Empowering Enterprise Generative AI with Flexibility: Navigating the Model Landscape

Empowering Enterprise Generative AI with Flexibility: Navigating the Model Landscape

The world of Generative AI (GenAI) is rapidly evolving, with a wide array of models available for businesses to leverage. These models can be broadly categorized into two types: closed-source (proprietary) and open-source models.

Closed-source models, such as OpenAI’s GPT-4o, Anthropic’s Claude 3, or Google’s Gemini 1.5 Pro, are developed and maintained by private and public companies. These models are known for their state-of-the-art performance and extensive training on vast amounts of data. However, they often come with limitations in terms of customization, control, and cost.

On the other hand, open-source models, such as Llama 3 or Mistral, are freely available for businesses to use, modify, and deploy. These models offer greater flexibility, transparency, and cost-effectiveness compared to their closed-source counterparts.

Advantages and Challenges of Closed-source Models

Closed-source models have gained popularity due to their impressive capabilities and ease of use. Platforms like OpenAI’s API or Google Cloud AI provide businesses with access to powerful GenAI models without the need for extensive in-house expertise. These models excel at a wide range of tasks, from content generation to language translation.

However, the use of closed-source models also presents challenges. Businesses have limited control over the model’s architecture, training data, and output. This lack of transparency can raise concerns about data privacy, security, and bias. Additionally, the cost of using closed-source models can quickly escalate as usage increases, making it difficult for businesses to scale their GenAI applications.

 The Rise of Open-source Models: Customization, Control, and Cost-effectiveness

Open-source models have emerged as a compelling alternative to closed-source models, and usage has been on the rise. According to GitHub, there was a 148% year-over-year increase in individual contributors and a 248% rise in the total number of open-source GenAI projects on GitHub from 2022 to 2023. With open-source models, businesses can customize and fine-tune models to their specific needs. By training open-source models on enterprise-specific data, businesses can create highly tailored GenAI applications that outperform generic closed-source models.

Moreover, open-source models provide businesses with complete control over the model’s deployment and usage. According to data gathered by Andreessen Horowitz (a16z), 60% of AI leaders cited control as the primary reason to leverage open source. This control enables businesses to ensure data privacy, security, and compliance with industry regulations. Open-source models also offer significant cost savings compared to closed-source models, as businesses can run and scale these models on their own infrastructure without incurring excessive usage fees.

Selecting the right GenAI model depends on various factors, including the specific use case, available data, performance requirements, and budget. In some cases, closed-source models may be the best fit due to their ease of use and state-of-the-art performance. However, for businesses that require greater customization, control, and cost-effectiveness, open-source models are often the preferred choice.

Cloudera’s Approach to Model Flexibility and Deployment

At Cloudera, we understand the importance of flexibility in GenAI model selection and deployment. Our platform supports a wide range of open-source and closed-source models, allowing businesses to choose the best model for their specific needs.


Fig 1. Cloudera Enterprise GenAI Stack
Openness and interoperability are key to leverage the full GenAI ecosystem.

With Cloudera, businesses can easily train, fine-tune, and deploy open-source models on their own infrastructure. The platform  provides a secure and governed environment for model development, enabling data scientists and engineers to collaborate effectively. Our platform also integrates with popular open-source libraries and frameworks, such as TensorFlow and PyTorch, ensuring compatibility with the latest advancements in GenAI.

For businesses that prefer to use closed-source models, Cloudera’s platform offers seamless integration with leading public cloud AI services, such as Amazon Bedrock. This integration allows businesses to leverage the power of closed-source models while still maintaining control over their data and infrastructure.

Find out how Cloudera can help fuel your enterprise AI journey. 

Robert Hryniewicz
Director of Product Marketing
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