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App Corp
Full-service software engineering
Engineering your experience…
App Corp
Full-service software engineering
Engineering your experience…
Reduce AI operating costs without sacrificing quality
App Corp helps companies reduce AI inference costs, optimize infrastructure spending, and build cost-effective AI systems. Practical strategies that work.
AI costs can spiral quickly. A single GPT-4 API call costs pennies. Multiply by millions of requests, add RAG retrieval, agent orchestration, and document processing — and suddenly your AI feature costs more than the infrastructure running the rest of your application.
Most AI cost problems are architecture problems. The wrong model for the task. No caching. No batching. No evaluation of whether the AI is actually providing value. Over-engineered agent systems doing work that a rules engine could handle.
App Corp approaches cost optimization as an engineering discipline. We profile your AI spend, identify the biggest cost drivers, and implement targeted optimizations — model routing, caching, smaller models for simple tasks, local inference where appropriate, and infrastructure right-sizing. The goal is not to make AI cheap. The goal is to make AI cost-effective.
Estimate architecture, cost, and ROI before you commit.
Compare inference costs across models and providers.
Estimate total cost of ownership for AI agent systems.
Estimate the cost of building and operating a RAG system.
Calculate whether AI automation will save you money.
Deep-dives into architecture, cost, and implementation.
Practical strategies for cutting LLM and AI inference costs without sacrificing quality.
When local inference makes sense and when cloud is the right choice.
How to choose the right model size for your task — and save 90% on inference.
Infrastructure cost optimization strategies for AI and SaaS applications.
Real projects, real architecture, real outcomes.
We audit your AI costs, identify the biggest drivers, and implement targeted optimizations. Most clients save 40–70% on inference costs.