AI-coding · AI architecture
How AI-coding companies build with AI — which foundational models they run and how directly, ranked by proximity to the model. Avg Foundation Proximity Score 88/100.
AI-coding · GCP · Native (own / self-hosted weights)
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 · AWS · Native (own / self-hosted weights)
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]
AI-coding · Multi-Cloud · Native (own / self-hosted weights)
Privacy-first code assistant with its own models plus optional partner LLMs, deployable air-gapped.
AI-coding · AWS · Direct API
Autonomous SWE agent (Devin) orchestrating Claude + GPT over its own long-horizon planning and code-execution sandboxes.
AI-coding · AWS · Direct API
AI app builder leaning on Anthropic Claude to generate full-stack apps from prompts.
AI-coding · AWS · Direct API
AI IDE (formerly Codeium) routing to Claude + GPT over its own retrieval/indexing engine.
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