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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.
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Anthropic's AI assistant for writing, coding, and analysis across web, mobile, and desktop, plus a developer API.
Cloud platform from the makers of PyTorch Lightning for building, training and deploying AI in browser-based GPU Studios.
Low-cost inference cloud with developer APIs to run open ML models and on-demand GPUs, billed pay-per-use.
Unified API to 500+ AI models (OpenAI, Anthropic, Google, etc.) with OpenAI-compatible calls priced ~20% below official rates.
AI-focused cloud offering NVIDIA GPU compute, storage and MLOps tooling for training and inference at scale, with usage-based pricing.
No public pricing
No public pricing
Free trial available
- ✦Conversational writing and editing
- ✦Code generation and debugging (Claude Code)
- ✦Data analysis and visualization
- ✦Web search plus memory across chats
- ✦Connectors and remote MCP integrations
- ✦Extended thinking for complex tasks
- ✦Browser-based Lightning Studios with on-demand GPUs
- ✦PyTorch Lightning training framework
- ✦Model training, fine-tuning and deployment
- ✦Collaborative, shareable ML environments
- ✦Scalable multi-GPU/multi-node compute
- ✦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
- ✦One key for 500+ models
- ✦OpenAI-compatible API
- ✦Pay-as-you-go credits (~20% below list)
- ✦Multimodal: text, image, video, audio
- ✦Usage analytics and budget alerts
- ✦Integrations (Claude Code, n8n, Zapier, etc.)
- ✦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%
- →Drafting and refining written content
- →Building and debugging software
- →Analyzing datasets for insights
- →Research and learning support
- →Team and enterprise automation
- →Prototype and train ML models in the cloud
- →Fine-tune and deploy foundation models
- →Run reproducible AI experiments collaboratively
- →Serving open-source models via API
- →Building AI apps cost-efficiently
- →Renting GPUs for inference or training
- →Scaling inference up and down on demand
- →Consolidating multi-provider AI billing
- →Switching models without re-integration
- →Powering apps and automation pipelines
- →Benchmarking models in one playground
- →Train large AI/ML models on GPU clusters
- →Run scalable inference workloads
- →Store and manage large training datasets
- →Run Slurm/Kubernetes AI pipelines