Token Economics

Token economics for GEN AI adoption

Reduce Cost • Improve Performance • Scale Responsibly

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Token Economics
Scale ResponsiblyReduce Cost & Improve Performance
OUR APPROACH

Core Strategies

Understanding token economics is crucial to building cost-effective AI applications. A well-designed Token Economics strategy ensures that your deployments succeed. We help you incorporate strategies including Model Selection, Intelligent Routing, Token Efficient Interfaces, Provider Level Caching into your applications so that your applications are built to last.

We also build usage and cost tracking dashboards and incorporate controls into your model access gateways.

Dashboard Strategy

Select the Right Model

Not every workload requires a frontier-scale model; match model capability to task complexity.

  • Use small locally hosted models for extraction, tagging, and classification
  • Reserve large reasoning models for complex decision workflows
  • Optimize across accuracy, latency, and cost
  • Our ongoing experiments with different models help you to select suitable models and manage cost-effectiveness
Outcome: Reduction in inference cost with maintained quality.

Intelligent Model Routing

Dynamically route each request to the most cost-efficient model.

  • Policy-based or AI-driven orchestration
  • Confidence scoring with fallback to stronger models
  • Hybrid edge + cloud execution paths
  • Task-aware routing across multi-model ecosystems
Outcome: Best performance at the lowest possible token spend.

Token-Efficient Design

Move Beyond Verbose JSON to Compact Formats like TOON

A major driver of cost is unnecessary tokens. Move beyond verbose JSON to compact formats.

  • Replace verbose JSON structures with token-efficient encodings (e.g., TOON)
  • Design compact schemas and semantic compression for machine consumption
  • Minimize repeated keys, whitespace, and structural overhead
  • Streamline both input context and model output formats
Outcome: Significant end-to-end token reduction with faster response times and lower cost.
Conversion to Toon – 40% reduction in tokens →

Provider-Level Caching

Leverage Built-in Caching from LLM Platforms (e.g., Bedrock)

Modern LLM platforms offer native prompt and response caching that can dramatically cut spend.

  • Enable prompt caching for repeated system/context tokens
  • Reuse shared conversation prefixes across sessions
  • Combine with application-level reuse strategies for maximum savings
Outcome: Major cost reduction and latency improvement without architectural complexity.

Usage & Cost Tracking

Sustained optimization requires continuous visibility.

  • Real-time token usage and spend dashboards
  • Cost attribution by feature, workflow, or user
  • Budget controls and governance guardrails
  • Data-driven ongoing token optimization
Outcome: Predictable, governable, enterprise-scale AI economics.
Usage-tracking Dashboard – Increase adoption with Leaderboards! →
IMPACT

SUCCESS STORIES

See how we've reduced cost and increased adoption with token economics.

Code Optimization
01
Optimization

CONVERSION TO TOON

Replaced verbose JSON with token-efficient TOON encoding—delivering 40% token reduction with lower cost and faster response times.

READ CASE STUDY
Dashboard
02
Governance

USAGE-TRACKING DASHBOARD

Increase adoption with leaderboards! Real-time usage and cost visibility with gamification that drives engagement.

READ CASE STUDY

To contact us about Ken-AI & GenAI, email marketing@sasken.com