Shift-left token governance for GenAI & Agentic AI.
Paste your JSON/YAML/TOON/TRON and instantly see how format choices change tokens and LLM cost—before your AI bill does.
TokiPrism is part of PragnaAI by RYSAAG Quanta (RQ). Convert JSON/YAML/TOON/TRON and compare token usage + model costs before you ship prompts, agents, and pipelines.
Early focus: high-volume uniform data where TOON & TRON shine (telemetry logs, catalogs, event streams). JSON still wins for deep nesting & irregular schemas.
Same data · Different formats · Different costs
Real benchmark: GitHub MCP tools schema · GPT-4o input pricing ($2.50/1M tokens) · Input tokens only; output not included
At 10K requests/day: JSON = $280 vs TOON = $190 vs TRON = $140 → Save $33K–$51K/year
💡 Discover TOON & TRON — Token-Optimized formats designed for LLM efficiency. TOON (Token-Optimized Object Notation) offers ~32% token reduction while preserving readability. TRON (Token-Reduced Object Notation) pushes further with ~50% fewer tokens — ideal for high-volume uniform data like logs, catalogs, and event streams. * Not related to the TRON blockchain.
When to Use What
Format choice depends on data shape and workflow — TOON/TRON are not universal replacements.
- JSON Irregular structures, deep nesting, APIs, broad compatibility
- YAML Human-authored configs, readability-first workflows
- TOON Large uniform LLM inputs — balance of savings + readability
- TRON High-volume data, agent memory — maximum token efficiency
💡 Rule of thumb: Choose the format based on who consumes the context — tools (JSON), humans (YAML), models (TOON), or agents (TRON).
Why TokiPrism?
Most AI cost overruns happen because teams never see token impact during development. TokiPrism fixes that.
- No visibility — JSON vs YAML vs TOON can materially change token count and cost, but teams can't see it.
- No shift-left guardrails — cost surprises hit after production, not while authoring prompts.
- No quick comparison — teams need a fast, deterministic tokens → cost view across models.
Built for Developers, Platform & Infra teams, Agentic AI builders, and FinOps practitioners.
Agentic-Ready Token Governance for AI Agents
Loops, memory, multi-step tool calls — agentic AI explodes token costs. TokiPrism is the control layer before execution.
- Pre-flight checks — Estimate token & cost impact before each agent step
- Loop guardrails — Detect and prevent runaway context growth
- Memory compaction — Use TOON/TRON for agent memory & tool outputs
* Agentic features are in concept/planning phase for future releases.
Convert & Normalize
Move between formats cleanly. Keep your artifacts readable and model-friendly.
Estimate Tokens
Measure prompt payloads and compare cost across models to avoid surprises.
Govern Before Prod
Shift-left checks to prevent oversized prompts, runaway agent loops, and budget drift.
Interested in TokiPrism?
Launching Q1 2026. Reach out to learn more or get notified at launch.
connect@rysaag.ioBuilt by practitioners in FinOps, Platform Engineering, and Agentic AI governance.
* Timeline subject to change