Automatic text classification is an important area of study in natural language processing (NLP) and machine learning. Text classification has become essential for businesses and organizations to handle incoming documents effectively and efficiently. The main objective of this study is to introduce and evaluate a selection of Free Open Source Software approaches for document classification and compare them against each other regarding their prediction performance and efficiency to identify the best candidate for a specific use case. In addition, the study compares the selected approaches prediction performance, efficiency, and cost-effectiveness with commercial providers’ proprietary software. This comparison provides insights into different approaches’ relative strengths and weaknesses to help businesses decide on the best strategy for their needs.