Inference Cost Calculator – Understand and Control Your AI Usage Spend
Inference—the process of running a trained model to answer questions, generate content or power product features—often becomes the largest ongoing cost in real-world AI systems. Every input token and output token your application sends to a model has a price, and self-hosted inference adds hardware and infrastructure on top. The Inference Cost Calculator from MyTimeCalculator helps you turn these low-level details into clear, actionable cost estimates.
This calculator is designed for teams building chatbots, copilots, retrieval-augmented generation (RAG) systems, analytics assistants, creative tools and more. With a few inputs you can see how cost changes with prompt size, response length, number of users, monthly requests or GPU hours.
1. Token-Based Inference Pricing: Input vs Output
Many modern AI providers charge separate rates for input tokens (your prompts, context windows and tool calls) and output tokens (the model’s responses). The Inference Cost Calculator uses that same structure:
- Input tokens: Everything you send to the model, including instructions, prior conversation, retrieved documents and system messages.
- Output tokens: Everything the model generates in response, including text, code or structured outputs.
For each scenario, the calculator multiplies your token counts by the configured prices, then reports total cost, per-request cost and normalized rates per 1K and per 1M tokens so you can compare models or providers directly.
2. Per-Request and Batch Views of Inference Cost
There are two natural ways to reason about inference spend, and the calculator gives you dedicated tabs for each:
- Per request / session: Ideal when you know a typical prompt and response size. You enter input and output tokens per request, specify how many requests or sessions you expect, and see total cost and average cost per request.
- Batch / monthly usage: Best when you already have usage logs or a forecast of total monthly tokens. You enter aggregate input and output tokens and optionally the number of requests to see normalized metrics and cost per request over the batch.
Switching between these modes helps bridge the gap between individual user experience and overall platform economics.
3. Self-Hosted GPU Inference Costs
Not all inference runs through managed APIs. Many teams deploy models to their own GPU clusters in the cloud or on-premise. In those cases, cost is driven primarily by GPU hours multiplied by an hourly rate for each GPU type. The GPU tab in this calculator allows you to:
- Choose a standard GPU type such as A100, H100 or L40S, or define a custom GPU.
- Enter how many GPU hours are used to serve inference traffic.
- Optionally provide total tokens served to compute an effective per-token hardware cost.
This makes it easier to compare self-hosted inference to API-based pricing on a per-token or per-request basis.
4. How to Use the Inference Cost Calculator
- Select a pricing mode. Use OpenAI-style to work with built-in input/output token prices, or choose Custom provider / model to define your own per-1K or per-1M token rates.
- Choose a calculation tab. The Per Request / Session tab is great for user-level cost, the Batch / Monthly Usage tab is ideal for aggregate forecasting, and the GPU-Based Inference tab is for self-hosted deployments.
- Enter token or GPU usage. Provide realistic estimates for input and output tokens per request or for total usage. For GPU mode, enter hours and optional token counts.
- Click the calculate button. The calculator shows total cost, breakdowns and normalized rates, plus a natural-language summary of your configuration.
- Adjust assumptions and compare. Change model, provider or token counts to see how cost scales with richer prompts, longer responses or higher traffic.
5. Practical Scenarios for Inference Cost Estimation
Some common ways teams use this Inference Cost Calculator include:
- Product pricing: Determining whether a subscription or per-seat price covers expected AI usage per user.
- Feature rollout decisions: Estimating how much a new AI-powered feature will cost at different adoption levels.
- Model selection: Comparing the cost of smaller and larger models given the same workload and desired quality.
- Cloud vs self-hosted comparison: Measuring whether running a model on your own GPUs is more or less expensive than a managed API, once hardware, utilization and engineering overhead are considered.
6. Making Your Inference Cost Estimates More Accurate
Early estimates are always approximate, but a few practices can improve accuracy over time:
- Log token usage per request and aggregate it daily or monthly to calibrate your forecasts.
- Track typical prompt and response sizes for different features and user segments.
- Experiment with prompt optimization and truncation to reduce input tokens without sacrificing quality.
- Periodically review provider pricing pages, since model prices and available tiers can change over time.
Related Tools from MyTimeCalculator
- Embedding Cost Calculator
- Model Training Cost Calculator
- Token Counter Calculator
- API Cost Calculator
Inference Cost Calculator FAQs
Frequently Asked Questions
Quick answers to common questions about estimating inference cost, token-based pricing, GPU-based deployment and how to interpret the calculator’s outputs.
Input tokens represent everything you send to the model, including prompts, instructions and context; output tokens represent everything the model generates in response. Many providers charge different prices for input and output tokens. The calculator lets you set or use built-in prices for both, then combines them into a total cost and effective per-token rates for your specific workload.
The per-request tab is as accurate as your assumptions about prompt and response size. In practice, requests can vary widely, especially if users paste long documents or trigger multi-step tool calls. Once your system is live, using actual token usage logs will give more precise batch estimates. You can then plug those values into the batch tab to refine your cost model and align it with real-world data.
Yes. In custom mode you can enter any provider and model name, then specify input and output prices per 1K or per 1M tokens. The calculator treats this configuration the same as the built-in OpenAI-style pricing and reports total cost, cost per request and effective per-token rates. This makes it easy to compare different vendors or internal deployments side-by-side using a common cost metric.
GPU-based inference cost starts from hardware usage: GPU hours multiplied by an hourly rate. If you also track the number of tokens served over those hours, you can compute an effective cost per 1K or 1M tokens and compare it directly to token-based API prices. The GPU tab in the calculator supports this by accepting both GPU hours and optional token counts, then reporting normalized per-token hardware cost for your chosen deployment and utilization level.
No. The Inference Cost Calculator focuses on model usage cost (token pricing) and GPU hardware cost for self-hosted scenarios. Storage for logs or embeddings, networking, vector databases, orchestration and engineering time are not included. For a complete financial picture you should combine inference cost estimates with your other infrastructure and staffing costs, based on how your system is architected and deployed.
It is a good idea to review and update your inference cost assumptions whenever you change model versions, add new AI-powered features, significantly increase traffic or when providers update their pricing. Running periodic checks with fresh usage logs and updated price data will help you avoid surprises and keep your AI budget aligned with actual user behavior.