Real savings examples

Use cases

Learn how developers and teams save thousands of dollars with TokenCompress

Solo Developer

Freelancer using Claude / GPT

Michael is a fullstack developer using Cursor and Claude 3.5 to speed up his work. Used to spend $165/month on API. Now — $36.

~5,500 LLM requests per month
Average context 12K tokens → 2.6K
2-minute integration with Cursor
Monthly savings
$129
$165
Yearly savings $1,548
Context compression 87%
ROI 1,400%
Team of 10

AI-first startup

A dev team of 10 actively uses Aider and LangChain. LLM budget grew to $1,650/month. With TokenCompress — $363.

Team plan with shared analytics
Centralized key management
Priority support
Team monthly savings
$1,287
$1,650
Yearly savings $15,444
Per person / month $128
ROI 4,193%
AI Agents

Autonomous agents on LangGraph

Company launched 50 AI agents for automated code review and testing. Agents make thousands of requests daily. Without compression — $8,000/month.

100M+ tokens per month
SDK for Python and TypeScript
Batch API for maximum efficiency
Infrastructure savings
$6,240
/месяц
Was $8,000
Now $1,760
Экономия в год $74,880
RAG Systems

Enterprise document search

Law firm implemented RAG for searching 500K+ documents. Each request includes 50-100K tokens of context.

Support for up to 1M tokens per request
Preserves legal terminology meaning
On-Premise deployment for compliance
Implementation results
-72%
request cost
Response speed +40%
Answer accuracy 99.2%
Implementation time 2 дня

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