LLMiserOptimize LLM costs. Start with your documents.
Optimize LLM costs. Start with your documents.
LLMiser turns PDFs, Office files, images and ZIPs into clean Markdown. Trim unnecessary context before sending it to AI. Free, with no sign-up.
Optimize LLM costs.
Convert PDFs, Office files, images and ZIPs to clean Markdown. Review the output and send only the context your AI task needs.
MDify is now LLMiser · by DevBehindYou
An LLM cost optimizer for your document workflow
Prepare useful context before you spend tokens. LLMiser converts PDFs, Office files, images and ZIP projects to editable Markdown. Choose a profile, review the output, and send only what your AI task needs.
1. Convert
Upload documents and choose Standard, Clean, Compact or RAG-ready output.
2. Refine
Check tables and OCR, remove irrelevant sections and keep essential context.
3. Measure
Compare model-specific usage and answer quality before changing your workflow.
Token counts in the app are rough text estimates, not billing measurements. Actual savings depend on your model and input method. See how document cost optimization works.
Files are uploaded for conversion with a default 48-hour retention period and documented exceptions. Read the Privacy Policy.
LLMiser questions, answered
What is LLMiser?
LLMiser is a free LLM cost optimizer for document inputs, made by DevBehindYou. It converts PDFs, Office files, images and ZIP projects into editable Markdown so you can remove unnecessary context before sending it to an AI assistant or RAG pipeline.
How does LLMiser help optimize LLM costs?
Convert a document, choose Clean or Compact output, and keep only the sections your task needs. Sending less unnecessary context can reduce billable input tokens. Savings depend on the model, tokenizer, input method and content. Always compare the result with the original and check answer quality.
Does converting PDF to Markdown always save tokens?
No. A provider that processes both PDF text and page images may use more tokens than a text-only Markdown input. A text extraction workflow may show little difference. Markdown can also lose charts or visual layout, so measure actual usage for your model and task.