Konduit provides AI infrastructure for deploying machine learning models across cloud, on-premise, edge and mobile environments, supporting models built in PyTorch, TensorFlow, Keras and ONNX. Its core tools include Konduit Serving for model-serving infrastructure and Kompile for model compilation, built on the Eclipse Deeplearning4j open-source ecosystem, alongside enterprise consulting and support for teams adopting that infrastructure.
Framework-agnostic model deployment – the same infrastructure handling models from several different training frameworks rather than locking a team to one – matters for any organization whose data science team hasn’t standardized on a single tool, which is most real organizations in practice. Being built on and contributing to the Eclipse Foundation’s open ecosystem also signals a different kind of commitment than a purely proprietary vendor, with the underlying tooling not disappearing if the company’s specific commercial offerings change.
The hybrid tooling-plus-consulting model means realizing full value likely involves a real services engagement, not just downloading software and going – worth scoping what’s genuinely self-serve versus what requires Konduit’s own involvement before assuming a purely independent deployment. Pricing isn’t disclosed; expect a direct conversation.
