VectorLay vs AWS

Overview: Two Very Different Approaches

AWS is the world's largest cloud provider, offering GPU instances through EC2 (p4d, p5, g5 instance families), managed inference via SageMaker, and foundation model access through Bedrock. It's the default choice for enterprises already in the AWS ecosystem.

VectorLay takes a fundamentally different approach. Instead of renting dedicated data center GPUs at premium prices, VectorLay operates a distributed overlay network that routes inference across consumer and enterprise GPUs with automatic failover. The result: dramatically lower costs and built-in resilience.

Pricing Comparison

This is where the difference is most stark. AWS GPU pricing reflects data center costs, premium hardware, and the AWS brand tax. VectorLay leverages distributed consumer GPUs to offer inference at a fraction of the price.

AWS prices are on-demand as of January 2026. AWS p5 pricing shown per-GPU (p5.48xlarge is 8×H100 at ~$98/hr total). Reserved instances and Savings Plans can reduce AWS costs by 30-40% with 1-3 year commitments.

Feature Comparison

When to Choose AWS

AWS is the right choice when you need deep integration with other AWS services, strict compliance requirements (HIPAA, FedRAMP), or you're already deeply invested in the AWS ecosystem with IAM, VPC, and CloudFormation.

• Enterprise compliance requirements (HIPAA, SOC 2, FedRAMP)
• Heavy use of S3, Lambda, DynamoDB in your ML pipeline
• Need managed training + inference (SageMaker)
• Foundation model API access via Bedrock

When to Choose VectorLay

VectorLay is the better choice when cost is a priority, you want hassle-free inference without managing infrastructure, and you need built-in fault tolerance without configuring load balancers and auto-scaling groups.

• Cost-sensitive inference workloads (save 55-80%)
• Need auto-failover without DevOps overhead
• Deploying open-source models (Llama, Mistral, Stable Diffusion)
• Startups and indie developers who don't need enterprise compliance
• Want to avoid egress fees, NAT Gateway charges, and AWS billing surprises

The Hidden Cost of AWS GPUs

The sticker price for AWS GPU instances is just the beginning. In practice, running GPU inference on AWS involves several additional costs that can increase your bill by 30-50%:

• •EBS storage: $0.08-0.16/GB/month for model weights and data
• •Data transfer: $0.09/GB for egress (adds up fast with inference responses)
• •NAT Gateway: $0.045/hr + $0.045/GB processed if you're in a private subnet
• •Elastic IP: $0.005/hr per idle IP address
• •CloudWatch: Logging and monitoring costs for production workloads

With VectorLay, pricing is all-inclusive. You pay per GPU-hour and that's it. No storage fees, no egress charges, no surprise line items on your invoice.

Fault Tolerance: Built-In vs. Build It Yourself

On AWS, achieving high availability for GPU inference requires significant engineering: you need to configure Auto Scaling Groups, Application Load Balancers, health checks, and cross-AZ deployments. If a GPU instance fails, spinning up a replacement can take minutes — an eternity for production inference.

VectorLay's overlay network handles this automatically. If a GPU node goes down, requests are instantly rerouted to healthy nodes with zero configuration. There's no ELB to set up, no health check endpoints to configure, and no ASG launch templates to maintain.

Bottom Line

AWS is the right choice for enterprises that need its vast ecosystem and compliance certifications. But for GPU inference specifically — especially if you're running open-source models and care about cost — VectorLay delivers the same results at a fraction of the price, with fault tolerance that would take weeks to build on AWS.

For most startups, indie developers, and cost-conscious teams, VectorLay is the smarter choice for GPU inference. Save the AWS budget for the services it's actually best at.

Frequently Asked Questions