DeepSeek and the Reality of AI Model Adoption: Why “Good Enough” Models Are Important

DeepSeek, a Chinese AI startup, has recently drawn attention in the artificial intelligence community by releasing advanced models like DeepSeek-R1. These models stand out for their strong performance across multiple benchmarks, cost-efficiency, and optimized computational design. While these innovations are significant, their practical adoption remains limited—primarily due to concerns about alignment with Western regulatory and ethical standards. At the same time, many enterprises find that “good enough” models already meet their business needs, reducing the urgency to adopt cutting-edge alternatives.

This article examines DeepSeek’s innovations, the role of model distillation, and the realities of AI adoption in enterprise environments. Beyond technical capabilities, we explore critical factors that shape AI deployment decisions, including security, scalability, and ease of use.

What is DeepSeek R1?

DeepSeek R1 is a generative AI model developed by DeepSeek, aiming to compete with other leading models such as OpenAI’s GPT series. The model has demonstrated strong performance on multiple benchmarks, showcasing capabilities in text generation, reasoning, and contextual understanding. Benchmarks indicate that DeepSeek R1 achieves comparable performance to models like GPT-4 in various NLP tasks while maintaining a lower computational footprint due to its optimized architecture and use of model distillation techniques.

Some key benchmark results for DeepSeek R1 include:

• MMLU (Massive Multitask Language Understanding): Achieving scores close to GPT-4, particularly excelling in knowledge-intensive tasks.
• GSM8K (Grade School Math Problems): Showing proficiency in mathematical reasoning, though slightly lagging behind GPT-4o.
• HellaSwag & Winogrande: Strong performance in commonsense reasoning and contextual prediction tasks.

DeepSeek R1’s main appeal lies in its balance of efficiency and capability, making it a competitive choice for enterprises seeking an alternative to major AI providers.

Understanding Model Distillation

Model distillation is an advanced AI training technique that allows a smaller, more efficient model (the “student”) to replicate the capabilities of a larger, more powerful model (the “teacher”). This process dramatically reduces computational costs while maintaining a high level of accuracy, making AI solutions more accessible.

DeepSeek’s use of model distillation enables it to build and distribute high-performing AI models at a fraction of the cost of traditional AI development approaches. Below is an output token cost comparison of OpenAI’s most economical (o3-mini) and expensive (o1) models against DeepSeek’s R1:

By applying these methods, DeepSeek has managed to outcompete OpenAI on pricing by a significant margin. DeepSeek has positioned itself as a competitor to well-established AI companies such as OpenAI and Google, offering viable alternatives without requiring massive infrastructure investments.

The "Good Enough" Model Paradigm

Despite DeepSeek’s technical prowess, most client projects don’t demand cutting-edge GenAI. Many businesses prioritize cost-effectiveness, reliability, and ease of integration over top-tier models. Here’s why:

1. Specific Business Needs – Many GenAI applications are task-specific, such as chatbots, document summarization, or customer support automation. A moderately capable model can often perform these tasks adequately without requiring best-of-the-best capabilities. Whether it is DeepSeek R1 or OpenAI o1, GPT-4o is often more than good enough.




2. Cost Considerations – Using AI models served as a cloud service is usually cheaper than self-hosting, as it eliminates the need for expensive infrastructure, maintenance, and ongoing monitoring. Most user-facing applications, such as chatbots and document summarization tools, do not require extremely high loads, making hosted solutions more cost-effective. However, for automated agents that run continuously and require constant inference, self-hosting might be a more viable long-term option to control costs and latency.




3. Infrastructure Constraints – Deploying an AI model isn’t just about selecting the best one; it requires robust infrastructure and seamless integration into existing workflows.




4. Ease of Deployment & Maintenance – Models like GPT-4o are available as secure services through Azure, simplifying deployment and maintenance. With DeepSeek R1, secure and easy deployment as a service is currently limited to specific regions (e.g., US only for Azure and AWS), requiring additional considerations, especially for European companies.

Security Considerations: A Crucial Factor in AI Adoption

AI security is a critical aspect that many organizations must address before integrating AI into their operations. Even if an AI model is powerful and cost-efficient, it cannot be used effectively if it lacks proper security measures.

Deployment Security: Cloud vs. Self-Hosting

Companies must decide whether to use GenAI models as a cloud service (AIaaS) or to self-host. Each approach has distinct security considerations:

• Cloud-based GenAI Solutions
  • Advantages: Scalable, managed security, less infrastructure required.
  • Risks: Data privacy concerns, reliance on third-party providers, regulatory compliance issues.
• Self-hosted GenAI Solutions
  • Advantages: Full control over data, customization, compliance with internal security policies.
  • Risks: Higher setup and maintenance costs, security responsibility falls on the organization.

In the specific case of DeepSeek R1 vs. models like GPT-4o on Azure OpenAI, DeepSeek cannot yet reliably be used as a trusted service. Nevertheless, you can run DeepSeek R1 using Azure Container Apps with serverless GPUs, although availability is limited by region. Azure OpenAI with GPT-4o is available in many regions, though some regions do not yet support a “Standard” deployment and require PTUs.

Scaling AI: Performance vs. Practicality

Scalability is another major factor that determines the success of AI models in real-world applications. While DeepSeek’s models may perform exceptionally well, their practical adoption depends on ease of scaling and whether they are available via serverless APIs. Key considerations include:

1. Computational Efficiency – High-performance GenAI models often require extensive computing resources if self-hosted. Enterprises must balance performance needs with available infrastructure.
2. Inference Speed & Latency – GenAI models deployed in real-time applications (e.g., chatbots, voice assistants) must respond instantly. Highly complex models may introduce latency issues, especially if they are self-hosted using limited GPUs.

The Trade-Off Between Innovation and Practicality

DeepSeek’s advancements in GenAI demonstrate the potential for highly capable, cost-effective models. However, practical adoption hinges on more than just technical superiority. Factors such as security, scalability, and ease of deployment play a crucial role in real-world implementation.

While “good enough” models remain the preferred choice for most business applications due to their reliability and simplicity, emerging models like DeepSeek R1 and OpenAI o1/o3 enable advanced use cases requiring deeper reasoning and more complex problem-solving. As the AI ecosystem evolves, organizations seeking to push the boundaries of AI capabilities will increasingly consider these new-generation models for specialized, high-value tasks.