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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.

Atlas Cloud logo
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
Groq logo
Groq
✓ verifiedFreemium

Fast, low-cost AI inference provider running LLMs on custom LPU chips via GroqCloud's pay-as-you-go API.

3.6M visits/mo
Modal logo
Modal
✓ verifiedFreemium

Serverless AI cloud for running inference, training and sandboxes on GPUs with fast cold starts and pay-per-use billing.

988K visits/mo
Replicate AI logo
Replicate AI
✓ verifiedPaid

Pay-per-use cloud API to run, fine-tune, and deploy thousands of open-source and proprietary AI models with one line of code.

1.3M visits/mo17K saves
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
Pricing
Seedance 2.0 video: from $0.09/sec
GPT Image 2: from $0.009/image
Nano Banana 2: from $0.04/image
GPT-OSS 20B: $0.075 per 1M input tokens ($0.30 per 1M output)
GPT-OSS 120B: $0.15 per 1M input tokens
Starter: $0/mo + compute ($30 free credit)
Team: $250/mo + compute
CPU (Small): $0.000025/sec ($0.09/hr)
Nvidia A100 80GB: $0.0014/sec ($5.04/hr)
Nvidia H100: $0.001525/sec ($5.49/hr)

Free trial available

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
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
  • LPU custom inference hardware
  • GroqCloud tokens-as-a-service API
  • High-speed, low-latency inference
  • Pay-as-you-go token pricing
  • Free API key to start
  • Broad open-model support
  • Serverless GPU compute defined in Python
  • Sub-second container cold starts
  • Autoscale 0 to 1000+ GPUs
  • Inference, training and batch workloads
  • Secure sandboxes for untrusted code
  • Built-in logging and observability
  • One-line API calls to run community and proprietary AI models
  • Support for image, video, speech, and LLM generation models
  • Fine-tuning and custom model deployment via Cog
  • Per-second usage billing on shared or dedicated hardware
  • Automatic scaling for high-traffic private models
  • Thousands of community-published models with production APIs
  • 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
Use cases
  • Integrate video and image generation
  • Access many LLMs through one API
  • Build multimodal AI applications
  • Batch generate and prototype cheaply
  • Running LLM inference at high speed
  • Cutting inference costs at scale
  • Powering low-latency AI chat apps
  • Serving models via a hosted API
  • Deploying and scaling model inference
  • Fine-tuning and training models
  • Running batch/parallel AI jobs
  • Executing untrusted code in sandboxes
  • Developers embedding image/video/speech generation into an app via API
  • Teams deploying and scaling their own fine-tuned models
  • Builders comparing outputs from multiple AI models in one playground
  • Companies avoiding GPU infrastructure management for ML inference
  • Serving open models in production apps and agents
  • Fine-tuning models on private data
  • Powering code assistants, chatbots and RAG at scale
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