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AWS vs EcoHash: Pricing, Specs, Strengths & Weaknesses

Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for AWS and EcoHash. Rates sourced from public APIs — verify current availability and billing terms before purchasing.

Overview

Attribute
Provider type
Hyperscaler
Specialist
Founded
2006
2024
Headquarters
Seattle, WA
United States
Billing model
On-demand, Reserved (1yr/3yr), Spot, Savings Plans
On-demand
Min commitment
None (on-demand)
None
Support tier
Basic → Enterprise
Community → Standard
Regions
5 regions
1 regions

Strengths & Best For

AWS

AWS EC2 P5 instances are powered by NVIDIA H100 Tensor Core GPUs (up to 8× H100, 640 GB HBM3, 3,200 Gbps EFA networking). P5e and P5en instances use NVIDIA H200 GPUs (up to 8× H200, 1,128 GB HBM3e). P5en adds Gen5 PCIe and third-generation EFA for 35% lower latency. All three families deploy in EC2 UltraClusters scaling to 20,000 GPUs with petabit-scale nonblocking networking delivering 20 exaflops aggregate. SageMaker HyperPod manages resilient training clusters at any scale. Amazon Bedrock provides serverless access to DeepSeek v3.2, MiniMax, Kimi 3, Z.AI GLM 5, and Writer Palmyra. 30+ global regions with enterprise SLAs.

Strengths
  • P5/P5e/P5en: H100 and H200 in UltraClusters up to 20,000 GPUs
  • 20 exaflops aggregate compute in EC2 UltraClusters
  • 3,200 Gbps EFA networking with GPUDirect RDMA
  • SageMaker HyperPod for resilient distributed training
  • Amazon Bedrock: serverless access to DeepSeek, MiniMax, Kimi, GLM, Palmyra
  • 30+ global regions with enterprise SLAs and compliance certifications
Weaknesses
  • Highest on-demand GPU rates among all providers
  • Complex pricing — on-demand vs reserved vs spot vs savings plans
  • GPU quota increases require support tickets and can take days
Best For
Hyperscale AI training requiring 1,000+ GPUsEnterprise workloads needing compliance (HIPAA, FedRAMP, SOC 2)Teams already invested in the AWS ecosystemProduction ML inference via SageMaker
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EcoHash

EcoHash is a GPU cloud offering NVIDIA RTX PRO 6000 Blackwell instances with 96GB GDDR7 VRAM alongside an open-model inference API, making it one of the few providers offering the latest RTX PRO 6000 hardware for large-context AI inference and training. The open-model API enables developers to access popular open-source models without managing infrastructure. A strong choice for teams that need maximum VRAM on RTX-class hardware for large-model inference.

Strengths
  • RTX PRO 6000 Blackwell — 96GB GDDR7 VRAM
  • Open-model inference API
  • Large-context inference capability
  • Competitive RTX PRO 6000 pricing
Weaknesses
  • Very new provider — limited track record
  • Smaller scale vs established providers
  • Limited documentation and ecosystem
Best For
Large-context LLM inference needing maximum VRAMTeams wanting RTX PRO 6000 on-demandOpen-model API users
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Live GPU Pricing

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Region Coverage

AWS — 5 regions
us-east-1us-west-2eu-west-1ap-southeast-1ap-northeast-1

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Key differences

AWS

P5 (H100), P5e/P5en (H200), UltraClusters up to 20,000 GPUs — hyperscale AI infrastructure

EcoHash

RTX PRO 6000 GPU cloud with an open-model inference API

AttributeAWSEcoHash
Provider typehyperscalerspecialist
Billing modelOn-demand, Reserved (1yr/3yr), Spot, Savings PlansOn-demand
Min commitmentNone (on-demand)None
Regions5 (us-east-1, us-west-2, eu-west-1, +2)1 (US)
Support tierBasic → EnterpriseCommunity → Standard
Best forHyperscale AI training requiring 1,000+ GPUs, Enterprise workloads needing compliance (HIPAA, FedRAMP, SOC 2)Large-context LLM inference needing maximum VRAM, Teams wanting RTX PRO 6000 on-demand
Founded / HQ2006 · Seattle, WA2024 · United States