Executive Summary

Many engineering organizations running retrieval-augmented generation (RAG) pipelines today face opaque and escalating per-token costs: system prompts, chains-of-thought, and third‑party middleware often add thousands of hidden tokens per request so that 400,000 software teams spending an average $30K ACV are collectively driving a $12.0B market in need of better visibility. The problem is especially acute for enterprises that must both control spend and prevent prompt leakage — they require attribution down to the user/query and assurance that system prompts remain private. You could build a privacy-first token calculator and instrumentation layer that operates as a lightweight SDK or sidecar, performing local tokenization and real-time per-request accounting, attributing cost to embeddings, retrieval, and model calls while masking or keeping system prompts on-prem. Features would include deterministic tokenizers kept up to date with provider models, integrations with popular vector stores and LLM endpoints, cost forecasting and anomaly alerts, and deployment modes that guarantee zero-log or enterprise on‑prem operation. The timing is favorable: broad LLM and RAG adoption is increasing operational token spend and cost sensitivity, and your quantified market metrics (Market Score 95/100, Revenue Potential 86/100) reflect strong willingness to pay for tooling that reduces waste and preserves privacy. Enterprises are also more vocal about prompt leakage, creating an opening for a solution that can prove privacy controls and accurate chargebacks. To stand out you must make privacy the default — client-side tokenization, cryptographic attestations or secure enclave options, and strict no-prompt-collection guarantees — while delivering markedly better attribution accuracy than generic observability tools. The honest tradeoffs are nontrivial: instrumenting diverse stacks and keeping tokenizers synchronized with evolving models is engineering-heavy, and go-to-market will require partnerships with LLM providers and vector DBs to lower adoption friction despite a medium-competitive landscape.

Executive Summary

Market Opportunity

Stop hidden LLM costs — privacy-first token calculator for RAG apps targets a $12.0B = 400k software teams x $30K ACV total addressable market with medium saturation and a year-over-year growth rate of 35%+ enterprise spend growth on AI tooling / observability.

Key trends driving demand: LLM & RAG adoption -- more teams rely on retrieval-augmented workflows, increasing token spend and complexity.; Cost sensitivity -- per-token model pricing pushes engineering teams to seek tooling that attributes and optimizes spend.; Privacy & prompt-leakage awareness -- enterprises demand that system prompts and chain-of-thought remain private.; Edge/browser runtimes & wasm -- enable accurate client-side tokenization and metering without server round-trips..

Key competitors include OpenAI (API + Usage Dashboard), tiktoken (Open-source tokenizer by OpenAI), LangSmith (LangChain Labs observability), PromptLayer, Weights & Biases (W&B) — model/experiment tracking used as a workaround.

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Stop hidden LLM costs — privacy-first token calculator for RAG apps

Executive Summary

Stop hidden LLM costs — privacy-first token calculator for RAG apps

Executive Summary

Market Validation

Market Opportunity

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