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Compare GPU and LLM inference API pricing between Beam and IO.NET. Find the best rates for AI training, inference, and ML workloads.
Provider 1
Provider 2
| GPU Model ↑ | Beam Price | IO.NET Price | Price Diff ↕ | Sources |
|---|---|---|---|---|
A100 PCIE 40GB VRAM • IO.NET | Not Available | — | ||
A100 PCIE 40GB VRAM • | ||||
A100 SXM 80GB VRAM • IO.NET | Not Available | 2x GPU | — | |
A100 SXM 80GB VRAM • | ||||
A30 24GB VRAM • IO.NET | Not Available | 4x GPU | — | |
A30 24GB VRAM • | ||||
A40 48GB VRAM • IO.NET | Not Available | — | ||
A40 48GB VRAM • | ||||
Gaudi 2 96GB VRAM • IO.NET | Not Available | 8x GPU | — | |
Gaudi 2 96GB VRAM • | ||||
H100 PCIe 80GB VRAM • Beam | Not Available | — | ||
H100 PCIe 80GB VRAM • | ||||
H100 SXM 80GB VRAM • IO.NET | Not Available | 4x GPU | — | |
H100 SXM 80GB VRAM • | ||||
H200 141GB VRAM • IO.NET | Not Available | — | ||
H200 141GB VRAM • | ||||
L4 24GB VRAM • IO.NET | Not Available | 8x GPU | — | |
L4 24GB VRAM • | ||||
L40 40GB VRAM • IO.NET | Not Available | — | ||
L40 40GB VRAM • | ||||
L40S 48GB VRAM • IO.NET | Not Available | 4x GPU | — | |
L40S 48GB VRAM • | ||||
RTX 4000 Ada 20GB VRAM • IO.NET | Not Available | — | ||
RTX 4000 Ada 20GB VRAM • | ||||
RTX 4090 24GB VRAM • Beam | Not Available | — | ||
RTX 4090 24GB VRAM • | ||||
RTX 5090 32GB VRAM • Beam | Not Available | — | ||
RTX 5090 32GB VRAM • | ||||
RTX 6000 Ada 48GB VRAM • IO.NET | Not Available | — | ||
RTX 6000 Ada 48GB VRAM • | ||||
A100 PCIE 40GB VRAM • IO.NET | Not Available | — | ||
A100 PCIE 40GB VRAM • | ||||
A100 SXM 80GB VRAM • IO.NET | Not Available | 2x GPU | — | |
A100 SXM 80GB VRAM • | ||||
A30 24GB VRAM • IO.NET | Not Available | 4x GPU | — | |
A30 24GB VRAM • | ||||
A40 48GB VRAM • IO.NET | Not Available | — | ||
A40 48GB VRAM • | ||||
Gaudi 2 96GB VRAM • IO.NET | Not Available | 8x GPU | — | |
Gaudi 2 96GB VRAM • | ||||
H100 PCIe 80GB VRAM • Beam | Not Available | — | ||
H100 PCIe 80GB VRAM • | ||||
H100 SXM 80GB VRAM • IO.NET | Not Available | 4x GPU | — | |
H100 SXM 80GB VRAM • | ||||
H200 141GB VRAM • IO.NET | Not Available | — | ||
H200 141GB VRAM • | ||||
L4 24GB VRAM • IO.NET | Not Available | 8x GPU | — | |
L4 24GB VRAM • | ||||
L40 40GB VRAM • IO.NET | Not Available | — | ||
L40 40GB VRAM • | ||||
L40S 48GB VRAM • IO.NET | Not Available | 4x GPU | — | |
L40S 48GB VRAM • | ||||
RTX 4000 Ada 20GB VRAM • IO.NET | Not Available | — | ||
RTX 4000 Ada 20GB VRAM • | ||||
RTX 4090 24GB VRAM • Beam | Not Available | — | ||
RTX 4090 24GB VRAM • | ||||
RTX 5090 32GB VRAM • Beam | Not Available | — | ||
RTX 5090 32GB VRAM • | ||||
RTX 6000 Ada 48GB VRAM • IO.NET | Not Available | — | ||
RTX 6000 Ada 48GB VRAM • | ||||
| Model ↑ | Beam | IO.NET | Input Diff ↕ |
|---|---|---|---|
DeepSeek | Not available | $0.568 in $2.28 out | — |
DeepSeek | Not available | $1.42 in $2.38 out | — |
DeepSeek | Not available | $0.345 in $0.582 out | — |
DeepSeek | Not available | $1.45 in $2.90 out | — |
DeepSeek | Not available | $0.300 in $1.20 out | — |
Google | Not available | $0.100 in $0.374 out | — |
Zhipu | Not available | $0.064 in $0.400 out | — |
Zhipu | Not available | $0.157 in $0.937 out | — |
Zhipu | Not available | $0.520 in $2.04 out | — |
Zhipu | Not available | $0.880 in $2.37 out | — |
Zhipu | Not available | $0.860 in $2.75 out | — |
Zhipu | Not available | $1.27 in $3.99 out | — |
Zhipu | Not available | $2.13 in $6.88 out | — |
Z AI | Not available | $1.38 in $4.40 out | — |
Z AI | Not available | $0.210 in $0.700 out | — |
Explore how these providers compare to other popular GPU cloud services
Compare Beam with another leading provider
Compare Beam with another leading provider
Compare Beam with another leading provider
Compare Beam with another leading provider
Compare Beam with another leading provider
Compare Beam with another leading provider
Only charged when your code runs, no charges for cold starts or server spin-up
Memory snapshots and GPU checkpoint restore bring containers back in seconds, which Beam reports as up to 35x faster than a traditional cold boot
Run untrusted code safely in isolated environments for AI agents and code interpreters
Pause and resume sessions while maintaining filesystem, memory, and running processes
Scale to zero when idle, burst to thousands of containers in seconds
Bring your own Docker images for full environment control, including running the Docker daemon inside containers
Access to 300,000+ verified GPUs from 139 countries with 6,000+ cluster-ready GPUs
Deploy clusters in under 90 seconds with auto-scaling capabilities
Choose from containers, Ray clusters, or bare metal based on workload needs
Uses the same distributed computing framework that OpenAI used to train GPT-3
AI models, smart agents, and API integration for workflow automation
Kernel-level VPN with secure mesh protocols for data protection
Deploy high-performance inference endpoints with custom models
Secure code execution environments for AI agents
Run large-scale workloads with distributed processing
On-demand GPU clusters for AI/ML workloads with multiple deployment options
AI models, smart agents, and API integration platform
Decentralized pool of GPU providers with unified APIs and competitive pricing.
Serverless GPU, CPU, and sandbox usage billed by the millisecond with no minimum charges
Serverless access to GPUs beyond the RTX 4090 is arranged through a committed-spend agreement
Flat hourly price per machine that already includes the vCPU, RAM, and NVMe storage
Multi-node InfiniBand clusters reserved monthly or yearly, quoted through sales
Per-vCPU and per-GB management fee on top of compute billed directly by your own cloud provider
Persistent volumes and snapshots included up to a capacity threshold, then billed per GB per month
Developer plan free with usage, Team plan with a monthly base fee plus usage, and a contact-sales Growth tier
All plans include monthly free credits that refresh each month
Discounts available for high monthly usage, arranged through sales
Most cost-effective option for distributed ML workloads using Ray framework
Standard containerized deployments with Docker support
Premium pricing for direct hardware access and maximum performance
Dynamic pricing based on actual resource usage with automatic scaling
Sign up on the Beam platform to receive monthly free credits
Create a virtual environment and install the Beam SDK
Configure your API token to connect to Beam
Run a function locally, then deploy it as a web endpoint with the Beam CLI
Create an account on the IO.NET platform with no complex KYC requirements
Purchase $IO tokens for compute payments or add other supported payment methods
Select from containers, Ray clusters, or bare metal based on your workload
Specify GPU requirements, region preferences, and scaling options
Launch your cluster in under 90 seconds and start your AI/ML workloads
30+ regions spanning the US, EU, Asia-Pacific, and Canada, with workloads routed across clouds and regions in real time
Documentation, public Slack community, and a status page; live chat support on paid plans and a private Slack channel on the top tier
Global distributed network across 139 countries with intelligent geographic clustering and latency optimization
Documentation portal, Discord community (500,000+ members), Telegram support, and direct engineering support for GPU and driver questions