Every token should generate returns. We find where yours don't — and fix it.
Engagements start with an audit. Most teams see where the money goes within the first two weeks.
We analyze your AI spend across every model, pipeline, and team — identify where budget leaks, which costs can't be attributed, and what to fix first.
Start here →We redesign your LLM pipelines — model routing, prompt compression, caching, and agent guardrails — so you get the same output for meaningfully less spend.
Talk to us →A long-term ROAI framework: cost attribution by product and team, governance policies, budget controls, and quarterly reviews as your AI usage scales.
Talk to us →The break-even between API pay-per-token and flat-rate subscriptions isn't obvious — here's the actual math across ChatGPT, Claude, Copilot, and more.
Read → Cost ArchitecturePer-token prices dropped 98% since 2022, yet enterprise AI budgets exploded 320% — the problem is how you're building, not what you're paying per token.
Read → Cost ArchitectureA support agent that looks like $0.001 per request is actually running 20–50 LLM calls per task — here's the math that exposes the real cost.
Read → Cost ArchitectureA concrete breakdown of LLM pricing across GPT-4o, Claude Sonnet 4.5, Gemini 2.5 Flash, Llama 70B, and Mistral — including why output tokens cost 3–5x more.
Read → Cost ArchitectureA routing layer that classifies queries by complexity and sends simple tasks to cheap models can cut your AI bill 60–80% with no quality degradation on the queries that matter.
Read →Apple Intelligence runs inference free on-device for app developers — here's what it can actually handle, how to call it, and when it changes the math on your AI costs.
Read → Local AIRun local models for 80% of tasks via Ollama and route hard problems to cloud APIs — this hybrid setup cuts cloud costs 70–90% without sacrificing quality where it matters.
Read → Local AILocal inference breaks even against cloud APIs at roughly 500K tokens/day for 7B models on consumer hardware — but the quality gap is real and the operational overhead is underestimated.
Read →LiteLLM, OpenRouter, and Portkey each solve a different slice of the LLM traffic management problem — here's how they compare and when to use each.
Read → OptimizationA practical comparison of Helicone, LangSmith, PromptLayer, Portkey, Phoenix, and OpenLLMetry for tracking and controlling LLM costs in production.
Read → OptimizationProvider-side KV caching can cut input token costs 50–90% on repeated context — but only if your prompts are structured correctly, with static content first.
Read → OptimizationSeven production-proven techniques for cutting LLM costs — from prompt compression and structured output to speculative decoding and response caching — with real numbers for each.
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