How to Reduce AI Token Costs by 90% in Production
Learn how smart context management can reduce your AI development costs by 90% without sacrificing code quality or accuracy.
AI-powered development platforms face a critical challenge: token costs can spiral out of control. Loading entire codebases into context for every request is expensive and inefficient. Here's how we solved it.
The Token Cost Problem
Traditional AI coding assistants load 50+ files (25K tokens) per request. At scale, this becomes unsustainable. A single complex deployment can consume your entire monthly token budget.
Smart Context Management
Instead of loading everything, use heuristic filtering to load only relevant files. Our approach: exact filename matching (100 points), directory matching (50 points), entry points (30 points), and cross-file dependencies.
Session Memory
Remember files the agent recently worked with. This creates continuity across conversation turns without re-loading context. Uses DynamoDB with 30-minute TTL for automatic decay.
Results
90% token reduction (25K → 2.5K tokens per request). 5 files loaded instead of 50. ~80% accuracy maintained. Predictable costs at scale.
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