DeepSeek Explained: Why This AI Model Is Gaining Popularity

As businesses adopt AI to simplify operations, improve automation, and develop innovative products, your choice of AI models will determine efficiency, scalability, and integration with existing systems. Though closed-source AI models like OpenAI’s GPT and Anthropic’s Claude have dominated the market have dominated the market, the options of open-source alternatives such as LLaMA, Mistral, Falcon, and BLOOM gained traction because of the transparency, community-driven innovation, and the range of control they offer the businesses over their AI implementations.

DeepSeek, a Chinese startup founded in 2023, offers its AI models as open source, including its R1 reasoning model, allowing for free use and adaptation. The technology industry took notice of DeepSeek for several reasons, but its development cost of under $6 million and cost-efficient hardware stood out. From software development and multimodal applications to real-time decision-making systems, DeepSeek proves that open-source AI can compete with some of the most advanced proprietary models. Read on to discover how DeepSeek works, how its models stack up against competitors, and why its cost-effective approach might change how businesses think about implementing AI solutions.

What is DeepSeek?

DeepSeek AI is an artificial intelligence platform specialized in natural language processing, computer vision-language tasks, and code generation. The platform offers a range of specialized models, including:

• DeepSeek-VL. Designed for multimodal understanding, integrating visual and textual data.

• DeepSeek-Coder. Specific to software development and provides AI coding assistance.

• DeepSeek-V2. A Mixture-of-Experts (MoE) language model optimized for efficient processing and inference.

• DeepSeek-V3. Latest version with improved capabilities in complex reasoning, mathematical problem-solving, and programming.

• DeepSeek-R1. First-generation reasoning model which focuses on logical inference, mathematical reasoning, and real-time problem-solving.

Key features

• Offers multiple model sizes, including 7B and 67B, with open-source options for research and commercial use.

• Provides flexible API access, allowing businesses and developers to integrate AI capabilities with transparent service status monitoring.

• Features a suite of applications, including:

• DeepSeek app. A mobile application for AI-powered interactions.

• DeepSeek chat. An AI chatbot for conversational engagement.

• DeepSeek platform. A centralized hub for model integration and deployment.

Pricing information

DeepSeek AI follows a hybrid pricing model, combining open-source availability with paid API-based services.

• Open-source models: Certain models, like DeepSeek LLM 7B, 67B, and DeepSeek-Coder, are freely available, which allows users to run them on their hardware.

• Cloud-based API access: For those who prefer a managed service, DeepSeek provides cloud-hosted models with a token-based pricing structure. The pricing varies based on cache hits and misses, which means that frequently accessed data is cheaper to process than new requests.

API pricing breakdown (Per 1 million tokens)

How does DeepSeek work?

DeepSeek AI operates through a pipeline that integrates deep learning models, data processing techniques, and optimized inference mechanisms. Below is a step-by-step breakdown of DeepSeek’s workflow:

Data ingestion and preprocessing

Before training the AI models, DeepSeek collects vast amounts of text, code, and multimodal data from diverse sources. This data undergoes a rigorous preprocessing phase, which includes:

• Tokenization. Breaking down text into smaller units (tokens) for efficient processing.

• Normalization. Removing inconsistencies like redundant spaces, special characters, and case variations.

• Filtering. Eliminating low-quality or biased data to improve model reliability.

• Encoding. Converting text into numerical representations suitable for deep learning models.

Training using an MoE approach

DeepSeek follows a Mixture-of-Experts (MoE) architecture to improve efficiency and model performance. This approach involves:

• Splitting tasks among multiple expert models. Instead of a single neural network handling all computations, DeepSeek activates specialized sub-models (experts) based on the input type.

• Routing mechanism. A gating network determines which expert models should process specific inputs, reducing computational load.

• Parallel training on distributed GPUs. The model is trained across multiple GPUs and tensor processing units (TPUs) to accelerate learning while handling large-scale datasets.

Fine-tuning and reinforcement learning

To improve its performance across different domains, DeepSeek undergoes fine-tuning and reinforcement learning steps:

• Supervised fine-tuning. The model is trained on specific datasets relevant to coding, reasoning, or multimodal tasks for domain-specific expertise.

• Automated reinforcement learning. Instead of relying solely on reinforcement learning from human feedback (RLHF), DeepSeek uses an automated feedback mechanism that evaluates and refines responses without extensive human intervention. However, RLHF is selectively used to align outputs with safety standards and reduce harmful responses.

Real-time processing

When a user submits a query or request, DeepSeek processes it through an optimized inference system that ensures fast and accurate results. The steps involved are:

• Query tokenization and embedding. The input is broken into tokens and mapped into a high-dimensional space to understand the context.

• Efficient expert selection. The MoE system routes the query to the most relevant expert models.

• Parallel computation for response generation. Multiple GPUs process the request simultaneously, reducing latency.

• Post-processing and filtering. The AI-generated response undergoes content moderation and quality checks before being delivered to the user.

Deployment and continuous learning

DeepSeek continuously evolves through real-world interactions and updates:

• Model updates and retraining. New data is regularly incorporated to refine accuracy and efficiency.

• API integration and scalability. The model is deployed via cloud-based APIs for integration into applications while scaling resources based on demand.

• User feedback-driven improvements. Continuous monitoring and feedback collection help DeepSeek improve response quality and security over time.

How is DeepSeek different from other AI models?

DeepSeek has several unique attributes that set it apart from other models.

Open-source accessibility

Unlike many proprietary AI models, DeepSeek adopts an open-source approach (DeepSeek LLM 7B, 67B, and DeepSeek-Coder), which allows developers and researchers to access, modify, and implement its large language models freely.

• DeepSeek-Coder-V2 offers an MoE architecture trained on an additional 6 trillion tokens and has demonstrated performance matching to proprietary models like GPT-4 Turbo in code-specific tasks.

• DeepSeek-R1 is an open-source reasoning model that matches the capabilities of OpenAI’s o1.

Training costs

The development of DeepSeek was under $6 million using less-advanced hardware like NVIDIA H800, which is several times less than the leading AI models while maintaining competitive performance levels. This cost reduction was achieved through several technical optimizations. Instead of updating all parameters during training, DeepSeek employed selective module training, which focuses only on essential components and reduces computational overhead. It also introduced auxiliary-loss-free load balancing, using a bias term to dynamically distribute tasks without additional loss functions, improving efficiency.

Integration into consumer electronics

While other AI models, such as Amazon’s Alexa, have been integrated into consumer electronics as voice assistants to facilitate user interaction and control, DeepSeek’s approach is distinct.

DeepSeek’s AI models have been embedded directly into the core functionality of household appliances, which supports on-device intelligence and advanced automation. Major Chinese manufacturers such as Haier, Hisense, and TCL Electronics have adopted DeepSeek’s technology in products like televisions, refrigerators, and robotic vacuum cleaners. This integration helps these devices process complex user commands and perform tasks with greater precision.

For instance, a robotic vacuum cleaner equipped with DeepSeek’s R1 model might interpret detailed cleaning instructions and navigate efficiently, offering a more tailored and effective cleaning experience.

Uses cases of DeepSeek

DeepSeek’s AI models have already been adopted across various sectors to improve operations and user experiences.

Medical diagnostics

DeepSeek AI models are used in medical imaging and diagnostics to assist radiologists in abnormal conditions through AI-powered analysis of X-rays, MRIs, and CT scans. Hospitals in China have begun adopting DeepSeek’s models to improve diagnostic precision and patient care. They can also help in healthcare by summarizing patient records and generating medical reports.

Software development

DeepSeek-Coder-V2, an MoE model, assists developers with code generation, bug detection, and code completion across 338 programming languages. It matches proprietary models like GPT-4 Turbo in coding benchmarks and can be fine-tuned for custom enterprise applications.

Content creation

DeepSeek-V3 can aid in AI-generated content for copywriting, scriptwriting, and search-optimized blog creation. By generating contextual content, it helps improve productivity for marketing agencies, digital publishers, and freelance writers.

Application development

Startups deploy DeepSeek’s open-source AI models to build applications like AI-powered customer support, chatbots, and automated data analysis tools. The flexibility of DeepSeek-Coder-V2 and DeepSeek-V3 helps companies develop AI solutions without the constraints of proprietary licensing.

AI-enabled driving

DeepSeek-V3 and DeepSeek-VL are deployed in advanced driver assistance systems (ADAS), which have improved object detection, lane assistance, and real-time decision-making in vehicles. Automakers like BYD and Great Wall Motor use DeepSeek-powered AI for autonomous driving innovations.

Education

DeepSeek-V3 and DeepSeek-Chat can be used as AI tutors, which might assist students with personalized learning in subjects like mathematics, science, and language comprehension. These models provide real-time feedback, generate explanations, and help with homework and exam preparation.

Limitations of DeepSeek

While DeepSeek has gained popularity for its open-source approach and high-performance AI models, it is not without its limitations. Certain constraints in its architecture, accessibility, and regulatory compliance may impact usability, adoption, and performance.

Complex deployment

While DeepSeek offers open-source accessibility, deploying and optimizing the model requires non-trivial knowledge in AI development, machine learning, and infrastructure management. Businesses without in-house AI expertise may struggle with self-hosting, fine-tuning, and maintenance, which might lead to hiring specialists or relying on external support, increasing operational costs.

Security vulnerabilities

DeepSeek models, including DeepSeek-R1, have been found vulnerable to jailbreaking techniques, which allow users to bypass restrictions and generate unintended content. This has raised concerns about the model’s capability against adversarial attacks.

Reports indicate that DeepSeek’s cloud-based services may transmit unencrypted user data to external servers, raising security and privacy concerns for businesses handling sensitive information.

Censorship concerns

DeepSeek’s content moderation policies are shaped by regulatory requirements in China, which has led to censorship on politically sensitive topics. Investigations have revealed that DeepSeek employs both application-level and training-level censorship mechanisms. These censorship practices have raised concerns about the model’s suitability for applications requiring unbiased information in contexts like academic research and journalism. Users who seek AI models with fewer content restrictions may find DeepSeek’s moderation policies limiting compared to alternatives.

Lack of third-party integrations

While DeepSeek has been integrated into platforms like Amazon SageMaker, DigitalOcean, Azure AI Foundry, and GitHub, its adoption in widely used enterprise and consumer applications might remain limited. Unlike AI models embedded into productivity suites, DeepSeek lacks deep integration into mainstream business tools and collaboration workplace platforms This limitation might make it less accessible for businesses and developers who rely on ecosystem compatibility for simplified workflows.

DeepSeek FAQ

Is DeepSeek banned?

No, DeepSeek is not banned. However, its availability and usage may be subject to regional restrictions and compliance with local regulations in countries with strict AI governance.

What is R1 in AI?

In AI, R1 refers to reasoning-first models designed to improve logical thinking and problem-solving capabilities. DeepSeek-R1 is DeepSeek’s open-source reasoning model, which deploys reinforcement learning and advanced training techniques optimized for complex tasks like mathematical proofs, code generation, and multi-step logical reasoning.

References

1. DeepSeek’s low-cost AI spotlights billions spent by US tech

2. DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

3. How DeepSeek Is Influencing Silicon Valley’s AI Landscape

4. DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence

5. China’s DeepSeek frenzy enters the home as TV, vacuum cleaner makers adopt its AI models

6. DeepSeek reshaping healthcare in China’s tertiary hospitals

7. Chinese auto giant BYD to integrate DeepSeek, broaden self-driving tech

Deploy DeepSeek R1 for Advanced Open-Source AI

DigitalOcean’s GenAI Platform offers direct access to DeepSeek R1-distill-llama-70B, delivering powerful AI capabilities through the latest in open-source innovation. Our infrastructure handles all the complexity, letting you focus on building exceptional AI applications without managing deployment configurations or server setups. The platform’s pay-as-you-go pricing means you only pay for what you use, with direct billing through your DigitalOcean account and no additional API keys needed.

Key features:

• DeepSeek R1-distill-llama-70B architecture for advanced reasoning

• Superior code generation and mathematical problem-solving

• Comprehensive multilingual support with context awareness

• Serverless deployment with zero infrastructure management

• Direct platform integration with usage-based billing

→ Deploy DeepSeek R1