Amplifikation 360 vs Neural Amp Modeler

Amplifikation 360 is a modular host for building guitar rigs from Kuassa Amplifikations and Efektor plug-ins. It supports free routing with unlimited devices, plus serial, parallel, and crossover container setups. Live and Toggle modes let users switch sessions or individual modules with a hardware MIDI controller. Other tools include a looper/audio player, chromatic tuner, movable interface panels, and tap tempo that can sync to a DAW master tempo. The app can run standalone or as a VST, VST3, Audio Unit, or AAX plug-in on Windows and macOS. Compatible Amplifikations and Efektor plug-ins a user owns are registered as modules inside it. The free plan includes Amplifikation Lite, Efektor Silencer, Efektor G3607 EQ, seven utility modules, and cab IR loading. Individual compatible plug-ins start at $25 one-time; the Amplifikation 360 Bundle is $299 one-time, with taxes excluded. Amplifikation One modules are not available in the host because they use an older engine architecture. The listed minimums are 64-bit Windows 7 or later, or macOS 10.11 or later, with at least 4 GB RAM. The product page states native Apple Silicon support and macOS notarization with Hardened Runtime.

Neural Amp Modeler (NAM) uses deep learning to create models of guitar amplifiers and pedals. The project is free and open source, with three code repositories under permissive licenses that are open to contributions. Users can train models online in Google Colab or install the Python-based trainer locally from PyPI. The Gateway plugin loads and plays snapshot models of gear, while ParametricOD demonstrates modeling across an overdrive's knobs and switches. The plugin repository produces VST3 and AU plugins as well as a standalone desktop application. Models can be shared through TONE3000, a community-organized online library. NAM runs on Windows, macOS, Linux, Raspberry Pi, embedded systems, and websites. Existing A1 models remain supported in NeuralAmpModelerCore and NeuralAmpModelerPlugin. Builders can incorporate NAM's open-source modeling technology into their own products. The training code depends on PyTorch Lightning; version 0.12.3 excludes compromised Lightning versions 2.6.2 and 2.6.3.