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What is //Beforeyouship is a pre?

This tool helps developers and product managers estimate the actual costs of running applications powered by large language models. Rather than relying on headline pricing, it accounts for real-world factors like API retries, prompt caching efficiency, batch processing discounts, and expected user growth patterns. You can model costs across multiple providers including OpenAI's GPT-4o, Anthropic's Claude, Google's Gemini, and DeepSeek to compare spending across different scenarios. This is useful for anyone building LLM features and needs to understand financial implications before launch or at scale.

Key Features

Multi-model cost comparison

estimate expenses across GPT-4o, Claude, Gemini, and DeepSeek in one place

Retry and failure modelling

account for API retries that increase actual token usage beyond initial requests

Prompt caching calculations

see cost savings from caching repeated prompt sections

Batch processing discounts

factor in reduced rates when using batch APIs instead of real-time requests

Growth scaling scenarios

project costs under 3x, 10x, and custom user growth multipliers

Interactive calculator

adjust parameters and instantly see how changes affect total spending

Pros & Cons

Advantages

  • Covers practical cost factors that simple per-token math misses, like retries and caching
  • Supports multiple LLM providers so you can make informed provider choices
  • Free to use with no account required, making it accessible for initial planning
  • Growth scenario modelling helps forecast budget needs as usage increases

Limitations

  • Limited to comparing the specific models listed; doesn't cover newer or specialist models
  • Accuracy depends on your ability to estimate real-world retry rates and cache hit rates for your use case
  • No persistent project saving in the free tier, so you can't easily revisit or share previous estimates

Use Cases

Evaluating whether to build a feature using an LLM before committing resources

Deciding between providers (OpenAI versus Anthropic versus Google) based on projected monthly spend

Budgeting for a new product launch with LLM features and forecasting costs at different user scales

Analysing the financial impact of optimisations like prompt caching or batch processing on your application