Every API power user has that one horror story: a batch job that looked harmless, ran overnight, and left a bill that made coffee taste different in the morning. A dev named u/SrMugre just shipped a tool that kills this problem before it starts.
Meet pCompiler, a prompt compiler with a built-in Cost and Latency Estimator. Feed it your compiled prompt, and it tells you exactly how many tokens you are about to burn, what it will cost, and how long the response will take. No surprises, no budget blowouts.
Here is the twist: run the --compare flag, and it generates a side-by-side comparison table across every model in your config. So instead of guessing whether GPT-4o or Claude or Gemini is the right pick for a 50,000-token batch, you see the price and latency differences in one glance.
How to use it
- 🔧 Install pCompiler and set up your
config.jsonwith model pricing rates per million tokens - 📝 Write and compile your prompt as usual
- Run the estimate command to see token count, cost, and expected latency
- 📊 Add
--compareto get the full model comparison table - Run
pcompile update-pricingto sync the latest API prices (they change constantly)
Pro tips
- Run the estimator before every batch job, not just big ones. Small prompts at high volume add up faster than you think
- Keep your pricing config updated weekly. Model providers change rates without announcements, and stale numbers defeat the purpose
- Use the comparison output to match model capability to task complexity. A classification task does not need the most expensive model in your lineup
Budget control is not glamorous, but it is the difference between an AI workflow that scales and one that bleeds money quietly. 💰 Check pCompiler out on GitHub and stop flying blind on API costs. 🚀
Frequently Asked Questions
Q: How do I prevent budget overruns on batch jobs?
Estimate the total cost upfront on a sample of your prompts to see the economic impact before running the full batch. Use the --compare flag to find the cheapest model that meets your speed requirements, so you can make an informed choice before committing.
Q: What does the –compare flag show?
It generates a cost and latency comparison across all your registered models for the same prompt. This helps you pick the most economical option for your specific use case without guessing or trial-and-error.
Q: How often should I update pricing with the CLI?
Run pcompile update-pricing monthly or quarterly since API rates change frequently. Keeping your config.json synced ensures your estimates stay accurate and your model decisions reflect current pricing.
Q: Is the token estimation reliable for budget planning?
Yes, the tool accurately calculates input tokens based on your compiled prompt, making it far more reliable than manual estimation. While actual token counts may vary slightly, it’s solid enough for forecasting and model selection.
The prompt compiler – How much does it cost ?
by u/SrMugre in PromptEngineering