller batches and having a lower overall throughput, increasing the cost-per-user. This simulator helps you visualize that trade-off.
Choosing an Inference-Optimized GPU
Not all GPUs are equal. Training-focused GPUs like the A100 and H100 are powerful but can be overkill for inference. NVIDIA's L4 and T4 GPUs are specifically designed for inference, offering high efficiency and a better performance-per-dollar for many applications. This simulator helps you see that difference in dollar terms.
Self-Hosting vs. Managed APIs
The cost of self-hosting, which this tool helps you simulate, is just one part of the equation. You also need to account for the engineering overhead. For many teams, using a managed API from OpenAI, Anthropic, or Google is a better starting point. Self-hosting becomes viable when you need unparalleled control or your scale is so massive that the cost savings outweigh the operational burden.