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Architecting Flexible AI Platform on AWS

Run Any Model, Anywhere. A next-generation AI platform built on flexibility, sovereignty, and granular control.

Teams running AI in production keep hitting the same wall:

  • "New models drop every week, but plugging them into our existing pipeline is rework every time."
  • "Each business unit runs its own GPUs and deploys models in isolation, so we have no enterprise-wide cost visibility or governance."
  • "API-based model spend is growing faster than we can control."
  • "We want to scale into the cloud, but we also need to keep getting value out of the on-prem GPUs we already paid for."

Flexible AI Platform on AWS — Flexible AI for short — is the integrated answer to those production realities.

It composes AWS's core infrastructure (Graviton, GPU, Trainium / Inferentia, EKS, S3 Vectors) with proven open-source components (LangGraph, Mem0, LiteLLM, Langfuse, vLLM, Qwen, …) so customers can pick the models and frameworks they want and run a full-stack AI platform — data pipelines, training, serving, agentic applications — coherently in one environment, on top of pre-validated reference architectures and adoption guidance.

Core stack

LangGraph · LiteLLM · vLLM · Langfuse · Qwen · Mem0 · EKS · Graviton · Inferentia/Trainium · S3 Vectors

Run Any Model, Anywhere

Three axes meet in Flexible AI:

  • AWS Services

    Graviton, GPU, Trainium / Inferentia, EKS, S3 Vectors — the AWS infrastructure surface Flexible AI runs on.

  • Open-source Frameworks & Models

    LangGraph, LiteLLM, Langfuse, vLLM, Qwen, and the rest of the OSS ecosystem — composable, swap-friendly, no vendor control plane.

  • Deployment Options

    AWS Cloud, on-premises, edge — same architecture pattern, deploy anywhere.

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