Compare tools
Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
⇄ Comparison dimension — pick the market you're actually shopping in
✕
Atlas Cloud
✓ verifiedPaid
Unified pay-per-use API serving 400+ multimodal AI models (image, video, audio, 3D, LLM) through one OpenAI-compatible key.
958K visits/mo
✕
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
✕
OpenRouter
✓ verifiedFreemium
Unified API gateway that routes requests to 400+ LLMs across 70+ providers with failover and no subscription.
17M visits/mo
Pricing
Seedance 2.0 video: from $0.09/sec
GPT Image 2: from $0.009/image
Nano Banana 2: from $0.04/image
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
Free: $0 (free models only, 50 requests/day)
Pay-as-you-go: 5.5% platform fee on inference
Core features
- ✦400+ AI models via one unified API
- ✦Multimodal coverage: image, video, audio, 3D, LLM
- ✦On-demand, pay-per-use pricing
- ✦Day-0 access to new state-of-the-art models
- ✦OpenAI-compatible single key
- ✦SOC 2 and HIPAA compliance, 99.99% uptime
- ✦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%
- ✦One unified, OpenAI-compatible API for 400+ models
- ✦Automatic provider failover for higher uptime
- ✦Edge routing for low latency
- ✦Custom data and provider policies
- ✦Pay-as-you-go credits usable across any model
Use cases
- →Integrate video and image generation
- →Access many LLMs through one API
- →Build multimodal AI applications
- →Batch generate and prototype cheaply
- →Train large AI/ML models on GPU clusters
- →Run scalable inference workloads
- →Store and manage large training datasets
- →Run Slurm/Kubernetes AI pipelines
- →Accessing many LLMs through one integration
- →Adding provider redundancy to AI apps
- →Comparing model price and performance
- →Powering agents and AI-native products
Visit