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Reduce Token Usage & Optimize Costs

Agent-CoreX cuts costs by 30–70% by eliminating unnecessary tool schemas from your LLM context. Learn how to maximize savings.

The Cost Problem

Traditional AI agents include ALL tools in every request:

How Agent-CoreX Saves Tokens

  1. Dynamic Selection - Only relevant tools in context
  2. Semantic Matching - Find right tools without tool names
  3. Caching - Reuse tool lists when possible
  4. Batching - Combine similar queries

Cost Optimization Strategies

1. Use Specific Queries

Savings: 15-20% per query

2. Set Lower top_k

Savings: 30-40% per query

3. Use Server Filters

Savings: 20-25% per query

4. Cache Tool Lists

Savings: 60-80% for repeated queries

5. Batch Similar Requests

Savings: 50-70% on this operation

Cost Calculation

Before Agent-CoreX

With Agent-CoreX

Monitoring Your Costs

  1. Go to Dashboard → Usage
  2. View costs by server
  3. Track trends over time
  4. Set budget alerts

Cost Optimization Checklist

Quick Wins

✅ Be specific in queries ✅ Reduce top_k to 3-5 ✅ Cache tool lists ✅ Filter by server

Advanced

✅ Batch requests ✅ Implement rate limiting ✅ Use scheduled jobs ✅ Monitor trends

Real-World Example

A customer reduced costs from 2,400/monthto2,400/month to 280/month:
  1. Implemented caching (40% savings)
  2. Reduced top_k from 20 to 5 (30% savings)
  3. Used server filters (15% savings)
  4. Combined similar queries (20% savings)
Total: 89% cost reduction
Next Step: Authentication Guide →