AI Image Generation Cost & Speed Calculator
Estimate Costs and Speed for AI Image Generation Projects
Plan your AI image generation projects better by calculating costs based on model types, image resolution, batch sizes, and hosting options. This calculator helps AI developers, product managers, and researchers budget and optimize workflows.
AI Image Generation Cost & Speed Calculator
Estimate costs and generation times for your AI image projects.
About This Tool
The AI Image Generation Cost & Speed Calculator is a financial and performance modeling tool for anyone working with generative image models. Whether you're using a commercial API like DALL-E or self-hosting an open-source model like Stable Diffusion, understanding the cost and time per image is critical for budgeting and architecture decisions. This calculator helps you compare these two primary approaches. For API-based models, you can input the provider's per-image cost to forecast your monthly bill. For self-hosting, you can select a GPU type and model to estimate the generation time and the resulting cost-per-image based on the GPU's hourly rate. It empowers developers and product managers to make data-driven decisions on whether to build or buy, how to price their own AI features, and how to scale their image generation pipelines cost-effectively.
How to Use This Tool
- Select the AI model you plan to use.
- Choose the desired image resolution.
- Enter the total number of images you expect to generate per month.
- Select your cost model: "Pay-per-Image API" or "Self-Hosted GPU".
- If using an API, enter the provider's cost per image. If self-hosting, select the GPU you will use.
- Enter your desired batch size for generation.
- Click "Calculate" to see the estimated monthly cost, cost per image, and average time per image.
In-Depth Guide
Understanding Image Generation Speed
Image generation speed is typically measured in "iterations per second" or "steps per second." A diffusion model generates an image over a series of steps (e.g., 20-50 steps). The total time is `(number of steps) / (steps per second)`. Performance depends on the model size, image resolution, and GPU power. A more powerful GPU like an H100 can perform these steps much faster than a consumer card like the T4.
The Economics of Self-Hosting
Self-hosting an open-source model like Stable Diffusion gives you control and can be very cost-effective at scale. The cost-per-image is simply the hourly cost of your GPU divided by the number of images you can generate in an hour. This calculator helps you estimate that throughput. However, you must also factor in the engineering time for setup, maintenance, and building a scalable inference service.
When to Use a Commercial API
Using an API from OpenAI (DALL-E), Midjourney, or Stability AI is the fastest way to get started. You have zero infrastructure to manage and instant access to state-of-the-art models. This is ideal for prototyping, applications with low to moderate volume, or when you need access to a proprietary model's specific aesthetic (like Midjourney). The trade-off is a higher cost per image and less control over the generation process.
The Impact of Batch Size
GPUs are parallel processors. They are most efficient when given a "batch" of work to do at once. Generating a batch of 8 images is not 8 times slower than generating one; it might only be 2-3 times slower. This is because the GPU can process the steps for all images in the batch in parallel. Increasing your batch size is a key technique for maximizing throughput and lowering your per-image cost on self-hosted infrastructure. The main constraint on batch size is your GPU's VRAM.