Various studies have pointed out that the performance of language models is poor in non-English or non-European languages. One of the factors affecting this performance is the effectiveness and suitability of the tokenization scheme used in the model. Indic scripts require multiple Unicode codepoints to represent a single visual unit to be encoded in the standard UTF-8 scheme. This paper investigates the effect of multiple tokenizers that use UTF-8 text input on the downstream performance of pretrained language models for Hindi and Marathi, languages written in \textit{Devanāgari} script. We present the intrinsic performance of the tokenizers using Fertility, Rényi Efficiency and Percentile Frequency, and report the extrinsic performance of monolingual and multilingual models on question-answering tasks, using an automated parts-of-speech and sentence similarity based evaluation framework, and on word-level tasks such as grapheme-to-phoneme conversion and transliteration. We propose a grapheme cluster tokenizer for the script which shows performance better than or competitive with other popular tokenizers. We also find that the Rényi Efficiency metric is highly correlated to downstream performance on question answering.