Technology Radar
Open-weight models, where model weights, architectures and inference code are publicly released, have reached a significant inflection point. Attracted by their lower total cost of ownership, we’ve been evaluating the latest models from families such as DeepSeek, GLM and Kimi for production delivery work requiring complex reasoning and high-quality code generation, and we’re seeing good results.
Beyond cost efficiency, open-weight models provide deployment flexibility, freedom from vendor lock-in and stronger data sovereignty. Engineering teams can right-size their deployments, from lightweight flash models to hundred-billion- or trillion-parameter mixture of experts (MoE) models, and perform private fine-tuning using domain data. Although self-hosting requires greater operational effort, major cloud providers now offer managed open-weight models that lower the barrier to adoption. Rather than treating them merely as budget options, organizations should evaluate open-weight models for a broad range of enterprise workloads, well beyond coding.
