A next-generation AI platform built on flexibility, sovereignty, and granular control. Run any model, in any environment, composed to fit your business — not the other way around.
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 — 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 is the integrated solution designed to take those production realities head-on.
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 coherent full-stack AI platform — data pipelines, training, serving, and agentic applications — on top of pre-validated reference architectures and adoption guidance.
Every layer of the AI stack is composable. Mix open-source and proprietary models, mix-and-match frameworks, and keep full visibility and control over weights, data flows, and infrastructure. When a new model lands, add a route — innovation cycles stop being blocked on platform rework.
Meet data residency and compliance requirements without sacrificing access to modern AI. Sensitive data does not leave the customer boundary, and GPUs are not shared with other tenants.
Compose the right compute per workload — GPU, Trainium, Inferentia, Graviton — and realize up to 40-60% savings versus comparable EC2 instances. Move freely between token-based pricing and GPU-as-a-service so infrastructure spend tracks business value, not abstractions.
One pane of glass across infrastructure, models, agent behavior, and cost. From GPU utilization and system performance, through per-prompt response quality, to fine-grained cost attribution by model, team, or project — every operational signal is available at the code level.
Pre-validated reference architectures and adoption guidance compress the PoC-to-production journey from months to weeks. Don't redesign from scratch — start building on top of AWS and a proven OSS ecosystem.
Flexible AI delivers flexibility on every axis. When cost, performance, or governance requirements shift, switch on the relevant axis instead of redesigning the whole architecture.
Pick the optimal compute per workload across GPU, AWS Inferentia/Trainium, and Graviton. No lock-in to a single chip family or instance type.
Operate open-source frameworks directly to keep full visibility and authority over model weights, data flows, and infrastructure layout.
Move between token-based pricing and per-hour GPU pricing as the workload demands. Tie infrastructure cost directly to business value.
Move workloads between on-premises, EC2 self-hosted, Amazon Bedrock, and external LLM providers without re-architecting.
Reference patterns covering model optimization, storage, platform engineering, and agentic applications. Adopt incrementally — pick the layer that matches your current state and grow from there.
From the application layer down to cloud, on-premises, and edge infrastructure — Flexible AI spans every layer. Adopt the components you need today and grow into the rest, or stand up the integrated platform in one pass.
GPU-backed model serving, AI Gateway, and Observability are shared building blocks. Compose them with use-case-specific components to fit a wide range of scenarios.
Stand up self-hosted model serving on EKS with AI Gateway (LiteLLM, Kong), inference engines (Ray, SGLang, vLLM), observability (Langfuse, MLflow), and vector DB. Native HuggingFace integration keeps you close to the latest models — with data sovereignty and enhanced observability spanning system metrics through to AI-level signals.
Run self-hosted, AWS-managed (Bedrock, Nova, SageMaker), and external (OpenAI, Gemini, Anthropic) models on a single platform. The AI gateway performs workload-optimized routing — switch models per workload without code changes — and centralized policy management keeps governance consistent across providers.
Start with AWS-native runtimes (Bedrock, Strands, AgentCore), then extend into self-hosted on EKS. Combine custom agent workflows (LangGraph, MCP/A2A) with domain-specific SLMs, and apply heterogeneous compute allocation — Graviton for planning, GPU for reasoning, Trainium/Inferentia for inference — to optimize cost.
Connect AWS Cloud and on-premises into a single cluster via EKS Hybrid Node. Regulated and sensitive workloads stay on-prem; the rest runs on AWS, with automatic fallback during incidents. "Train on-prem, serve globally on AWS" works without re-architecting.
AWS purpose-built AI silicon delivers up to 40-60% cost savings versus comparable EC2 instances and industry-leading OTPS. Native PyTorch support and the Neuron Kernel Interface enable fine-grained tuning; Neuron Explorer traces execution flow.
Whether you are building from scratch or hardening an existing environment, Flexible AI offers an architecture, support model, and starter kit for every stage.
Stories from customers redefining their AI infrastructure strategy with the Flexible AI approach are coming soon.
If you are interested in Flexible AI, reach out via the GitHub channels below, or work with your AWS account team (SA / TAM).