Timber compiles trained tree-based models from XGBoost, LightGBM, scikit-learn, CatBoost, and ONNX into optimized native C99 code. The tool eliminates Python runtime overhead by generating standalone binaries that serve predictions via a local HTTP API. Developers can load models with a single command and achieve microsecond-level latency for inference tasks.

Built for teams requiring fast, predictable, and portable inference, Timber targets fraud detection systems, edge/IoT deployments, and regulated industries like finance and healthcare. The compiler produces deterministic artifacts suitable for audit trails and compliance requirements. Platform teams can replace Python model-serving stacks with lightweight native binaries that require no external dependencies.

Benchmarks show 336x speedup over Python XGBoost inference on Apple M2 Pro hardware, with 10,000 single-sample predictions completing in microseconds. The tool supports JSON exports from major frameworks and includes a technical paper with reproducible benchmarks. While ONNX support focuses on tree ensembles and CatBoost requires JSON formats, Timber provides a compelling alternative for production deployments where latency and footprint matter more than development convenience.