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Novita vs Vultr: Pricing, Specs, Strengths & Weaknesses

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

Overview

Attribute
Provider type
Specialist
Specialist
Founded
2023
2014
Headquarters
San Francisco, CA
Matawan, NJ
Billing model
Per-token (inference), On-demand / Spot (GPU cloud)
On-demand (hourly)
Min commitment
None
None
Support tier
Community → Enterprise
Basic → Enterprise
Regions
3 regions
5 regions

Strengths & Best For

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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Vultr

Vultr offers H100, A100, L40S, RTX 6000 Ada, and RTX 4000 Ada GPU instances across 32 global locations with simple hourly billing and no long-term commitment required. A developer-friendly GPU cloud with a clean API, straightforward pricing, and broad geographic coverage for teams needing AI inference or training capacity close to their users. Vultr Cloud GPU instances include dedicated VRAM, NVMe storage, and generous transfer allowances. A solid choice for global deployment of AI workloads without the complexity of hyperscaler pricing models.

Strengths
  • 32 global locations — wide geographic coverage among specialist GPU clouds
  • Simple hourly billing — no long-term commitment
  • H100, A100, L40S, RTX 6000/4000 Ada in catalog
  • Clean API and developer-friendly onboarding
  • Generous transfer allowances included
Weaknesses
  • Smaller GPU catalog than specialist AI clouds
  • Less ML-focused tooling vs CoreWeave or Lambda
  • Limited spot/preemptible GPU options
Best For
Global deployment of AI workloadsSimple inference and training workloadsDeveloper-friendly teams wanting simple billingTeams needing GPU capacity in emerging regions
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Key differences

Novita

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

Vultr

H100, A100, L40S, RTX 4000/6000 Ada — simple hourly billing across 32 global locations

AttributeNovitaVultr
Provider typespecialistspecialist
Billing modelPer-token (inference), On-demand / Spot (GPU cloud)On-demand (hourly)
Min commitmentNoneNone
Regions3 (US, EU, APAC)5 (US, EU, APAC, +2)
Support tierCommunity → EnterpriseBasic → Enterprise
Best forCost-optimized LLM inference at scale, Startups needing cheap inference API + GPU training from one vendorGlobal deployment of AI workloads, Simple inference and training workloads
Founded / HQ2023 · San Francisco, CA2014 · Matawan, NJ