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EcoHash vs Novita: Pricing, Specs, Strengths & Weaknesses

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

Overview

Attribute
Provider type
Specialist
Specialist
Founded
2024
2023
Headquarters
United States
San Francisco, CA
Billing model
On-demand
Per-token (inference), On-demand / Spot (GPU cloud)
Min commitment
None
None
Support tier
Community → Standard
Community → Enterprise
Regions
1 regions
3 regions

Strengths & Best For

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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Novita

Novita has grown into a dual-product platform: a GPU cloud for training and fine-tuning (H100 SXM, A100, RTX 4090) and a high-volume LLM inference API serving 100+ open-source models at some of the lowest per-token rates in the market. In 2025–2026 Novita expanded its model catalog to include Llama 3.1/3.2, Qwen 2.5, DeepSeek V3/R1, and Mistral variants, and introduced batch inference pricing for high-volume workloads at up to 50% discount. The GPU cloud side offers fast provisioning (typically under 3 minutes), pre-built Docker environments for PyTorch and JAX, and persistent storage volumes. A practical choice for cost-conscious teams that want both raw GPU access and a cheap inference API from one provider.

Strengths
  • Among the lowest per-token LLM inference pricing in the market
  • Batch inference API at up to 50% discount for high-volume workloads
  • 100+ open-source models including Llama 3.2, Qwen 2.5, DeepSeek R1
  • Fast GPU provisioning — typically under 3 minutes
  • Pre-built PyTorch and JAX environments reduce setup time
Weaknesses
  • Smaller provider — cluster scale limited vs CoreWeave or Lambda for very large jobs
  • Inference API latency trails Fireworks and Cerebras on speed benchmarks
  • Less mature enterprise support and SLA options
Best For
Cost-optimized LLM inference at scaleStartups needing cheap inference API + GPU training from one vendorBatch processing workloads with high token volumesTeams fine-tuning Llama or Qwen on a budget
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Key differences

EcoHash

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

Novita

GPU cloud and LLM inference API — H100, A100, and 100+ open-source models

AttributeEcoHashNovita
Provider typespecialistspecialist
Billing modelOn-demandPer-token (inference), On-demand / Spot (GPU cloud)
Min commitmentNoneNone
Regions1 (US)3 (US, EU, APAC)
Support tierCommunity → StandardCommunity → Enterprise
Best forLarge-context LLM inference needing maximum VRAM, Teams wanting RTX PRO 6000 on-demandCost-optimized LLM inference at scale, Startups needing cheap inference API + GPU training from one vendor
Founded / HQ2024 · United States2023 · San Francisco, CA