RunPod Pricing Breakdown vs VectorLay (2026)

RTX 4090 (24GB VRAM): Price Comparison

The RTX 4090 is the most popular GPU for inference workloads. With 24GB of GDDR6X VRAM and excellent performance on models up to 34B parameters, it handles the vast majority of production use cases. Here's what it costs on each platform.

RTX 3090 (24GB VRAM): Price Comparison

The RTX 3090 remains a cost-effective choice for inference. It shares the same 24GB VRAM as the 4090 at a lower price point, making it ideal for workloads where raw throughput is less critical than cost per token.

H100 (80GB HBM3): Price Comparison

The NVIDIA H100 is the flagship data-center GPU for large model inference and training. With 80GB of HBM3 memory and the Transformer Engine, it's the gold standard for running 70B+ parameter models at scale.

A100 (80GB HBM2e): Price Comparison

The NVIDIA A100 is the previous-generation data-center GPU that remains widely used for inference and fine-tuning. At 80GB of HBM2e memory, it handles large models comfortably and is typically cheaper than the H100.

The A100 is priced identically on both platforms at $1.64/hr. However, VectorLay's total cost of ownership is still lower once you factor in the hidden fees that RunPod charges on top of the base GPU rate—read on for the full breakdown.

All GPUs: Hourly Rate Summary

Prices as of February 2026. RunPod on-demand pricing shown; community cloud may be lower. VectorLay pricing is flat-rate with no hidden fees.

Total Cost of Ownership: Beyond the Hourly Rate

The hourly GPU rate is only part of the picture. When you run production inference workloads, several additional costs creep in that can significantly impact your monthly bill. Here's where the two platforms diverge.

Egress Fees

Network egress—the cost of sending data out from your GPU instance—is one of the most overlooked expenses in cloud computing. For inference workloads that serve responses to end users, egress can add up quickly.

Storage Costs

ML models are large. A single LLM checkpoint can be 15-70GB, and you often need persistent storage for model weights, caches, and logs.

Support Costs

Hidden Costs: Side-by-Side

Here's a comprehensive look at every cost beyond the GPU hourly rate that affects your total bill.

Annual Savings: 2x RTX 4090 Running 24/7

A typical production inference setup uses two RTX 4090 GPUs running around the clock to serve an LLM or image generation model. Here's what that costs on each platform over a full year, including the base GPU rate and known additional fees.

The $4,380 in savings is based solely on the GPU hourly rate difference. When you add in RunPod's egress and storage surcharges, the real savings with VectorLay are even higher. For teams running multiple GPUs, these savings scale linearly.