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.
Batch Mode Architecture
Batch Mode reduces tool overhead by 98%. Instead of exposing dozens of tools to the LLM, we use 8 direct tools with batch_execute() handling all multi-step operations in a single call. Combined with Auto mode's smart model routing, tokens stretch even further.
Results
98% token overhead reduction. Auto mode routes simple tasks to cheap models (0.10x multiplier) and complex tasks to premium models. 5 files loaded instead of 50. Predictable costs at scale with transparent per-model multipliers shown in real time.
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