What is Reducing LLM input tokens by 70%?
Adola uses Rose 1, a compression model designed to reduce the number of input tokens sent to large language models. This matters because fewer input tokens mean lower API costs, faster processing times, and less strain on context windows. The tool analyses your prompts and documents, then condenses them intelligently while preserving the information that actually affects the LLM's output. It's built for teams running production systems where token usage directly impacts operating costs; even a 50% reduction in input tokens can meaningfully improve margins on high-volume applications. Adola offers a freemium model, so you can test the compression rates on your own data before committing.
Key Features
Token compression
reduces input tokens by up to 70% whilst maintaining output quality
Rose 1 model
proprietary compression engine optimised for production LLM systems
Simple integration
works with existing LLM workflows via API or web interface
Freemium access
test on your data without payment to assess compression benefits
Batch processing
handle multiple prompts or documents in a single run
Pros & Cons
Advantages
- Significant cost reduction on API bills, especially for high-volume inference
- Faster response times due to fewer tokens being processed
- Preserves semantic meaning; the LLM still receives the information it needs
- Easy to evaluate with the free tier before committing budget
Limitations
- Results depend on input type; highly technical or structured data may compress less effectively
- Adds a preprocessing step to your pipeline, introducing slight latency overhead
- Limited public information on how Rose 1 handles domain-specific or proprietary terminology
Use Cases
Reducing costs for customer-facing chatbots handling thousands of daily queries
Compressing long documents or knowledge bases before sending to RAG systems
Optimising context windows for tasks involving large amounts of reference material
Lowering operational expenses for production AI systems running on metered APIs
Improving latency-sensitive applications by reducing tokens to process