which gives you quicker access to the new stuff, but the effort is about the same as writing a new implementation in another language Not really. You can just

which gives you quicker access to the new stuff, but the effort is about the same as writing a new implementation in another language Not really. You can just serialize a Tensorflow graph, freeze the variables as constants, and then load and run the graph using Tensorflow's small and convenient C API. There are bindings to the C API for multiple languages (e.g. Rust and Go). This is how I use neural networks in my Rust (and formerly Go) programs: I just build and train the graph in Python and then use it in Rust. Newer versions of Tensorflow also have the XLA compiler, which compiles a graph to executable code that you can link directly into an application. I haven't tried this approach yet, since the C API serves me well, but it looks promising.