*Model providers and AI platforms. On pay-as-you-go, every token is billed.
We map your AI spend across models, prompts, and pipelines — then build a cost strategy aligned with your product goals. Clear priorities, measurable outcomes.
A deep analysis of how your team uses LLMs — where tokens are wasted, which models are over-specified, and where caching or routing can cut costs 60–80%.
Ongoing implementation and governance — model routing, prompt compression, budget alerts, and monthly reviews so your AI costs stay predictable as you scale.
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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