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Key Use Cases

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.

01. Self-hosted Model Serving on AWS

Stand up a self-hosted model serving environment on EKS.

  • AI Gateway: LiteLLM, Kong.
  • Inference Engines: Ray, SGLang, vLLM.
  • Observability: Langfuse, MLflow.
  • Vector DB: Qdrant, Chroma, Milvus.

Native HuggingFace integration keeps you close to the latest models. The result is data sovereignty plus enhanced observability spanning system metrics through to AI-level signals.

02. Hybrid Model Serving

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.
  • Centralized policy management keeps governance consistent across providers.

03. Agentic AI

Start with AWS-native agent runtimes (Bedrock, Strands, AgentCore), then extend into self-hosted on EKS.

  • Custom agent workflows: LangGraph, MCP / A2A.
  • Combine with domain-specific small language models.
  • Heterogeneous compute allocation — Graviton for planning, GPU for reasoning, Trainium / Inferentia for inference — to optimize cost.

Reference workloads: the Loan Buddy agent, OpenClaw DevOps Agent, and OpenClaw Document Writer.

04. Hybrid Cluster

Connect AWS Cloud and on-premises into a single cluster via EKS Hybrid Node.

  • Regulated and sensitive workloads stay on-prem; the rest run on AWS.
  • Automatic fallback to AWS during on-prem incidents.
  • "Train on-prem, serve globally on AWS" works without re-architecting.

05. Cost Optimization with Trainium / Inferentia

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 for fine-grained tuning.
  • Neuron Explorer for execution-flow tracing.

Key Benefits

  • Run Any Model Anywhere

    • Unified access control
    • No vendor lock-in
    • Data residency and regulatory compliance
    • Self-hosted models, AWS-managed services (Bedrock), and external LLMs — all reachable
    • Self-service portal

  • Optimize Costs

    • Optimize model and GPU utilization
    • Apply and orchestrate heterogeneous compute (GPU / Trainium / Graviton) per workload
    • Smooth migration path from Amazon Bedrock to self-hosted

  • Protect Existing AI Investment

    • Bolt-on to existing on-prem environments
    • Hybrid deployment without re-architecting
    • Unified management across on-prem and cloud GPUs

  • Agentic AI & Compute Modernization

    • One environment for autonomous agent operations
    • End-to-end observability across infrastructure, agent behavior, and outputs
    • Full code-level control over workflows

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