Jarvis Labs vs Novita: Pricing, Specs, Strengths & Weaknesses
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Jarvis Labs and Novita. Rates sourced from public APIs — verify current availability and billing terms before purchasing.
Overview
Strengths & Best For
Jarvis Labs is an ML-focused GPU cloud offering H100, A100, and RTX instances with per-second billing, pre-configured environments for PyTorch, TensorFlow, and other popular frameworks, and a simple interface designed for machine learning engineers. On-demand GPU rental with no minimum commitment makes it easy to spin up and tear down instances for training runs, fine-tuning, and AI inference experiments. A popular choice for ML engineers who want pre-built environments and granular per-second billing.
- Per-second billing — no minimum commitment
- Pre-configured PyTorch/TensorFlow environments
- Simple UI designed for ML engineers
- Fast provisioning
- Small provider — limited GPU availability at scale
- No enterprise SLAs
- Limited regions and GPU SKU variety
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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.
- 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
- 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
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Key differences
Jarvis Labs
ML-focused GPU cloud with per-second billing and pre-configured environments
Novita
GPU cloud and LLM inference API — H100, A100, and 100+ open-source models