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Last Updated: December, 19 2024
Ever since the introduction of consumer-grade AI models like ChatGPT, the demand, and potential of AI integration has been rising. A report from Statista confirms this, by projecting an increase of 23% in widescale AI adoption between 2022 and 2025.
Seeing such statistics, you might wonder what the AI development cost would be if you wanted to build a custom AI solution from scratch.
Since custom AI solutions are still in their infancy in terms of implementation, there is not a lot of history when it comes to the cost and complexities of developing artificial intelligence solutions. And with so many different AI model types and strategies around, it becomes especially confusing to figure out how much it will cost you for your specific use case.
To address all of your doubts regarding the cost of AI development solutions, we will explore all of the details and nuances of the industry to give you an accurate estimate of AI costs. So let us dive into everything there is to know about all of the costs and considerations involved with AI development.
Right off the bat, an AI app development company will charge you anywhere between $20,000 to $300,000 to develop a custom solution powered by artificial intelligence.
This range is agnostic of infrastructure costs as they will depend on your chosen service provider, application scale, and geography. Based on your industry, project, and solution type, let us dive further into how AI development costs vary.
The project type for your AI solution is what largely dictates its overall cost as it determines exactly how much effort the software developers will have to go through for delivery. In our cost breakdown based on project type, we ran with the following team structure to determine the AI project timelines:
Based on the mentioned team structure, the division of AI costs and development times based on project types are as follows:
| Project Type | Development Time | Average Cost |
|---|---|---|
| Minimum Viable Product (MVP) | 2 Weeks – 6 Months | $10,000 – $150,000 |
| Proof of Concept (PoC) | 2-10 Weeks | $10,000 – $100,000 |
| Full-Scale Implementation | 2-12 Months | $25,000 – $300,000 |
| Fine-Tuning | 1-4 Weeks | $5,000 – $40,000 |
| Consulting | 2-10 Weeks | $20,000 – $80,000 |
While AI projects are usually indifferent to the industry that they are developed for, certain industries require additional QA or engineering stages to be compliant with regulations. Industries where the reliability of AI solutions is critical, are especially prone to incurring more development costs due to additional features added for redundancy. A breakdown of average AI solution development costs for different industries has been laid below:
| Industry | Average Solution Cost |
|---|---|
| Healthcare | $50,000 – $500,000 |
| Finance | $35,000 – $400,000 |
| Manufacturing | $30,000 – $500,000 |
| Automotive | $500,000 – $5,000,000 |
| Telecommunications | $100,000 – $500,000 |
| Retail and eCommerce | $40,000 – $200,000 |
| Education | $50,000 – $300,000 |
| Logistics and Supply Chain | $50,000 – $300,000 |
| Marketing and Advertising | $30,000 – $150,000 |
| Real Estate | $30,000 – $150,000 |
The cost of AI can also be influenced by the type of AI solution you wish to develop; this is because different solutions require different data types, model architectures, and AI algorithms. For example, a natural language processing model is comparatively easier to engineer compared to an image generation model due to the simplicity of the solution and the availability of data sources.
Below is a table highlighting the difference in development costs for different types of AI technology.
| AI Solution Type | Use Cases | Average Development Cost |
|---|---|---|
| Chatbots | – Customer support – Knowledge base automation – Personal assistants | $15,000 – $130,000 |
| Predictive Analytics | – Sales forecasts – Inventory level maintenance – Risk management | $50,000 – $250,000 |
| Recommendation Systems | – eCommerce suggestions – Content recommendations | $40,000 – $200,000 |
| Image Recognition | – Community surveillance – Custom image classification – Medical anomaly detection | $30,000 – $500,000 |
| Natural Language Processing (NLP) | – Language translation – Sentiment analysis – Text classification | $50,000 – $300,000 |
| Process Automation | – Supply chain optimization – Automatic quality checking – Fulfillment initiation | $20,000 – $200,000 |
| Generative AI | – Text generation – Media content generation | $20,000 to $200,000 |
Estimate The Cost of AI Development in 24 Hours with Space-O
Are you still unsure about how much your AI solution would cost? Schedule a free call in your preferred time slot and Space-O’s experienced AI executives will estimate the final AI cost by understanding your industry and requirements.
While the artificial intelligence cost is mostly agnostic to changes in market trends, there are still some other factors at play that affect the overall cost of developing high-quality AI solutions. Let us start understanding what these factors are in depth, so you can guide your AI project plans better and avoid the common pitfall of overspending on unnecessary areas.
The cost of development is subject to change based on your given location. This is due to the fact that developers situated in different geographies bear different living costs which makes their services either more or less expensive. For example, North American businesses have had a history of outsourcing development projects to Southern European and Asian countries to leverage the lower cost of innovation.
However, the hardware required to develop AI solutions is a lot more intensive, requiring specific GPUs which might not be available in your selected development geography. This is why choosing the right development region for your AI project becomes critical, as you have to balance between cost and development potential.
Whether you’re developing a basic machine learning solution or a fully-fledged AI application, the importance of training data quality cannot be understated. This is because AI and ML models use data to draw conclusions to any given query despite their model weights. Basically, the only way for any AI model to be accurate is if it has been trained on high-quality data.
However, coming across readily available high-quality datasets is nearly impossible for niche applications. To remedy this shortage in data, your data scientists will either scrape data from public platforms or create custom data repositories for training your new model. Choosing either of these options is bound to raise the overall cost of your solution due to how it lengthens the AI development process.
Whether you choose to deploy your new AI system with on-premises infrastructure or cloud networks can have a significant bearing on your AI software development and maintenance cost. This is especially true for hardware costs if you do not already own capable computing resources with the required performance. For instance, a GPU like the Nvidia H100 is considered the standard for developing deep-learning solutions but can cost you nearly $30,000.
Going with a serverless cloud computing service allows you to not only circumvent the initial investment but also readily deploy your AI solution to multiple availability zones to reduce latency for the end-user. Not to mention that post-deployment, the computing resources can be auto-scaled to further optimize artificial intelligence cost.
It goes without saying that your AI software development cost will largely depend on the scale of your project. If you need complete AI-powered modernization of an existing app, then chances are that it will be a lot more expensive. In contrast to developing a brand new app, modernization requires highly intensive testing and user validation before it can be deployed.
Additionally, the modernized application has to be deployed at a much larger scale without any discrepancies to ensure uniform functionality. Due to such barriers in execution, artificial intelligence systems often end up being cheaper for startups than enterprises.
If your project is built for a sensitive industry such as healthcare or education, there are several regulatory compliance standards that your AI systems will have to adhere to. The most common compliance standards include HIPAA, GDPR, and ISO 27001. All of these compliance standards have their own tests and requirements that applications must clear to be certified.
Requirements include setting up access control, data encryption, user authentication, and data breach detection systems. Since such measures have to be developed in tandem with the solution, it causes artificial intelligence costs to spike proportionally.
If you are developing a stand-alone AI system, then integration will not be a concern. However, in the scenario where you have to integrate an AI software component within your existing applications, you will face complexities in making them work together. This is mainly due to the complex API or microservices-based architectures involved in successful integration.
The challenge of integrating AI software into existing applications is kicked up a notch if your platform is outdated and is classified as a legacy application. This is because legacy applications would require a complete overhaul with app modernization techniques for data interoperability which will drastically increase the artificial intelligence cost for you.
Given the concrete nature of these factors of artificial intelligence cost, the journey of developing AI solutions might seem intimidating. Luckily, there are some ways in which you can reduce the cost of your upcoming project which do not affect the final output greatly.
If you feel that your AI software solution might be going over budget, there are several ways to make it less expensive. The cost reduction methods covered in this section prioritize functionality above all and only feature holistic methods of controlling expenditure without sacrificing much on usability. Now we will move on to explore what exactly the top three ways to reduce the cost of your AI project are.
Outsourcing your AI project to an offshore or nearshore partner holds multiple benefits over assembling an in-house team. One is the fact that you get a well-experienced development team working on your project, which eliminates a lot of trial and error associated with custom AI development.
An offshore AI development team can especially help in making the project affordable due to the discrepancies in living costs for offshore developers. Communication gaps are usually not a concern if your selected AI development partner uses industry-standard channels such as Jira, Confluence, and Slack.
Instead of building a fully custom AI solution, it might be more economical to use a pre-trained AI model and dedicate resources to fine-tune the model for your performance standards. Fine-tuning can be done to the majority of generative and classification AI models, and it works by re-training the AI model using custom data and adjusting the core architecture.
For example, let’s say you wish to have an AI-powered chatbot to help you with providing information from your company’s knowledge base. In this case, you would use a pre-trained generative AI model such as Llama 2 and train it on your company’s SOP and policy data. Pair this with modifications to architecture layers to make the final output more readable and relevant to all users.
Iterative development cycles boast several advantages over traditional waterfall-style development cycles as they allow you to plan every move in advance and get results faster. An iterative development cycle starts with making an MVP for the artificial intelligence solution and proceeds by building layers on top of the MVP to create a comprehensive solution.
There are detailed development approaches such as Agile and DevOps to help you make a structured iterative development cycle for your project. You could also go with an approach that is more oriented toward artificial intelligence projects such as MLOps to integrate additional development stages for data refinement and model training.
Estimate The Cost of AI Development in 24 Hours with Space-O
Are you still unsure about how much your AI solution would cost? Schedule a free call in your preferred time slot and Space-O’s experienced AI executives will estimate the final AI cost by understanding your industry and requirements.
Yes, open-source resources such as pre-trained models and datasets allow developers to build and fine-tune AI applications more efficiently, reducing both development time and the computational resources required. This accessibility fosters innovation and collaboration within the AI community, further driving down costs by sharing knowledge and best practices.
When implementing AI, the primary compliance concerns have to do with data privacy regulations which might be breached by data collection systems used in your AI algorithms. This can lead to a major limitation in deployability to regions such as the EU where data security laws are strictly enforced. Additional legal disputes and penalties may also be levied if your AI technology is found to be non-compliant in nature.
Outsourcing artificial intelligence or machine learning projects is a great way to offset costs but it leaves businesses confused when choosing their AI development partner. It is generally advised to look for the following criteria to ensure that you find the right AI development partner:
Chatbots are undoubtedly one of the most affordable and reliable AI solutions to integrate into any service. The affordability of such AI systems is primarily due to the rise in pre-trained NLP models that accurately decipher user intent to provide appropriate responses. These AI-powered chatbots are a perfect starting point for implementing AI as they offer much better functionality compared to the outdated decision-tree style chatbots.
In this blog, you have gained a comprehensive understanding of all the various costs and variables involved which determine the cost of AI. We also explored the ways in which you can make the journey of implementing AI holistic while reducing the overall cost of development. Yet the true scope of your solution is what will ultimately decide and finalize your cost of AI development.
To know exactly which AI solutions would be best for you, and how much they would cost, you need the help of an esteemed AI development partner like Space-O. Our team of AI engineers and data scientists can rapidly plan the perfect AI systems and development processes to give you an accurate estimate of how much it would cost you.
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