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Fireworks AI logo
Fireworks AI
✓ verifiedPaid

Developer platform for fast serverless inference and training of open generative models, billed per token or GPU-second.

611K visits/mo1.3K saves
Deep Infra logo
Deep Infra
✓ verifiedPaid

Low-cost inference cloud with developer APIs to run open ML models and on-demand GPUs, billed pay-per-use.

375K visits/mo
Nebius logo
Nebius
✓ verifiedPaid

AI-focused cloud offering NVIDIA GPU compute, storage and MLOps tooling for training and inference at scale, with usage-based pricing.

678K visits/mo133K saves
ZenMux logo
ZenMux
✓ verifiedPaid

Enterprise unified API gateway giving one integration point to 100+ LLMs like Claude, GPT, and Gemini with reliability guarantees.

435K visits/mo11K saves
Pricing
On-Demand H100/H200: $7/GPU-hour
On-Demand B200: $10/GPU-hour
On-Demand B300: $12/GPU-hour
Fine-tuning (LoRA SFT, models up to 16B): from $0.50 per 1M training tokens

No public pricing

NVIDIA H100: $3.85/GPU-hour on-demand ($2.15 preemptible)
NVIDIA H200: $4.50/GPU-hour on-demand
NVIDIA B200: $7.15/GPU-hour on-demand
Shared filesystem storage: $0.08/GiB per month

No public pricing

Core features
  • Serverless per-token inference with OpenAI/Anthropic-compatible APIs
  • On-demand dedicated and reserved GPU deployments
  • Fine-tuning and reinforcement-learning training pipelines
  • Large library of open LLM, vision, image and audio models
  • Optimized inference engine for throughput and latency
  • Hosted inference for many open models
  • Simple REST/OpenAI-compatible API
  • Pay-per-token or per-time billing
  • On-demand GPU rental
  • Broad catalog (Llama, DeepSeek, Qwen, Flux, etc.)
  • DeepStart and DeepCluster tooling
  • NVIDIA GPU instances (H100, H200, B200, GB200)
  • On-demand and preemptible GPU pricing
  • High-performance and object storage
  • Managed Kubernetes and Slurm (Soperator)
  • Serverless and managed inference (Token Factory)
  • MLOps tooling and 24/7 expert support
  • Commitment discounts up to 35%
  • Unified API for 100+ AI models
  • Intelligent request routing across models
  • AI Model Insurance for quality/reliability guarantees
  • Enterprise-focused LLM access layer
Use cases
  • Serving open models in production apps and agents
  • Fine-tuning models on private data
  • Powering code assistants, chatbots and RAG at scale
  • Serving open-source models via API
  • Building AI apps cost-efficiently
  • Renting GPUs for inference or training
  • Scaling inference up and down on demand
  • Train large AI/ML models on GPU clusters
  • Run scalable inference workloads
  • Store and manage large training datasets
  • Run Slurm/Kubernetes AI pipelines
  • Building applications that need failover across multiple LLM providers
  • Consolidating billing/access to many AI models under one API
  • Enterprises requiring guaranteed model output reliability
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