Promptr
Compress your LLM prompts via a simple API to cut token usage and costs by up to 40%.
| What is it | Compress your LLM prompts via a simple API to cut token usage and costs by up to 40%. |
|---|---|
| Pricing | Unknown |
| Platform | API |
| API | Yes |
| Best for | Reducing costs for repeated API calls to models like GPT-4, Optimizing token usage in production AI applications |
| Domain registered | 2024 |
Data updated Aug. 1, 2026
What does Promptr do?
Promptr is a developer-focused API service that compresses text prompts before you send them to large language models (LLMs). The core idea is simple: you send your original prompt to Promptr's API, and it returns a shorter, semantically equivalent version. This compressed prompt uses fewer tokens, which directly lowers your costs when you use it with paid LLM APIs from providers like OpenAI. The service promises reductions of up to 40% on token usage, translating to significant savings, especially for prompts that are reused many times.
It works through a single REST API endpoint. Developers can integrate it with a curl command or libraries in Python, JavaScript, or C#. You get an API token, send your prompt, and receive the compressed version back. What makes Promptr stand out is its granular control. You can wrap parts of your prompt in special tags to prevent compression on critical instructions or set custom compression ratios for different sections, giving you precision over what gets shortened.
This tool is built for developers and businesses that are scaling their use of LLMs and are looking for straightforward ways to manage costs. It's particularly useful for applications that generate content, handle customer support, or perform other tasks where the same base prompt is used repeatedly. By trimming unnecessary verbosity without changing the prompt's intent, Promptr helps teams get the same quality of AI output while spending less on compute.
Key features
What makes it stand outWho is Promptr for?
Who benefits most from this toolTrust & presence
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