AI development cost: how much does it really cost to build AI solutions?

Artificial intelligence has long ceased to be something from the distant future – today, it has become an integral part of business practice. 

SMBs, startups, and enterprises are trying not to miss their place in the race for AI. According to Gartner, overall global AI spending is projected to reach $2 trillion in 2026, led in large part by AI integration into products such as smartphones and PCs, as well as infrastructure. And this is not surprising: artificial intelligence helps solve a wide range of tasks – from simple automation to complex processes – and also improves the customer experience.

Interest in AI is very high, but there is a significant gap between desire and actual costs. Underestimation often leads to unexpected difficulties – in team selection, infrastructure scaling, data quality assurance, and more. Implementing AI is not like pressing a “magic button”. It is a complex and long-term process that requires careful budgeting. That is why it’s important to get all the nuances of AI development cost right. 

In this article, we will go over the main AI expenses in detail, analyze how much different types of AI projects cost, talk about proven ways to optimize AI cost, and explain why AI remains an excellent investment when approached correctly.

What components make up AI development cost?

When planning a budget for an AI solution, it is important to understand that its structure is much more complex than superficial estimates. Below you can see the components that make up the cost of AI development.

1. Project: complexity and scale 

The more features and integrations you want, the bigger the final bill. For instance, rolling out a straightforward AI chatbot typically starts at $15,000–20,000, but if your goal is a sophisticated generative model, expect months of work and a $60,000+ investment. Larger and more complex models demand substantial compute power, extensive data preparation, and specialized expertise, all driving the price up.

2. Data: collection, processing, and annotation

Data is the fuel for AI. Its preparation includes collection, cleaning, structuring, labeling, balancing, and augmentation. Industry reports indicate that data analysts working with artificial intelligence devote up to 60%-80% of their time to manual data cleansing processes. Open datasets can help, but when a project needs something unique, companies often decide to build and label their data from scratch. 

3. Infrastructure: cloud services and computing resources 

When deep neural networks or generative models are involved, modern AI development needs a lot of computing power. This means that you need to plan for more than just regular cloud hosting. You also need to plan for secure data storage, scaling, continuous monitoring, and tech support. Forgetting any of these can raise the total AI cost.

4. Technology stack: software, licenses, and tools

Licensing and tools can be the biggest surprise in a budget. There are a lot of free, open-source frameworks out there, such as TensorFlow and PyTorch, and they work well for new businesses. But for more complicated projects, teams often pay for libraries or platforms to get extra features or better support. When choosing between open-source and commercial software, you always have to weigh the pros and cons of each option, such as cost, speed, and support. 

5. Testing, launch, support, and maintenance

An AI solution is not a one-time product. Regular testing and retraining of models, troubleshooting, and adaptation to new data and market conditions are required to ensure sustainable results. In industries with high regulatory requirements (medicine, insurance, logistics, etc.), maintenance and compliance costs are even higher. 

6. Team of specialists and their level of expertise

Costs vary depending on qualifications, geographic location, and involvement – rates range from $30 per hour (e.g., Eastern Europe) to $150 and above (Western Europe and US, niche experts). Specialists with experience in generative AI, deep neural networks, and NLP require a higher salary and compensation. The duration of the team's engagement directly affects the overall AI cost estimation as well.

Additional factors affecting AI development cost

• Rules and regulations, as well as safety. For some businesses, following the rules or maintaining sensitive data in private isn't just a beneficial idea; it's required. If you work in a regulated field like healthcare or finance, you should be ready to pay for security audits, paperwork, and ongoing compliance.

• How you structure your team. Whether you build an in-house team, hire an agency, or find a balance in-between, your choice will shape everything – cost, speed, and control over the result. 

• Unexpected risks and budget reserves. AI innovation rarely goes completely by the “script”. Projects often hit surprises along the way – maybe a roadblock with data, maybe a new technical hurdle. Having a small financial buffer isn’t just smart – it keeps the project on track even when plans change.

"Understanding the detailed cost structure of AI development is key to transparent budget management and turning complex projects into predictable business investments rather than a “black box” of costly experiments."

– Ilia Kiselevich, CEO & Founder at SolveIt

Breakdown of AI development costs by project type

After exploring the key factors that influence AI development cost, let's group AI projects into five main types. Each group varies in technology used, team size, and budget needed. This breakdown gives you a clear picture of what to expect and how to pick the right setup for your AI project.

1. Basic AI solutions

This group includes simple tools that use ready-made models or have limited options for customization.

Examples:

• Chatbots and voice assistants for customer support.

• Simple ML models for spam filtering, text processing, and auto-fill.

• Basic recommendation and analytics systems.

Cost range: $20,000 to $50,000

Timeframe: 1-2 months

2. Pre-built AI solutions

Pre-built solutions enable rapid deployment, limited configuration, and standard functionality, making the cost of implementing AI relatively manageable and suitable for MVPs.

Examples:

• SaaS chatbots for quick customer engagement

• Plug-and-play analytics tools for SMEs

• Subscription-based AI APIs for marketing or HR tasks

• Standardized AI services with limited customization

Cost range: $10,000 to $40,000

Timeframe: a few weeks

3. Mid-level AI solutions

Projects with more complex logic and partial model customization, requiring serious data preparation and training on niche datasets.

Examples:

• Natural language processing (NLP) systems for tone analysis and automatic translation.

• Computer vision for image recognition and classification.

• Personalized recommendations on medium-complexity platforms

Cost range: $50,000 to $150,000

Timeframe: 2-4 months

In this segment, the range of technologies is broader, there is significant work with high-quality data, models are trained directly for business tasks, and the involvement of ML engineers and data scientists is required.

4. Advanced AI solutions

A class of complex solutions with deep learning, generative models, and integration of multiple AI modules.

Examples:

• Generative artificial intelligence (e.g., for image or text generation).

• Real-time systems for video and audio processing.

• Complex predictive models and recommendation systems with high accuracy.

Cost range: $150,000 and above

Timeframe: 4-6 months and above

Projects require a team of highly skilled specialists, powerful computing infrastructure, and comprehensive testing. They typically involve collaboration with researchers, developers, and security specialists.

5. Custom AI solutions 

Custom solutions are created for unique business tasks and customer infrastructure. They are more expensive to develop but provide maximum flexibility, scalability, and competitive advantages.

Examples: 

• Medical diagnosis systems tailored to specific healthcare workflows

• Financial risk analytics and forecasting engines

• AI integration in complex industrial or telecom systems

• Security and compliance solutions for regulated sectors

• AI developed for niche industries or proprietary data

Cost range: $100,000 and above

Timeframe: 6 months and above

The impact of industry specifics on the cost of AI

Understanding the nuances and specifics of each industry is also important. For example:

• E-commerce and retail often use AI for personalization and automation, which makes the cost more predictable and moderate.

• Manufacturing and logistics are focused on process optimization, which can require integration with IoT and complex analytical models.

• Healthcare and real estate require strict compliance with regulations, a high degree of security, and explainable models, which significantly increases costs.

"Selecting the right type of AI solution and having a clear understanding of the budget are the foundations. At SolveIt, our approach not only allows us to build AI that solves specific tasks but also provides control over costs and risks, making technological implementation transparent and manageable."

– Andrey Savich, CTO at SolveIt

Best practices for AI development cost optimization

Now we can move on to learn more about how to cut AI cost effectively. At SolveIt, cost optimization in AI projects isn't just about saving money; it's also about making a process that is easy to manage and is predictable, with the best return on investment. Below are the methods that help our clients cut AI software costs without sacrificing quality. 

1. Keep scope clear and focused

Start with a PoC or MVP to find risks and test your business ideas early on. This lets you invest in small amounts at a time, pivot if required.

Expert tip: Set specific success criteria for every stage, from concept to deployment, and review them regularly with key stakeholders – it’s vital for predictable progress and cost control. For example, use metrics like prototype usability, model accuracy, or feedback from pilot users as stage gates before scaling up.

2. Mind your infrastructure costs

Use advanced cloud features like auto-scaling, reserved and spot instances, and automatic shutdown of idle resources. Employ continuous cost monitoring tools and anomaly detectors to identify and mitigate overspending promptly.

Expert tip: Adopt cost-aware ML frameworks and optimize model inference pipelines (quantization, pruning) to reduce GPU hours and cloud bills.

3. Be strategic with outsourcing

Build and maintain a strong internal team with core AI skills, especially for high-value expertise. Hire trusted external specialists only for specific tasks. Strategic outsourcing helps lower your total AI app development cost without compromising on expertise.

Expert tip: To reduce the loss of quality and the cost of rework, ensure that your SLAs are clear and verify your contractors' compliance carefully.

4. Build reusable code and automate

Standardize code, create reusable modules and APIs, and automate CI/CD processes. This reduces development time, facilitates maintenance, and allows solutions to scale faster.

Expert tip: Invest in documentation and train your team to use common libraries – this significantly reduces the gap between developers and speeds up adaptation.

5. Follow incremental development and monitor constantly

Iterative releases and rapid feedback allow you to find and fix bugs early on, reducing rework costs. Automatic model quality monitoring ensures stability and increases ROI.

Expert tip: Use tools for automatic model drift detection and prevent quality degradation before it affects your business.

Maximizing your AI ROI: Why is AI development worth the investment?

Along with the potential for ROI, tech leaders inevitably wonder: how much does AI cost, and how soon do those costs translate into measurable business value? At this stage, we have already figured out that investing in artificial intelligence is a strategic move that transforms business and opens up new horizons for growth. Awareness of AI benefits and understanding the specifics of ROI assessment help not only to invest wisely but also to guarantee maximum return. 

Key benefits of AI for businesses 

Automation of routine tasks

AI frees up resources by taking over repetitive tasks: processing requests, chatbots, data classification. This allows employees to focus on high-value tasks and increases overall productivity.

Increased operational efficiency

Supply chain optimization, dynamic demand forecasting, and reduced losses and errors all lead to significant savings and faster business processes.

Deep analysis and forecasting

AI identifies hidden patterns in big data, helps make fact-based decisions, and minimizes risks in important business areas.

Personalization and customer loyalty

AI adapts products and services to each user, making the interaction experience unique and increasing conversion rates.

Employee engagement and development

AI tools for training, support, and performance analysis stimulate team development and make their work more conscious and productive.

Common challenges in measuring AI ROI

Long implementation and evaluation cycles

AI solutions require time to scale and configure, so quick ROI estimates are often unreliable.

Diversity and complexity of KPIs

AI covers a wide range of metrics, from technical characteristics to business indicators. Combining them into a single system requires deep expertise.

Non-obvious and delayed effects

Improvements in decision quality and customer satisfaction are not immediately apparent and sometimes affect strategic brand indicators.

ROI dependence on the maturity stage of the AI system

Initial investments can be significant, but as maturity and data volume grow, the return on AI increases significantly.

Impact of data quality on ROI

Poor data quality can undermine AI model performance, leading to inaccurate insights and decisions, which directly affects ROI.

Practical recommendations for maximizing AI ROI

1. Set specific, measurable goals with SMART KPIs

Define clear business goals with measurable KPIs linked to each stage of AI implementation – from PoC to scaling and start every project with detailed artificial intelligence cost estimation to keep outcomes and spending aligned.

Expert tip: Involve all business stakeholders to jointly agree on goals and conduct regular reviews.

2. Leverage predictive modeling and scenario analysis

Use modeling of expected benefits (cost savings, revenue growth, risk reduction) to justify investments and select optimal options.

Expert tip: Use multi-year scenario forecasts for presentations and decision-making.

3. Establish clear baselines and measure real impact

Before launching, record current metrics (processing time, errors, customer satisfaction, revenue) to accurately assess changes from AI. This helps determine the real contribution and cost of inaction.

Expert tip: Use a balanced set of metrics – financial, operational, customer, and human resources – for a comprehensive analysis.

4. Ensure cross-functional knowledge sharing

Bring together business, data, and technology teams to facilitate knowledge sharing between internal and external experts. This reduces risk and increases internal capacity.

Expert tip: Plan training programs and track the acquisition of new competencies with KPIs.

5. Create feedback loops for continuous value realization

Continuously collect data on AI performance, analyze effectiveness, refine models, and identify new opportunities. This transforms the project from a one-time initiative into a source of continuous growth.

Expert tip: Set up automated reports and monitoring tools for timely response and improvement.

Now you understand how to maximize the return on investment in AI development. But even if you know all the “theoretical” tips and tricks, without “real” implementation, you will not get results. That is why choosing the right partner is so important for maintaining optimal AI app development cost.

AI development cost: SolveIt experience

At SolveIt, we understand that every project is unique, which is why we tailor solutions considering the specific AI development cost factors for each client. Below are a few examples.

AI consulting and light integration

Cost: $15,000 to $40,000

We will help you assess the potential of AI in your business and organize the integration of ready-made algorithms. For example, we integrated an algorithm for analyzing consumption and recommending tariffs for an energy company, which we successfully implemented in a mobile application. Note that this cost is reflecting only the AI part. The full development cost could be much higher, depending on the project complexity and other specific factors.

AI product development – MVP and mid-range

Cost: $40,000 to $120,000

We create mobile and web applications with custom AI models and functionality beyond the basics. For example, an application with a ChatGPT-based virtual assistant for schedule management and life tracking. This project includes the creation of unique models, integration with platforms, and user experience development.

Complex AI systems and full integration

Cost: $120,000 and above

We implement comprehensive AI solutions with generative technologies, deep learning, and scalable architecture. One of our projects is a caregiving platform with a generative concierge based on OpenAI, providing a personalized approach and task coordination. Such solutions include deep customization, multi-level integration, and long-term support.

AI product design and brand integration

Cost: $20,000 to $60,000

We develop complete brand identities and styles for AI products, including UX/UI for web and mobile platforms. One of our case studies involved creating a design and brand kit for an AI companion focused on romantic interaction, with all the necessary assets for publication in the App Store.

Our AI prices are transparent, and our development methods are based on reusing components, open-source, and cloud technologies to keep costs under control without compromising quality.

How to choose a reliable tech partner and keep your AI development budget optimal

When it comes to your next AI project, selecting the right technology partner makes all the difference. Here’s what really matters when choosing a team for your AI project.

Transparent pricing, flexible models

The best partners keep cost breakdowns clear and engagement options open – so you know exactly where your budget goes and can choose a collaboration format that fits your goals, from MVP launches to scaling.

Broad experience and hands-on cases

Ask for specifics: have they delivered both mobile AI apps (iOS, Android, Flutter) and robust web solutions (NLP, voice control, recommendations)? Check their portfolio for projects like yours and proven cases across industries. A real expert walks you through strategy, design, data preparation, training, support – not just code delivery.

Modern cost control tactics

Look for open-source, reusable modules, and smart cloud resource management. You’re not paying for tech trends – you want lean, scalable solutions that save time and reduce overhead at every stage.

Business-first mindset and real adaptability

A partner should ask about your business goals, not just features. They adapt plans and teams to your challenges, helping you pivot fast and invest only what makes sense. Your ROI – and your users – are the priority.

Trust, reviews, and reliable results

Choose a team with genuine client feedback, a history of sustainable launches, and transparency in their communication. Consistency beats big promises.

SolveIt’s portfolio covers all these points: experience in AI, transparent pricing, agile workflows, and a reputation for delivering what matters most – measurable outcomes, on time and within budget. If you’re ready for AI development by experts who’ve already solved these challenges, let’s talk.

Conclusion 

Developing AI-based solutions is a complex, multi-stage process that requires not only time and resources but also careful budget planning. Many companies underestimate these aspects, which leads to overspending, delays, and disappointment.

At SolveIt, we understand how important transparency and clarity are in managing the cost of AI development. We analyze the cost structure in detail, identify risks early on, and apply proven optimization methods to ensure that your projects are both innovative and cost-effective. Partnering with SolveIt guarantees transparency, quality, and tangible results.

We can conclude that when executed effectively, investing in AI drives growth, enhances operational efficiency, and creates new business opportunities. Let's work together to create AI solutions that truly transform your business – on time, on budget, and exceeding expectations.

FAQ: AI development cost

1. What are the main components of AI development cost?

We identify several main components that make up AI development cost:

• Project: complexity and scale.

• Data: collection, processing, and annotation.

• Infrastructure: computing resources and cloud services.

• Technology stack: licenses, software, and tools.

• Testing, launch, support, and maintenance

• Team of specialists and their level of expertise

In addition, there are other factors that influence costs:

• Rules and regulations, as well as safety.

• How you structure your team.

• Unexpected risks and budget reserves.

2. Can you provide a breakdown of AI development costs by project type?

Certainly. Here are the main categories of AI projects that differ in terms of technology, resources, and budget. 

1. Basic AI solutions

Cost range: $20,000 - $50,000

Timeframe: 1-2 months

2. Pre-built solutions

Cost range: $10,000 – $40,000

Timeframe: a few weeks

3. Mid-level AI solutions

Cost range: $50,000 - $150,000

Timeframe: 2-4 months

4. Advanced AI solutions

Cost range: $150,000 and above

Timeframe: 4-6 months and above

5. Custom AI solutions 

Cost range: $100,000 and above

Timeframe: 6 months and above

3. What are the best practices for AI development cost optimization?

Here is a list of our best points for optimizing AI cost based on our experience.

1. Keep scope clear and focused

2. Mind your infrastructure costs

3. Be strategic with outsourcing

4. Build reusable code and automate

5. Follow incremental development and monitor constantly

6. Select the right engagement model

4. Can you name the most common challenges in measuring AI ROI?

Below, you can find the most challenging limitations in measuring AI ROI correctly.

• Long implementation and evaluation cycles

• The diversity and complexity of KPIs

• Non-obvious and delayed effects

• ROI dependence on the maturity stage of the AI system

• Impact of data quality on ROI

5. Are there any practical recommendations for maximizing AI ROI?

1. Set specific, measurable goals with SMART KPIs

2. Leverage predictive modeling and scenario analysis

3. Establish clear baselines and measure real impact

4. Ensure cross-functional knowledge sharing

5. Create feedback loops for continuous value realization

6. Could you list the most common AI services offered at SolveIt along with their associated costs?

Each AI project is unique at SolveIt, and you can count on a highly personalized approach to ensure that your project is completed and implemented on time and within budget. Considering our experience and expertise in AI development, we can identify several groups to classify our AI services and costs.

AI consulting and light integration

Cost: $15,000 to $40,000

AI product development – MVP and mid-range

Cost: $40,000 to $120,000

Full integration and complex AI systems

Cost: $120,000 and above

AI product design and brand integration

Cost: $20,000 to $60,000