Architecture comparison
AI/LLM
AI21 Labs develops and serves its own Jamba family of LLMs and related services via its AI21 Studio API and private deployments, while also distributing Jamba models through third‑party clouds such as Azure and upcoming NVIDIA APIs.[5][6][7] Public materials describe deployment options across public cloud and private/on‑prem environments but do not reveal a full production stack beyond this high‑level multi‑cloud posture.[3][5][6]
AI/LLM
Liquid AI builds and serves its own Liquid Foundation Models (LFM and LFM2) with a custom hybrid liquid/convolution/attention architecture, optimized for both data-center and fully on-device deployment across CPUs, GPUs, and NPUs.[1][3][4][9][11] Public materials emphasize hardware-in-the-loop training and edge/PC deployment, but do not expose a full production cloud stack, suggesting a mix of self-hosted/model-serving infrastructure plus OEM/partner integrations rather than a single public hyperscaler.[9][11]