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Why is AI so expensive to run?

AI Business Process Automation > AI Workflow & Task Automation6 min read

Why is AI so expensive to run?

Frequently Asked Questions

Why does running AI cost so much more in production than during testing?
AI costs surge in production primarily due to escalating compute demands, especially when scaling generative AI workloads in cloud environments. IBM research shows computing costs are projected to rise 89% between 2023 and 2025, with 70% of executives citing generative AI as the main driver.
Are big AI models really that expensive to train and run?
Yes—training costs have skyrocketed with model complexity. For example, GPT-4 cost an estimated $78 million to train, while Google’s Gemini Ultra cost $191 million, according to 2024 industry analyses.
Can small businesses afford custom AI solutions, or is it only for big companies?
While large firms dominate AI investment, SMBs can reduce costs by adopting open-source models—65.8% of new foundation models in 2023 were open-source—and building tailored systems that eliminate inefficiencies like manual data entry and disconnected tools.
Isn’t using off-the-shelf AI tools cheaper than building a custom system?
Off-the-shelf tools often lead to 'subscription chaos' and brittle integrations that increase long-term costs. Custom AI systems avoid these inefficiencies by unifying workflows into a single, owned platform that scales with the business.
How does data scarcity affect AI operating costs?
High-quality language data for training may be exhausted by 2024, forcing reliance on synthetic data—which can reduce output diversity and increase development complexity, driving up costs for maintaining model performance.
What’s the real reason companies delay AI projects?
Every executive surveyed by IBM reported postponing or canceling at least one generative AI initiative due to cost concerns, particularly around compute expenses required to move from experimentation to full-scale production.

Stop Paying More for AI That Doesn’t Deliver

AI doesn’t have to be expensive—you just need the right approach. As we’ve seen, the true cost of AI isn’t in the technology itself, but in inefficient workflows, fragile no-code tools, and the hidden labor of managing disconnected systems. Generic platforms may promise quick wins, but they fail when business complexity grows, leaving teams stuck with brittle automations and mounting technical debt. At AIQ Labs, we help businesses move beyond rented solutions and build custom, production-ready AI workflows that fully integrate into real-world operations. Whether it’s automating invoice processing, refining lead qualification with bespoke scoring models, or powering hyper-personalized marketing campaigns, our solutions are designed to deliver measurable efficiency—saving teams 20–40 hours per week and achieving ROI in as little as 30–60 days. Backed by proven in-house platforms like Agentive AIQ, Briefsy, and RecoverlyAI, we turn AI from a cost center into a scalable digital asset. Ready to stop overspending on underperforming AI? Take the first step: claim your free AI audit today and discover how much time and money your business could save with intelligent automation built to last.

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