Batch-processing jobs have a nasty habit of teaching budget lessons the expensive way. One oversized prompt, one wrong model choice, and suddenly your API bill looks like a phone number.
A developer named Marcos Jimenez just shipped pCompiler, an open-source prompt compiler with a built-in Cost and Latency Estimator. The idea is simple: know what a prompt will cost you before it ever touches the API.
But here is the twist. The tool does not just estimate costs for one model. Run the --compare flag, and it generates a side-by-side comparison table across every model you have registered. So instead of guessing whether GPT-4o or Claude Sonnet is cheaper for your specific prompt, you see the numbers instantly.
How to use it
- 🔧 Install pCompiler from the GitHub repo and set up your
config.jsonwith model pricing rates per million tokens - 📊 Run the estimate command on your compiled prompt to see token count, cost, and expected latency
- ⚡ Add the
--compareflag to get a full comparison table across all your registered models - 🔄 Use
pcompile update-pricingto auto-sync the latest API prices into your config (because providers change rates constantly)
Pro Tips
- If you run batch jobs, estimate the cost on a single prompt first, then multiply by your batch size. Obvious, but most budget blowouts happen because people skip this step.
- The
update-pricingcommand is your friend. Model pricing shifts every few weeks. Stale rates in your config mean your estimates are lying to you. - Use the comparison table to find the sweet spot between cost and latency. The cheapest model is not always the best pick when you need results in under 30 seconds.
🛠️ Grab it here: github.com/marcosjimenez/pCompiler
Frequently Asked Questions
Q: Is the Prompt Compiler worth using if I’m not doing batch processing?
Yep. Even for single prompts, knowing the cost upfront helps you pick the right model without guessing. The model comparison feature is especially helpful if you’re unsure which model offers the best balance of cost and speed for your specific needs.
Q: How do I use the model comparison feature?
Register your models in `config.json` with their pricing, then run the estimate command with the `–compare` flag. You’ll get a side-by-side table showing cost, latency, and token estimates across all registered models, makes it easy to pick the most cost-effective choice.
Q: How often should I update the API pricing?
Run `pcompile update-pricing` regularly, especially before big batch jobs, since API prices shift frequently. It keeps your estimates accurate without any manual work.
Q: Can this actually prevent budget overruns?
Absolutely. By estimating costs before you hit the API, you can catch expensive model choices, compare alternatives, and switch to a cheaper option before spending a dime. This is critical for batch processing where costs stack up fast, you can calculate your total spend upfront and avoid surprise bills.
The prompt compiler – How much does it cost ?
by u/SrMugre in PromptEngineering