Artificial intelligence appears simple to adopt when we watch viral videos of models like ChatGPT answering questions or generating realistic images on demand. Thanks to easy-to-use APIs and SaaS platforms, you can spin up a proof of concept in a day. But beneath the surface, AI development is expensive. The real costs lie not in the initial deployment but in assembling the right team, building infrastructure and continuously tuning and maintaining the models. In this article we break down the hidden costs, compare in-house and SaaS approaches and explain when a "do-it-yourself" strategy using open-source tools makes sense.

Why model deployment seems quick but costs add up

Large technology companies can deploy trained models in a matter of hours because they have mature pipelines and experienced teams. However, the hard work begins after deployment: data must be curated, pipelines monitored and retraining routines built to keep the model performing. A chatbot or generative model can be integrated into a website quickly, but turning it into a reliable, production-grade system requires continuous engineering. Fine-tuning on domain-specific data, monitoring hallucinations and adapting to user feedback all require expertise and ongoing investment.

Cost breakdown of AI projects

The cost of an AI project depends on its complexity, data requirements and delivery model. A recent estimate breaks down typical ranges for common use cases: a simple AI chat assistant costs US $5k–$50k; a generative AI MVP costs $60k–$150k; predictive analytics projects cost $80k–$200k; real-time computer vision solutions cost $120k–$300k; and voice or natural-language assistants cost $100k–$250k. Infrastructure adds recurring expenses: cloud compute for model serving can start around $2k per month and scales with usage. When using third-party models via API, token usage quickly multiplies; a chatbot processing 20 million input tokens and 10 million output tokens per month could cost $500–$5,000 monthly.

There are also significant ongoing costs after launch. Fine-tuning and retraining typically require 5–10% of the initial development cost each year; infrastructure scaling accounts for 5–15%; monitoring and quality assurance add 3–5%; compliance and regulation changes account for 2–15%. In total, keeping an AI system healthy may cost 17–30% of the original investment annually.

Talent and infrastructure

Hiring experienced AI engineers and data scientists is often the biggest cost driver. In North America the median compensation for an AI engineer can exceed US $300k a year, and employee salaries account for 29–49% of the total cost of frontier-model projects. Beyond salaries, operating sophisticated models can be expensive: a gaming start-up has reported spending $200k per month to maintain a generative AI model in production. This reflects costs for GPUs, storage, data pipelines and DevOps engineers.

In-house vs SaaS vs open-source

There are three main ways to obtain AI capabilities:

Build a bespoke system in-house. This offers maximum control and customization, but requires hiring an expert team, establishing data pipelines and investing in infrastructure. It is justified when AI is core to your product or when you need to protect proprietary data.

Use SaaS platforms or APIs. Providers such as OpenAI, Google and Anthropic manage the training and infrastructure. You pay based on usage, so there are no upfront hiring costs. This is ideal for experimenting or scaling quickly. However, monthly API bills can become unpredictable if usage spikes, and you may be locked into a vendor's roadmap.

Adopt open-source AI tools. Libraries such as Hugging Face Transformers or open LLMs let you run models locally. Open-source lowers the total cost of ownership by eliminating licensing fees and avoiding unpredictable API bills, and it offers deeper customization and vendor independence. But you are responsible for setup, tuning and security, which still requires skilled engineers and infrastructure.

DIY: when "rolling your own" makes sense

For start-ups and small teams with technical expertise, building on open-source can be cost-effective. You can fine-tune existing models on your own data and host them on commodity GPUs. This approach works well for internal tools, research or products that require specialized knowledge and privacy. It allows you to innovate without per-call charges and to keep control over data.

For most companies, though, the sweet spot is a hybrid approach: start with SaaS to validate the use case, then gradually transition critical parts in-house when usage grows or when proprietary data and control become important. Throughout, invest in data quality, governance and skilled people. A model can be deployed in a day, but the journey to a reliable AI-powered product is long and requires investment in talent and processes.