Token Usage Analysis & Optimization
AI inference costs can spiral quickly as usage scales. Our Token Intelligence framework helps you understand exactly where every dollar is going — and reduce it by 40–60% without sacrificing quality.
AI Costs Are Growing Faster Than the Value
Inefficient Prompts
System prompts bloated with redundant instructions. Repetitive context sent on every call. Output tokens wasted on verbose formatting when JSON would suffice. These add up to thousands of dollars monthly.
Context Window Waste
Stuffing maximum context into every call "just in case." Not filtering retrieval results before injection. Conversational history growing unchecked. Unnecessary few-shot examples on every request.
No Caching Strategy
Identical or near-identical prompts sent repeatedly without caching. No prefix caching. No semantic similarity matching to reuse prior responses for equivalent queries.
Token Intelligence: 4 Pillars
Audit & Baseline
Instrument all LLM calls with token-level telemetry. Build a cost heatmap showing which prompts, endpoints, and user cohorts are driving spend. Establish the baseline for measuring optimization impact.
Analyze & Classify
Classify token spend by type: system prompt, retrieved context, conversational history, user input, and output tokens. Identify the top 20% of prompts driving 80% of costs.
Optimize & Validate
Apply a prioritized set of optimizations: prompt compression, dynamic context assembly, output format constraints, semantic caching, and model right-sizing. Each change is A/B tested to verify quality preservation.
Monitor & Govern
Deploy continuous token cost monitoring with anomaly alerts. Define per-endpoint cost budgets. Implement automated rollback if quality metrics degrade. Integrate with cloud billing APIs for unified visibility.
A Toolkit for Every Type of Waste
Prompt Compression
Remove redundancy from system prompts and instructions without reducing task performance. Typical reduction: 25–45% of system prompt tokens.
Semantic Caching
Cache responses to semantically equivalent queries using vector similarity. Reduces API calls for common query patterns by 20–60% in production applications.
Context Filtering & RAG Optimization
Pre-filter retrieval results with relevance scoring before context injection. Remove low-relevance chunks. Use reranking to keep only the most pertinent context in the prompt.
Model Right-Sizing
Route simple tasks to smaller, faster, cheaper models (Claude Haiku, GPT-4o-mini) and reserve expensive models for tasks that genuinely require deeper reasoning. Tiered model routing reduces cost per request by 60–80% for routable queries.
Request Batching
Consolidate independent LLM calls into batch requests where latency allows. Batch APIs often offer 50% cost reductions for workloads that can tolerate minutes-scale latency.