Pretraining language models for low-resource languages poses significant challenges due to scarce and poor-quality data, a lack of comprehensive evaluation benchmarks, and often limited computational resources. Research on compute-optimal language modeling typically focuses on scaling up decoder language models efficiently for high-resource languages. While some studies have investigated the down-scaling of encoder language models for low-resource languages, they often prioritize optimizing for computational constraints rather than pretraining text volume constraints. We address this research gap by analyzing the scaling behaviors of encoder language models which use the Replace Token Detection (RTD) and Masked Language Modeling (MLM) objectives under limited pretraining text volumes. By downsampling three different high-resource languages (English, French, Korean) and two low-resource languages (Xhosa and Swahili), we simulate varying degrees of data scarcity and evaluate downstream performance using established benchmarks such as the GLUE benchmark for English, FLUE for French, KLUE for Korean, and MasakhaNEWS for Xhosa and Swahili. Our findings demonstrate that optimal MLM accuracy scales logarithmically with increasing pretraining text volume across these diverse languages. Additionally, our results show that RTD models consistently outperform MLM models in low-resource scenarios, achieving superior downstream performance with pretraining text volumes smaller than 1000MB for downsampled high-resource languages. However, we find that RTD performs worse than MLM for Xhosa and Swahili. We also find that dynamic masking significantly improves MLM accuracy in these settings. Furthermore, our results show that smaller models are more effective for smaller pretraining text volumes, highlighting the importance of adjusting model size according to data availability in order to maximize performance and efficiency.