Automating the lifecycle of LLM inference nodes on AWS with intelligent agentic workflows. Integrated with Amazon Bedrock Agents for autonomous infrastructure management.
Our platform leverages AI to automatically diagnose AWS instance health. When inference nodes (like G5 instances) become overloaded or encounter anomalies, AI triggers AWS Auto Scaling to maintain optimal performance.
Integrated with Amazon Bedrock Agents
AI predicts AWS Spot Instance price fluctuations and automatically deploys AI models to the most cost-effective nodes. This demonstrates deep expertise in AWS Spot Fleet architecture and cost optimization.
Up to 70% Cost Reduction
// AI Node Cluster Configuration
cluster: deploynode-aws-01
model: llama-3.1-70b
region: us-west-2
scaling: auto-agent
instance_type: g5.2xlarge
spot_strategy: cost-optimized
Define your AI node clusters with simple YAML configuration. Our platform handles the rest, from provisioning to scaling and monitoring. Integrated with AWS CloudFormation for infrastructure as code.
We leverage AWS Key Management Service (KMS) to protect user model weights and data privacy. All node communications are encrypted using AWS Certificate Manager, ensuring end-to-end security for your AI workloads.
Model weights and data protected by AWS KMS
Fine-grained access control with AWS IAM
SOC 2 and GDPR compliant infrastructure
"DeployNode.Site has transformed our AI infrastructure management. We've reduced our AWS costs by 65% while improving model inference performance by 40%. The self-healing nodes feature has eliminated 99% of our downtime."
"The intelligent spot instance orchestration has been a game-changer for our LLM inference workloads. We're now running 3x more models with the same budget, and the AWS integration is seamless."
"DeployNode.Site's integration with Amazon Bedrock Agents has enabled us to build autonomous infrastructure management workflows that scale with our growing AI demands."
Deploy and manage large-scale LLM inference fleets across multiple AWS regions with centralized orchestration and cost optimization.
Enable research teams to quickly provision GPU instances for model training and inference with automatic cost controls and scheduling.
As an AI-native infrastructure platform, we project over $50,000+ in AWS SageMaker/EC2 consumption over the next 12 months. Our intelligent workflows drive significant AWS usage through automated node provisioning and management.