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Serverless AI cloud for running inference, training and sandboxes on GPUs with fast cold starts and pay-per-use billing.
Low-cost inference cloud with developer APIs to run open ML models and on-demand GPUs, billed pay-per-use.
Unified API gateway that routes requests to 400+ LLMs across 70+ providers with failover and no subscription.
Developer platform for fast serverless inference and training of open generative models, billed per token or GPU-second.
GPU rental marketplace with per-second billing across thousands of GPUs, aimed at AI training, inference, and fine-tuning workloads.
No public pricing
No public pricing
- ✦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
- ✦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 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
- ✦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
- ✦On-demand GPU cloud with per-second billing
- ✦Interruptible instances at discounted rates for batch/fault-tolerant jobs
- ✦Reserved capacity with 1, 3, or 6-month terms for steady workloads
- ✦Serverless deployment with autoscale-to-zero for inference endpoints
- ✦Dedicated multi-node clusters with InfiniBand for large-scale training
- ✦Python SDK and CLI plus REST API for programmatic provisioning
- ✦Access to 68+ GPU types across 40+ data centers
- ✦Pre-configured templates for popular open-source models
- →Deploying and scaling model inference
- →Fine-tuning and training models
- →Running batch/parallel AI jobs
- →Executing untrusted code in sandboxes
- →Serving open-source models via API
- →Building AI apps cost-efficiently
- →Renting GPUs for inference or training
- →Scaling inference up and down on demand
- →Accessing many LLMs through one integration
- →Adding provider redundancy to AI apps
- →Comparing model price and performance
- →Powering agents and AI-native products
- →Serving open models in production apps and agents
- →Fine-tuning models on private data
- →Powering code assistants, chatbots and RAG at scale
- →ML engineers training or fine-tuning models on rented GPUs
- →Startups running inference at scale without owning hardware
- →Developers needing quick, low-cost access to specific GPU types
- →Teams building AI agents that autonomously provision compute