Architecture comparison
AI-coding
Magic’s public evidence points to a proprietary, long-context model stack centered on its own LTM models rather than a packaged third-party LLM. The clearest infrastructure signal is a Google Cloud partnership for AI supercomputers, but the public record does not expose a full production data stack or compliance posture.
AI-coding
Poolside builds and trains proprietary foundation models via its internal **Model Factory** (Titan training stack on a ~10K GPU cluster, Apache Iceberg data layer, Kubernetes orchestration) and then deploys those models and agentic systems fully inside customer VPCs/on‑prem through the Poolside Platform, with additional distribution via AWS Bedrock and Trainium-backed inference.[1][2][7][8][9][5][6][11] The production architecture is therefore a proprietary, Kubernetes-based ML/agent stack running on AWS (and customer infrastructure) with an Iceberg-centered data plane and tightly integrated model training, evaluation, and deployment pipelines.[1][2][7][8][9][11]