Analyzing What it Costs to Develop an AI App Like DeepSeek

DeepSeek has burst onto the AI scene almost out of nowhere, and randomly, the rivaling long-standing giant, OpenAI, from the first day. Its promise of efficient and cost-effective AI solutions is actively shaping the AI app market.

Despite this meteoric rise, the platform has also been faced with heavy criticism and prospective prohibitions by different government bodies. This presents a unique chance for businesses to capitalize on the moment and become the next AI innovator. 

Understanding the DeepSeek costs associated with developing your own AI app is critical. To address this concern, this article provides a full overview of the elements driving the cost of constructing an AI software like DeepSeek, giving insights into how to budget for and potentially dominate this fascinating new terrain.

But first:

What is DeepSeek? 

DeepSeek is a Chinese AI startup based in Hangzhou, China, founded by 40-year-old entrepreneur Liang Wenfeng in May 2023. The company specializes in developing open-source LLMs, just like the popular OpenAI company responsible for the development of chatGPT. 

Since its launch in 2023, DeepSeek has developed several highly efficient LLMs responsible for highly specialized tasks. Some of these include the DeepSeek Coder, released in November 2023, which is built to handle coding-related tasks, and the DeepSeek-V2, released in December 2023 and responsible for more general purposes. After these early releases, DeepSeek launched different iterations of these models, such as the DeepSeek-Coder-V2, DeepSeek-V3, Janus-Pro-7B, and DeepSeek-R1. 

Despite the fact that DeepSeek has been launching different LLMs in the past 2 years, it only started receiving wide global attention in January 2025 after the launch of its latest LLM, the DeepSeek-R1 model. 

The DeepSeek-R1 model is an advanced reasoning model designed to compete with OpenAI’s o1 models. This LLM is built using a Mixture of Experts (MoE) architecture, which encompasses a large-scale reinforcement learning approach, a reward engineering incentive system, efficient knowledge transfer techniques, and an emergent behavior network for handling reasoning tasks. In addition to all these advanced technological features, the LLM is also open-source and free compared to OpenAI’s o1 models. 

Since this launch, the name DeepSeek has been on the lips of everyone invested in the advancements of AI technology, and while the name DeepSeek technically refers to the company behind these model rollouts, many people loosely refer to DeepSeek as the app or large language model for handling advanced reasoning tasks. 

DeepSeek vs. OpenAI: A Side-by-Side Comparison

First, both DeepSeek and Open AI are LLM development companies that focus on rolling out AI models for different purposes. While OpenAI has launched reputable AI models like chatGPT and o1, DeepSeek’s latest rollouts include the DeepSeek-R1 and the Janus-Pro-7B, among others. 

To give things a bit more granular perspective, let’s examine some vital comparisons between DeepSeek’s latest R1 model and OpenAI’s ChatGPT. 

Both DeepSeek and ChatGPT are advanced AI models designed to handle various tasks. However, they are more suited for different tasks and priorities. Generally, DeepSeek focuses on delivering precise and tailored responses for unique and advanced use cases like data analysis and enterprise solutions. On the other hand, ChatGPT is more of a general-purpose language model capable of handling conversational interactions and generating human-like texts. 

Furthermore, DeepSeek-R1 is built for more logically-intensive operations like coding, math, and structured problem-solving. On the flip side, chatGPT is more versatile, with the ability to handle a bit of everything from conversations and creative writing to brainstorming sessions. While DeepSeek can self-correct and research sources across the internet, chatGPT offers a more polished interface with features like chat history and voice mode. 

Below is a comparison table that places both DeepSeek side-by-side to highlight their core differences. 

How Much did DeepSeek Cost During Development? 

Apparently, this is the question on the lips of every AI enthusiast since DeepSeek launched. 

The company released a paper in which DeepSeek researchers revealed that it costs just $5.576 million to train its latest DeepSeek-v3 LLM in only 2 months. According to the official release notes, this figure was for only the model’s “official training” costs based on the rental prices of Nvidia’s graphics processing units. As such, the stated cost didn’t include the costs of “prior research and ablation experiments on architectures, algorithms, and data.” 

Nonetheless, despite these omissions, the AI community has responded to this disclosure by claiming that the actual costs are significantly higher than those in DeepSeek’s report.  

Moving away from the reported training costs, semiconductor research, and consulting firm SemiAnalysis examined DeepSeek’s cost of development more closely, factoring in hardware spending and other additional expenses. SemiAnalysis tried to divert attention away from the “$6 Million” tag, claiming it was wrong and akin to pointing to a specific part of a bill of materials for a product and treating it as the entire cost. 

The report went on to point out that the hardware for DeepSeek cost well over $500 Million across the company’s history. Therefore, getting an accurate description of how much DeepSeek costs requires a comprehensive look at several aspects of development, like infrastructure investments, research and development (R&D), training, and other hardware components.

These and other vital considerations are considered by AI development companies when developing AI apps like DeepSeek. In the following section, we’ll break down some of the vital cost factors involved in the development process. 

Understanding the Cost Factors Involved in Developing an AI App Like DeepSeek

When you understand the vast capabilities of an AI app like DeepSeek, you’ll see that it takes a lot of different working parts to make such an app function optimally. However, building AI for business requires different considerations because you might need the app to either perform basic or complex functions depending on your organizational needs. 

At Debut Infotech, our AI consulting services help you identify these unique needs and plan for your application appropriately. 

Nonetheless, the cost factors to consider when looking to develop an AI app like DeepSeek include the following: 

1. Training Data

Training data is the foundation of any successful AI app because it helps the AI model learn and make better decisions. More importantly, it exposes the AI model to the scenarios it is likely to encounter in the real world so that it doesn’t produce inaccurate or biased results when called to action. 

Therefore, it determines the development costs for building an AI app like DeepSeek. 

It is expensive to acquire high-quality and diverse datasets, which are necessary for developing top-quality AI apps. In addition, you’ll also have to factor in the costs of preprocessing this training data when you access it to make sure it is safe for use. 

2. Model Complexity and Size

The more parameters an AI model has, the more data you need to train such a model. That’s why the model complexity and size sometimes account for close to 30% of the total project cost. The recently released DeepSeek-R1 LLM has a whopping 671 billion parameters. So, you know it requires a sizable amount of production costs, as SemiAnalysis explains. 

This means models with greater complexities and size will require a significant amount of computational resources and, by extension, huge training data and, consequently, high costs. So, you need to decide on the model’s proposed use cases to identify how much model complexity and size is ideal. 

3. Hardware Costs and Computational Resources

Although DeepSeek only revealed details about the cost of training its model, we’ve highlighted that reports by SemiAnalysis show that DeepSeek has spent significantly on hardware and other computational resources over the past two years. 

These funds are diverted toward acquiring graphics processing units (GPUs) and Tensor Processing Units (TPUs). These hardware components help accelerate machine workloads and handle graphics-related tasks, such as testing the model’s ability to generate clear images. 

For some models, you may decide to opt for cloud services that you’ll pay for monthly. However, using them continuously over time may become expensive. 

4. Talent Acquisition

Of course, most of the technical components we’ve been highlighting previously will be put together by experienced professionals. This means you have to hire artificial intelligence developers, machine learning engineers, and NLP experts to help structure and develop the app to meet your organization’s outlined needs. 

This also costs money. In fact, setting up an in-house team of skilled data professionals and engineers might incur more costs because their compensation systems aren’t exactly project-based. AI development companies, on the other hand, often have incentivized pricing models that leverage their existing connections with experienced developers and specialists. 

5. API Integration and Management 

Would you love for the API app you’re about to create to integrate seamlessly with other systems? 

If you answered yes to that question, then you should prepare to handle another layer of complexity, which attracts extra costs when developing your AI app. This is because providing robust API access attracts extra costs during development. Nonetheless, it facilitates smooth operations and data protection for both your organization and the AI app’s target users. 

6. Security and Compliance 

Security and compliance are two of the most indispensable cost factors to consider when building an AI app like DeepSeek. Due to the rising threats of cyber-attacks on AI platforms, different regulatory bodies have started upgrading the adherence guidelines and conditions for anyone to develop and use an AI large language model app. 

As such, you must intentionally set reasonable budgets for data protection guidelines like GDPR and other regulatory bodies in your region. You must also implement robust security features to complement your existing security infrastructure. For instance, you need to invest heavily in secure data storage systems, encrypted communications, and legal security audits. 

7. Language and Translation Capabilities 

The DeepSeek R1 model currently supports multiple languages, such as English and Chinese. This implies that the LLM contains awesome Natural Language Processing (NLP) tools and models that help it understand, interpret, and generate output in multiple languages. 

These additional components require money for development. More so, the more the number of languages you want your AI app to support, the more the complexity and, consequently, the more the development fees. 

8. Licencing and Legal Fees

Licensing charges for development tools such as TensorFlow and PyTorch range from $100 to $1,0001. Using existing Large Language Models (LLMs), such as GPT-4o, incur expenses based on usage. Likewise, regulatory compliance, especially for apps managing sensitive data, requires adherence to rules such as GDPR and HIPAA, dramatically boosting costs.

Legal consultations for patents, copyrights, and compliance can cost anywhere from $5,000 to $15,000. Ignoring these considerations can result in legal problems and significant fines. Accurate budgeting for licensing and legal issues is necessary for the successful and lawful deployment of an AI application.

DeepSeek Cost Estimation Table 

So, how much exactly does it cost to put an AI app like DeepSeek together? 

It strongly depends on all the factors highlighted above. However, the table below provides an estimate of the different factors involved in developing this app to give you a rough handle on the approximate costs.

Conclusion

Building an AI program like DeepSeek involves a considerable investment, typically ranging from $50,000 to $300,000 or more, depending on the app’s complexity. This means the price varies based on your unique choices for that app. 

DeepSeek appears as a cost-effective alternative to models such as OpenAI’s ChatGPT, with stated development costs of around $6 million. In addition to your unique choices, model complexity, training data, computational resources, talent acquisition, language capabilities, real-time processing, API integration, security, legal fees, and ongoing maintenance all have an impact on costs. To manage these complications and ensure a successful AI project, visit a reputable AI development company such as Debut Infotech Pvt Ltd for experienced advice and solutions tailored to your requirements.

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