Share
The generative AI revolution has been slowed down by two roadblocks: high costs and security concerns. However, with the release of DeepSeek's V3 and R1 models, the game has changed. These models deliver state-of-the-art performance on par with the latest and greatest from OpenAI and Anthropic at a fraction of the cost while enabling secure, customizable deployments. This shift is redefining how companies approach their GenAI strategy. Organizations that held back due to costs and security concerns no longer have an excuse to delay, while those already leveraging AI can now scale more use cases without breaking the bank.
DeepSeek’s V3 and R1: State-of-the-Art Performance, Now Accessible
DeepSeek’s latest models rival LLMs across critical benchmarks, including coding, reasoning, and creative tasks. They not only match industry leaders in performance but are cost effective and open, meaning their architecture and “weights” (the core data defining their intelligence) are publicly available. This combination of affordability and transparency expands possibilities for GenAI applications. Now, let’s explore how companies can enhance security, transparency, and flexibility at a fraction of the cost.
Control, Security, and Customization: The Power of Open Models
With open-weight models, enterprises gain greater control over their AI systems. They can:
- Self-host on-premises or in private clouds, ensuring sensitive data remains within their infrastructure.
- Fine-tune models to meet specific business needs, improving accuracy and relevance.
What is Fine-Tuning?
Think of a large language model like a medical graduate before residency. They know a little bit about everything but are not experts in any one field. For that, they need additional training. So, when your problem goes beyond the General Practitioner’s knowledge, they refer you to a specialist.
An LLM is similar: you can find a readily available model with general knowledge, but if your use case is not generic, fine-tuning your model will be beneficial. Fine-tuning allows businesses to integrate proprietary information such as customer interactions, industry jargon, and compliance guidelines to specialize it. Fine-tuning achieves higher accuracy, brand-aligned outputs, contemporary relevancy, domain adaptation, and fewer response errors. Models like GPT-4o have limited fine-tuning capabilities compared with open models like DeepSeek.
Cost Savings Without Compromise
DeepSeek’s pricing model disrupts industry norms, making high-performance AI more accessible:
- API costs are up to 90% lower than GPT-4 or Claude.
- Self-hosting eliminates per-query fees, significantly reducing long-term expenses.
Real-World Impact: A Chatbot Case Study
Consider a customer-facing chatbot designed to assist users by leveraging publicly available information about your products. If the chatbot cannot resolve a query, it seamlessly directs the customer to a live agent.
Now, imagine all relevant product information is stored in a 300-page PDF. Each time a customer interacts with the chatbot, the entire document is scanned to generate a response
In GenAI-parlance, every chat interaction spends approximately 200.000 tokens of input and 1.000 of output. For simplicity, assume that each word in the PDF, every customer input, and each chatbot-generated response counts as a token. Since the chatbot processes the full document for every new interaction, token usage scales rapidly.
One of our enterprise clients deployed a similar chatbot that handles 5,000 chats per day! The cost difference, as shown below, is staggering.
And this is just for a single GenAI use case. We see many companies that are starting to scale to 20-30 new use cases per year, leading to millions in annual savings without compromising on quality.
The GenAI Landscape Has Changed—Adapt or Fall Behind
DeepSeek’s V3 and R1 are not just incremental improvements. They represent a paradigm shift: enterprise-grade AI at commodity prices, with unmatched control and transparency.
- If you’ve delayed GenAI projects due to cost or security concerns: Now is the time to act.
- If you’ve already adopted GenAI: Re-evaluate your stack. Sticking with closed, expensive models risks overspending and stagnation.
Organizations that embrace open, efficient AI will have the competitive edge. The question isn’t whether AI will transform your industry, it’s whether you’ll be ahead of the curve or trying to catch up. Learn how to identify high-impact AI initiatives and maximize value with our How to Prioritize Large Language Model Use Cases whitepaper. Download it now for a strategic framework to guide your LLM investments: How to Prioritize Large Language Model Use Cases.
Act now. Your competitors certainly are.
Xebia can help you deploy, fine-tune, and scale DeepSeek’s models securely. You can explore our Scaled GenAI Services and contact us today to build a GenAI strategy that maximizes ROI, minimizes risk and creates business value uniquely for your business.