As you already might know prompt tokens are the backbone of communication with large language models (LLMs). However, as usage scales, token costs can quickly become a significant expense. If you’re using Toon—a tool designed for optimizing prompt workflows—you can dramatically cut down on these costs without sacrificing performance.
Why Token Costs Matter
Every interaction with an LLM consumes tokens. These tokens represent:
Input tokens: The text you send to the model.Output tokens: The text generated by the model.
The more tokens you use, the higher your bill. For businesses running thousands of prompts daily, even small inefficiencies can lead to big costs.
Example:
Original prompt: “Please summarize the following text in a clear and concise manner, highlighting the key points and provide a RACI Matrix for S/4 HANA”Compressed prompt: “Summarize key points.”
What is TOON?
TOON is a compact, human-readable serialization format designed for passing structured data to LLMs with significantly reduced token usage. It acts as a lossless, drop-in representation of JSON, optimized for token efficiency.
Why Use TOON?
Token-efficient: Saves 30–60% tokens compared to formatted JSON for large uniform arrays.LLM-friendly: Explicit lengths and fields improve parsing and validation.Minimal syntax: Removes redundant punctuation (braces, quotes).Tabular arrays: Declare keys once, stream data as rows.Optional key folding: Collapses nested chains into dotted paths for fewer tokens.
Benchmarks
TOON uses 39.6% fewer tokens than JSON while improving retrieval accuracy (73.9% vs 69.7%).For uniform tabular data, TOON is slightly larger than CSV (+6%) but far smaller than JSON (-58%).
When NOT to Use TOON
Deeply nested or non-uniform structures → JSON may be better.Pure tabular data → CSV is smaller.Latency-critical apps → Benchmark first; compact JSON might be faster.
How to Use TOON
NPM Lib
npm install -format/toon
Python
https://github.com/xaviviro/python-toon
https://github.com/toon-format/toon
https://github.com/toon-format/spec
Why TOON Matters while your working for different SAP Product LoBs
Token Cost Reduction
SAP S/4 HANA systems handle large datasets (e.g., thousands of line items in a purchase order).JSON representation of these datasets is expensive in token terms.TOON compresses this data by 30–60%, reducing LLM API costs significantly.
Performance Gains
Smaller prompts mean lower latency and faster response times.This is critical for real-time SAP applications like Joule for procurement or HR assistants.
Improved Accuracy
TOON’s explicit structure (e.g., tabular arrays with declared keys) helps LLMs parse data better.This reduces hallucinations in SAP workflows like financial reconciliation or compliance checks.
High Level Flow
Integration Approach
Middleware Layer: Convert SAP OData/JSON responses to TOON before sending to LLM.SAP BTP Extension: Implement TOON conversion in CAP, Cloud Foundry apps or Kyma runtime or SAP Databricks, AI Core Models you have selected.Prompt Wrapping: Always wrap TOON in fenced code blocks for LLM clarity
Best Practices
Use tab-delimited rows for extra token savings.Benchmark TOON vs JSON for your specific SAP dataset.Cache static context to avoid repeated token costs.
Conclusion
TOON offers a simple yet powerful way to reduce LLM costs in your SAP landscape environments. By compressing structured data without losing meaning, SAP teams can achieve:
Up to 60% cost savingsFaster response timesImproved AI accuracy
As SAP continues its AI journey, adopting TOON can make intelligent automation more scalable and cost-effective.
As you already might know prompt tokens are the backbone of communication with large language models (LLMs). However, as usage scales, token costs can quickly become a significant expense. If you’re using Toon—a tool designed for optimizing prompt workflows—you can dramatically cut down on these costs without sacrificing performance.Why Token Costs MatterEvery interaction with an LLM consumes tokens. These tokens represent:Input tokens: The text you send to the model.Output tokens: The text generated by the model.The more tokens you use, the higher your bill. For businesses running thousands of prompts daily, even small inefficiencies can lead to big costs.Example:Original prompt: “Please summarize the following text in a clear and concise manner, highlighting the key points and provide a RACI Matrix for S/4 HANA”Compressed prompt: “Summarize key points.”What is TOON?TOON is a compact, human-readable serialization format designed for passing structured data to LLMs with significantly reduced token usage. It acts as a lossless, drop-in representation of JSON, optimized for token efficiency.Why Use TOON?Token-efficient: Saves 30–60% tokens compared to formatted JSON for large uniform arrays.LLM-friendly: Explicit lengths and fields improve parsing and validation.Minimal syntax: Removes redundant punctuation (braces, quotes).Tabular arrays: Declare keys once, stream data as rows.Optional key folding: Collapses nested chains into dotted paths for fewer tokens.BenchmarksTOON uses 39.6% fewer tokens than JSON while improving retrieval accuracy (73.9% vs 69.7%).For uniform tabular data, TOON is slightly larger than CSV (+6%) but far smaller than JSON (-58%).When NOT to Use TOONDeeply nested or non-uniform structures → JSON may be better.Pure tabular data → CSV is smaller.Latency-critical apps → Benchmark first; compact JSON might be faster.How to Use TOONNPM Lib
npm install -format/toon
Python
https://github.com/xaviviro/python-toon https://github.com/toon-format/toon https://github.com/toon-format/spec Why TOON Matters while your working for different SAP Product LoBsToken Cost ReductionSAP S/4 HANA systems handle large datasets (e.g., thousands of line items in a purchase order).JSON representation of these datasets is expensive in token terms.TOON compresses this data by 30–60%, reducing LLM API costs significantly.Performance GainsSmaller prompts mean lower latency and faster response times.This is critical for real-time SAP applications like Joule for procurement or HR assistants.Improved AccuracyTOON’s explicit structure (e.g., tabular arrays with declared keys) helps LLMs parse data better.This reduces hallucinations in SAP workflows like financial reconciliation or compliance checks.High Level FlowIntegration ApproachMiddleware Layer: Convert SAP OData/JSON responses to TOON before sending to LLM.SAP BTP Extension: Implement TOON conversion in CAP, Cloud Foundry apps or Kyma runtime or SAP Databricks, AI Core Models you have selected.Prompt Wrapping: Always wrap TOON in fenced code blocks for LLM clarityBest PracticesUse tab-delimited rows for extra token savings.Benchmark TOON vs JSON for your specific SAP dataset.Cache static context to avoid repeated token costs.ConclusionTOON offers a simple yet powerful way to reduce LLM costs in your SAP landscape environments. By compressing structured data without losing meaning, SAP teams can achieve:Up to 60% cost savingsFaster response timesImproved AI accuracyAs SAP continues its AI journey, adopting TOON can make intelligent automation more scalable and cost-effective. Read More Technology Blog Posts by SAP articles
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