𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗖𝗼𝘀𝘁 𝗼𝗳 𝗔𝗜 𝗔𝗣𝗜𝘀
An API price on a website is not your real production budget.
Pricing pages show unit rates. They show the cost per million tokens or cost per image. These numbers are useful but incomplete.
A real product uses more than just one request. You must account for:
- Repeated context
- Tool results
- Cache writes
- Retries
- Duplicate submissions
- Failed media jobs
- Outputs users reject
I built a budget model for three workloads to see how much these factors change the math.
Standard LLM Applications A simple calculation might show $81 for 6,000 requests. But if you add a 3% retry rate and a 15% planning buffer, your cost hits $95.94. This difference grows as you scale.
Coding Agent Workflows Do not measure coding agents by the message. Measure them by the completed task. One task might involve:
- Reading source files
- Inspecting dependencies
- Running shell commands
- Processing command output
- Retrying failed steps
Two tasks with the same short answer can have different costs if one task requires reading a whole repository and the other only reads one file.
- Image Generation The cost of one accepted image is not the cost of one API call. If a user needs 2.4 attempts to get one image they like, your costs more than double.
To manage these costs, you need detailed records. For text, track request IDs, tokens, and retries. For media, track job IDs and failure stages.
I separate cost planning into four layers:
- Provider pricing (unit rates)
- Product usage (users and requests)
- Operational reality (retries and rejections)
- Budget buffers (safety margins)
A calculator is a planning tool. It cannot predict model quality or future price changes. Use it to build a baseline, then compare it to your actual billing dashboard.
Source: https://dev.to/cleandatadev/i-compared-the-real-cost-of-claude-code-openrouter-and-image-apis-1cip
Optional learning community: https://t.me/GyaanSetuAi