The True Cost of AI Software Development vs. Manual Coding

Are you still spending months (and a fortune) on manual coding when AI software development can generate whatever you need in minutes? 

If yes, think again. The fact that AI is a cost-effective strategy (especially in the long run) is not a catchy trick or empty promise. The savings come from operational efficiencies, reduced labor in the long term, and the ability to scale faster. 

For example, a well-known Meta (formerly, Facebook) has seen a 201% increase in net income and a 178% surge in stock value. Coca-Cola, Amazon, and Netflix show the same tendency after implementing an AI. 

In this article, we’ll compare traditional software development VS AI software development, highlighting where the true cost benefits lie. We’ll also show how Flatlogic’s code generator can help you bridge the gap and save thousands of dollars from the very beginning. 

Dive in!

What is traditional software development? 

With traditional software development, programmers write code and clearly define how the software should operate. Its logic and workflows. Every function and feature of the software is manually coded, ensuring that it works in a predictable and controlled manner.

Key characteristics of traditional software development:

• Rule-based programming
• Manual updates and maintenance
• Development life cycle follows a fixed process
• Given the same input, the output will always be the same
• Best for structured problems

What is AI software development? 

Unlike traditional software development, AI makes decisions without being explicitly programmed for every scenario. AI models use machine learning, natural language processing, and deep learning to recognize specific patterns, adapt, and improve over time. 

Key characteristics of AI software development:

• Data-driven approach
• Predictions and recommendations based on probability rather than fixed rules

• Accuracy and performance improvement over time with more data
• Best for complex and unstructured problems

Examples of AI software development: 

• Chatbots and virtual assistants (like ChatGPT) 
• AI-powered search engines 
• Image and voice recognition (e.g., Siri)
• AI-driven content generation (e.g. Jasper AI or ChatGPT) 

Key differences

Let’s examine the main cost factors impacting the overall price you should pay for each option. 

Labor costs

Traditional software development: 

Labor costs are one of the largest expenses in development. For smaller custom software projects (limited features), businesses commonly give something about $10,000–$20,000​. More complex applications are priced at $50,000 or more, primarily due to months of developer effort.

Here is a cost breakdown to see the difference more clearly:

• Small projects: $10,000-20,000
• Medium projects: $50,000-100,000
• Large projects: $100,000+

AI software development: 

AI-driven development usually requires additional specialized talent, which raises labor costs. Beyond general software developers, companies may need data scientists and machine learning engineers to build and integrate AI models. These specialists command higher salaries on average. 

In the US., an AI engineer or machine learning specialist can earn anywhere from about $90,000 to $150,000 per year​ (often higher for experienced roles), which is generally more than a typical software developer. 

Data scientists and ML engineers commonly fall in the $120k–$200k salary range​. This means hiring a small AI development team (2-3 specialists) can cost upwards of $200,000 annually.

Below is a price breakdown for AI software development. 

• Small projects: $50,000-100,000
• Medium projects: $100,000-300,000
• Large projects: $300,000+

Compare: $10,000 – 50,000+ (traditional software development) VS $50,000-100,000 (AI software development) 

Infrastructure costs

Traditional software development:

Traditional software typically runs on standard servers or cloud instances.  Costs commonly scale with the user base but are relatively predictable.

A web application for a small business might run on a cloud server costing a few hundred dollars per month. There may also be one-time hardware or setup costs if on-premises deployment is needed, but generally traditional software doesn’t demand extreme computing power for most business applications.

Here is a cost breakdown regarding the costs of infrastructure:

• Small projects: $100-1,000
• Medium projects: $1,000-2,000
• Large projects: $2,000-10,000

AI software development: 

AI-driven development often requires significantly more powerful infrastructure. Training and deploying AI models (especially large machine learning or deep learning) can be compute-intensive. This can mean using GPUs (Graphics Processing Units) or other accelerated hardware, large memory and storage for big datasets, and robust cloud services. All of these can drive up costs. 

In fact, many small businesses introducing AI find they must invest in upgraded hardware and cloud capacity; 53% reported that the initial infrastructure investment for AI was higher than they anticipated​. One real-world example is a marketing agency that spent an upfront $3,000 on a cloud GPU server and continues to pay around $300 per month to use an AI content generation model​.

These are significant costs relevant purely to compute power needs, on top of normal hosting. In more extreme cases, a medium-scale AI deployment with multiple GPUs can run into five or six figures annually. 

Let’s see now the cost breakdown for AI software development:

• Small projects: $1,000-5,000
• Medium projects: $5,000-20,000
• Large projects: $20,000-100,000

Compare:  $100–1,000 per month (traditional software development) VS $1,000-5,000 (AI software development)

Development tools and licensing

Traditional software development:

Traditional software developers use IDEs (Integrated Development Environments), code libraries, databases, and frameworks. Many popular development tools and frameworks (like programming languages, web frameworks, etc.) are open-source or included in developers’ workflows at no extra cost. 

Although free tools provide a solid foundation, they often lack advanced features, dedicated support, and enterprise-grade security. They won’t always scale effectively or integrate seamlessly with complex business workflows. So, for businesses wishing enhanced performance, security, and support, it’s better to invest in premium development tools. 

Development tool expenses can vary:

• Free – Many essential programming languages, frameworks, and databases (e.g., PostgreSQL, MySQL)

• $100–500 per developer per year – For premium IDEs like JetBrains IntelliJ IDEA or WebStorm

• $1,000–10,000 per year – For proprietary enterprise software, such as Microsoft SQL Server or Oracle DB

• $50–300 per month – Subscription-based low-code/no-code platforms or cloud development tools like GitHub Copilot or AWS CodeBuild

AI software development: 

On the development side, many AI frameworks (TensorFlow, PyTorch, etc.) are open-source and free, which is good news. However, effectively using these frameworks may require skilled personnel. At the same time, proprietary tools and APIs can be expensive.

For example, if your business opts for proprietary AI platforms or APIs, expenses can quickly add up:

• $0–500 per month – Open-source AI frameworks (free) with optional cloud-based development tools like Google Colab Pro+ for enhanced performance

• $0.002–0.12 per API call – AI-powered APIs (e.g., OpenAI’s GPT, Google AI, IBM Watson). Costs vary based on usage volume

• $1,000–10,000 per month – Cloud-based AI model hosting (e.g., AWS Sagemaker, Azure AI, or Google Vertex AI)

• $20,000–150,000+ per year – Custom AI tool licenses or enterprise AI platforms like DataRobot or H2O.ai for advanced automation and model deployment

Compare:  $0-10,000 per year (traditional software development) VS $0-150,000 per year (AI software development) 

Time-to-market

Traditional software development:

Traditional software development relies on human effort for coding and problem-solving. The timeline depends on the complexity of requirements and the productivity of the development team. 

Although there are established methodologies (Agile, etc.) to streamline traditional development, certain complex features might require writing a lot of code and extensive testing. This means that calculating costs may be tricky here. 

AI software development:

AI development can sometimes allow a quicker implementation of complex features – for example, using a pre-trained AI model for speech recognition is much faster than writing a speech recognition algorithm from scratch (which is practically infeasible traditionally). 

On the other hand, integrating  existing AI models and ensuring they work correctly within the product still requires testing and possibly adjusting the rest of the system.

In terms of cost impact, faster development through AI assistance means lower labor costs (fewer developer hours) and earlier launch (so potential revenue or operational savings start sooner). One tech company reported that using AI coding tools gave their engineering team a 25% increase in development velocity (feature throughput)​.

That kind of efficiency can reduce the overall cost of a project or allow a small team to accomplish more in the same time.

Compare:  Increased costs due to extended time-to-market (traditional software development) VS reduced costs due to accelerated time-to-market (AI software development) 

Maintenance

Traditional software development:

Traditional software requires periodic updates. Bug fixes, security patches, minor enhancements, and adaptation to changes in operating systems or third-party services. 

The key point is that traditional software isn’t “one and done”— there is an ongoing cost to ensure it continues to meet business needs and runs securely as environments evolve. 

For small to medium-sized applications, maintenance typically costs around 15-20% of the initial development cost per year. For large-scale systems or enterprise applications, the figure could reach even 20-25%. 

AI software development:

AI models can degrade over time as real-world data changes – a phenomenon known as model drift. To maintain accuracy and relevance, models might need to be retrained or updated with new data periodically. 

This retraining process can be a significant effort: it means collecting fresh data, possibly labeling it, and running new training cycles (which consumes compute resources and developer/analyst time). 

For example, a predictive model might need a refresh every few months to stay accurate. 

On average, AI system maintenance costs 30-50 % of the initial development cost annually due to the need for regular retraining, data management, and infrastructure upgrades. For small to medium AI-based applications, it would be between $50,000 and $200,000 per year.

Compare: 20-25% annually (traditional software development) VS 30-50% (AI software development) 

Handling of data

Traditional software development:

Traditional software development implies the use of static, structured data. Its logic is hard-coded and built to handle predefined inputs and produce specific outputs. 

Traditional applications usually deal with moderate data volumes, often structured in relational databases. Storage needs are relatively modest and predictable – e.g., a CRM or e-commerce app might only store gigabytes to a few terabytes of data. This translates to lower storage expenses: a small on-premise server or a basic cloud database service can often suffice. 

Overall, adding new data (like user records) doesn’t drastically change infrastructure needs​. Backup and archival costs exist (e.g., periodic database backups), but they are manageable on a small scale. 

From all the above, we can conclude that traditional software development has predictable and manageable data handling costs, averaging $20,000–$85,000 per year.

AI software development: 

The more data you feed, the better an AI system becomes. As it learns from data, its accuracy and efficiency improve. 

AI projects tend to require massive datasets, which drives up storage demands and costs. 

Speaking more specifically, handling data with AI software development typically ranges from $100,000–500,000 per year. 

Compare: $20,000-85,000 (custom software development) VS $100,000-500,000 per year (AI software development) 

Scalability

Traditional software development:

Traditional software scalability usually means optimizing code or increasing computing resources (like upgrading a server or adding more servers). This has a cost, but it’s a relatively straightforward linear scaling in many cases – you pay for a bigger server or more licenses, etc., proportional to growth.

For example, upgrading from a basic server with 4 CPU cores and 16GB of RAM to a more powerful server with 8 CPU cores and 32GB of RAM could cost around $100–$500/month. If you need additional software licenses (e.g., for database management or enterprise-level applications), this could add another $200–$1,000/month depending on the software and user count. Generally, businesses need $5,000-30,000 per month for scaling. 

AI software development:

AI-based systems may face a different scaling cost curve. 

Suppose the usage of an AI feature grows. In that case, the computational cost per use may remain high. For instance, if an AI model must run on each user request, doubling users doubles the cost of AI.

Many AI algorithms, especially deep learning, are computationally intensive to run (not just to train). This means scaling up an AI-driven service might require significantly more computing power (e.g., additional GPU instances or higher cloud service fees) compared to scaling a non-AI service. 

Speaking more specifically, you would need to pay $2,000–10,000 per month for cloud GPU instances (e.g., NVIDIA A100). 

Compare: $5,000-30,000 per month (traditional software development) VS $10,000-30,000 per month  (AI software development) 

Long-term benefits and savings

Traditional software development:

Traditional software projects often have fairly modest long-term ROI. One analysis noted that the typical ROI for a custom software project is around 20% or less. 

This means the financial returns barely exceed the costs – essentially, you might only get about $1.20 back for every $1 spent, even after several years. 

Over 5 years, a traditional software might pay back its investment and yield some profit, but nothing drastic (for example, a $200k project might return a total benefit of ~$240k, which is 20% ROI). 

AI software development:

Studies find that AI investments yield an average return of about 3.5× (350%) – meaning, for every $1 invested, companies get $3.5 in value – and some organizations see up to 8× returns on AI projects​. 

Even taking a conservative view, an AI-driven software project that costs $200k could produce $400k–$700k in net benefits within a few years (through combined savings and new revenue). Over a 5-year period, it’s not unusual to see ROI well above 800% for AI projects – essentially doubling the investment or better​. 

Compare: 20-50% ROI ((traditional software development) VS 100-800%+ (AI software development)

Pre-built AI models vs Custom-built

We’ve already talked about the difference between traditional software development and the use of AI. Although the last option has tremendous benefits, it’s not the cheapest. 

Developing an AI model makes sense if you want to see results in the long run and are ready for a big upfront investment from the very beginning. However, there is one trick that could save you thousands of dollars and helps to see the actual results much faster. We are talking about pre-built AI models. 

Pre-built AI models are machine learning models that have been pre-trained on large datasets. These models are designed to solve specific tasks such as image recognition, natural language processing, speech-to-text conversion, and more. The biggest advantage of the pre-trained AI models is that you do not need to build them from scratch. 

Choose Flatlogic: Combine pre-built AI models with customization flexibility

Flatlogic offers a unique hybrid approach that combines the cost-effectiveness of pre-built AI models with the flexibility of custom solutions. Our platform allows users to create custom SaaS, ERP, CRM, CMS, and other solutions quickly and efficiently. We help businesses save time and resources while delivering robust and customizable software, offering full code ownership and scalability.  

Choose Flatlogic for a seamless, cost-effective, and scalable approach to software creation!

Features of our AI software development:

• Modular architecture 
• Rapid deployment
• Highly customization
• Integration capabilities
• Robust security features
• User-friendly interface

Conclusion

If you’re considering AI development, weigh the initial investment against the potential long-term benefits. 

Traditional coding offers predictable costs and is well-suited for structured problems. However, it requires substantial time, money, and effort, which leads to the fact that it’s not the best strategy if you consider a 5-years perspective or even a more distant future. 

AI is here to stay as one of the TOP trends in software development. The sooner you start to implement it in your business processes, the better positioned you’ll be to gain a competitive edge. 

And if you want to start with AI now and not at the cost of an arm and a leg, there is no better way than to be with Flatlogic. Our code generator will automate whole coding and focus on what truly matters: your business goals and customer needs.