Modern software systems expose hundreds of configuration options whose combinations can substantially affect execution time, memory and energy usage. Measuring software performance resulting from a configuration set requires running a realistic workload. Since the configuration space grows exponentially, exhaustive benchmarking is infeasible. Prior work therefore trains models on a small set of measured configurations to predict the performance of the rest. Yet these models are specific to a particular software version, hardware platform, and workload, so changes require new measurements and retraining, and such changes are becoming more frequent with coding agents. We challenge this per-system training assumption with SNAPPERF (Software-aware, No-retraining, Adaptive Prediction), a sample-efficient tabular foundation model specialized for configurable software. SNAPPERF is pretrained once on synthetic data. Each new update only requires a few measured configurations as context to predict the rest without any per-system training or hyperparameter tuning. Across 39,866 configurations from 12 real-world systems, spanning 6 domains and 4 performance metrics, SNAPPERF reduces prediction error by 38% over the prior state of the art at the same measurement budget, and by 25% over the best general-purpose tabular foundation model. It matches the state of the art's accuracy with 54% fewer measurements on average. These results suggest that knowledge about software can be encoded in pretraining data rather than in per-system models, opening a new research direction in software-aware pretraining.

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