Build vs Buy AI Investments in 2025: Rethinking the Build vs. Buy Debate in the Wake of DeepSeek’s Rise

Aquant’s Oded Sagie offers insights on re-thinking the build vs. buy debate in the wake of DeepSeek’s rise. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.

The traditional “build vs. buy” debate is no longer a binary choice – open AI models like DeepSeek-R1 introduce a new path where companies can leverage foundational AI technology to develop their own solutions without starting from scratch. At the same time, outsourcing AI solutions remains a strong alternative for organizations prioritizing speed, simplicity, and lower maintenance costs.

The Changing AI Landscape: Beyond Bigger Models

DeepSeek’s approach diverges from the conventional trend of expanding AI models through sheer scale. Instead, it focuses on reinforcement learning, fine-tuning, and data distillation to optimize reasoning and efficiency. More importantly, its open model allows companies to build upon it, much like how AI platform companies use OpenAI’s models like GPT4o to develop customized solutions.

This shift offers key lessons for enterprises evaluating their AI strategy:

• Size isn’t everything: DeepSeek demonstrates that smaller, well-optimized models can perform competitively against their larger counterparts, shifting the focus from raw power to intelligent refinement.
• Development is only the beginning: Building an AI model, even with an open-source foundation, requires continuous updates, training, and optimization to maintain relevance and performance.
• Cost-efficiency matters: DeepSeek’s ability to train at a lower cost challenges the assumption that larger budgets always yield better AI outcomes.

However, despite its advancements, DeepSeek has not yet surpassed the most cutting-edge AI models developed elsewhere. Industry experts note that its capabilities align with models that were released months prior, reinforcing the reality that staying at the forefront of AI requires ongoing investment and adaptation.

The Build vs. Buy Decision: Strategic Considerations for AI Investments

DeepSeek’s emergence reinforces that AI adoption is no longer a choice between fully building from scratch or buying off-the-shelf solutions. Instead, businesses now have the flexibility to customize and refine AI models based on existing open-source frameworks – or to outsource AI entirely. The key considerations include:

Customization vs. Foundation

• Building on Open Models: Leveraging DeepSeek or other open-source models enables companies to customize AI for specific needs while significantly reducing the effort required to develop models from the ground up.
• Purchasing Solutions: Fully managed AI platforms provide speed to market, pre-built infrastructure, and ease of deployment. Many businesses find outsourcing to be the best choice when they lack the technical expertise or internal resources to manage AI model development and maintenance.

The Advantages of Outsourcing AI

• Faster Time to Value: Pre-built AI solutions can be deployed immediately, allowing companies to focus on strategic applications rather than technical development.
• Reduced Technical Burden: Organizations without a dedicated AI engineering team may struggle with maintaining, updating, and optimizing models. Outsourcing ensures that these challenges are handled by expert vendors.
• Cost Predictability: While open AI models reduce initial development costs, long-term maintenance and operational expenses can add up. Outsourcing AI through managed services offers a predictable pricing model, often making it more financially sustainable in the long run.

Domain-Specific Expertise

• AI solutions perform best when they are trained with industry-specific data. Whether built internally, adapted from existing models, or fully outsourced, AI must be tailored to real-world use cases to be truly effective.

Scalability and Maintenance

• AI development is not a one-time project; it requires continuous investment in data updates, monitoring, and refinement.
• Companies that choose to build on open-source models must still invest in engineering resources to maintain and enhance performance over time, while outsourced solutions often include built-in updates and enhancements as part of the service.

Finding the Right AI Strategy 

DeepSeek’s emergence reinforces that AI success isn’t solely about who builds the biggest model – it’s about who strategically applies AI to drive the most value. Organizations in 2025 will need to rethink their approach: rather than choosing between building and buying, they must determine how best to combine foundational AI models with their unique business needs.

For companies with strong AI capabilities, leveraging open-source models like DeepSeek can provide the customization and control needed to build a competitive advantage. However, for organizations prioritizing speed, simplicity, and predictable costs, outsourcing AI solutions remains a viable, often preferable, option.

Whether building on open AI frameworks or purchasing fully managed solutions, the key to AI success lies in thoughtful implementation, continuous optimization, and a clear focus on delivering real-world value.