What major advantage does TensorFlow offer to developers?

TensorFlow provides significant advantages to developers, particularly due to its access to a wide range of applications. This open-source library is designed for various tasks in machine learning, including but not limited to image recognition, natural language processing, and predictive analytics. Its versatility allows developers to build complex models for different domains, thereby broadening the scope of what they can accomplish with a single framework.

The extensive ecosystem associated with TensorFlow, including tools like TensorBoard for visualization and TensorFlow Extended (TFX) for production machine learning pipelines, further enhances its utility. This robust support for diverse applications makes it easier for developers to implement machine learning solutions that can be tailored to their specific needs across industries, from research to enterprise-level solutions.

The other options present drawbacks or limitations that do not align with the advantages that TensorFlow provides. For instance, suggesting high costs for deployment or requiring hardware upgrades undermines the accessibility TensorFlow aims to offer by being adaptable to various environments, including those with limited resources. Additionally, integration with external tools is actually a feature that enhances TensorFlow's capabilities, contrary to the claim of lack of integration.