Microbial growth phenotype is commonly summarised by a small set of parameters: specific growth rate, lag phase duration, maximum cell density, and growth curve profile; yet even within a single genus these characteristics can vary substantially across species, with direct consequences for how growth performance is interpreted and compared. This challenge is particularly pertinent for the largely uncharacterised Halomonas genus, whose members are increasingly proposed as next-generation industrial biotechnology chassis owing to their halotolerance and capacity for open, non-sterile cultivation. In this work, the growth phenotypes of 82 Halomonas strains were assessed under collection-recommended conditions of temperature, growth medium, and salt concentration using optical density measurements in 96-well plates. Maximum specific growth rates ranged from 0.05 ± 0.03 to 1.14 ± 0.34 h⁻¹ (mean 0.44 ± 0.22 h⁻¹), with the shape of the growth curve frequently deviating from the canonical sigmoidal form and displaying substantial inter-strain variability. No meaningful correlation was identified between any growth parameter and either cultivation temperature or salt concentration (−0.39 ≤ ρ ≤ 0.39). Considered collectively, growth-associated parameters identified H. hamiltonii, H. zhaodongensis, H. heilongjiangensis, and H. magadiensis as promising candidates for future biomanufacturing applications. The variability observed in growth curve profiles raised a critical methodological concern regarding researcher-dependent identification of growth phases and the computation of growth parameters. Six independent analysts assessed the same dataset, and at least one determined a significantly different maximum specific growth rate for half of the strains examined (p < 0.05). This has broad implications beyond strain characterisation: analyst interpretation directly affects reported growth rates, which serve as key inputs and constraints in metabolic modelling and upstream bioprocess design. The reliability of both primary literature and secondary computational analyses built upon these data is therefore directly affected by analyst-specific decisions, a problem further compounded for poorly characterised organisms, whose growth parameters are often borrowed from related species in the absence of species-specific data, introducing a largely unacknowledged source of uncertainty. This foundational phenotypic dataset highlights an urgent need for standardised, generalisable frameworks for growth parameter determination, particularly for non-model organisms.