Hosted model catalog
Browse more than 100 models across language, vision, speech, media-generation, embedding, reranking, and classification categories.
DeepInfra
Freemium
DeepInfra provides hosted machine-learning model inference and on-demand GPU instances for developers and teams. Its catalog covers text, image, audio, video, embedding, reranking, and other model workloads with pay-as-you-go pricing.
What is DeepInfra?
DeepInfra is a machine-learning inference and infrastructure platform for running hosted models and renting GPUs on demand. Its model catalog spans automatic speech recognition, embeddings, reranking, text generation, text-to-image, text-to-music, text-to-speech, text-to-video, world models, and zero-shot image classification.
The platform is designed around usage-based billing. Language models can use per-token pricing, while most other models are billed by inference execution time. DeepInfra also offers on-demand GPU instances for training, fine-tuning, and inference, with container setup, selectable base images, and hourly billing.
What can DeepInfra do?
Pay-as-you-go inference
Use token pricing for some language models and execution-time billing for most other models, without long-term contracts or upfront costs.
On-demand GPU instances
Rent NVIDIA B200 GPUs in single-card or multi-GPU configurations, with pricing billed by the minute and no egress fees stated on the GPU page.
Container-based GPU setup
Select a GPU tier, name a container, choose a base image such as TensorFlow, PyTorch, or CUDA, and connect using a generated SSH command.
Privacy and security commitments
The site states a zero-retention policy for inputs, outputs, and user data, and identifies DeepInfra as SOC 2 and ISO 27001 certified.
Inference infrastructure
DeepInfra says it operates inference-optimized infrastructure in secure, US-based data centers and can tailor inference around cost, latency, throughput, or scale.
Use Cases
“Deploying language-model applications”
Teams can select hosted text-generation models from families including DeepSeek, Qwen, Llama, Kimi, Gemini, and others, then pay for token usage.
“Multimodal and media workflows”
Developers can evaluate or run workloads involving image, audio, and video inputs or outputs using the corresponding model categories in the catalog.
“Model experimentation and fine-tuning”
ML practitioners can launch GPU containers with PyTorch, TensorFlow, or CUDA base images for training, fine-tuning, or inference tasks.
“Scaling inference usage”
Applications with changing demand can use usage-based model inference or add GPU capacity without committing to a long-term infrastructure contract.
Frequently Asked Questions
What does DeepInfra provide?
DeepInfra provides hosted machine-learning model inference and on-demand GPU instances. Its catalog includes language, speech, image, video, embedding, reranking, and classification models.
How is DeepInfra priced?
Some language models use per-token pricing, while most other models are billed according to inference execution time. GPU instances use usage-based hourly pricing, with the GPU page also describing billing by the minute and no egress fees.
Can I use DeepInfra for training or fine-tuning?
Yes. The GPU instances page describes launching containers for training, fine-tuning, and inference, and lists TensorFlow, PyTorch, and CUDA among the available base-image choices.
How do I start a GPU instance?
The documented workflow is to choose a GPU tier, name a container, select a base image, copy the generated SSH command, and launch the configured container.
Does DeepInfra state a data-retention policy?
Yes. The home page states that DeepInfra has a zero-retention policy for inputs, outputs, and user data. It also states that the company is SOC 2 and ISO 27001 certified.
Quick Facts
- Product type
- Machine-learning inference and GPU infrastructure platform
- Model categories
- Text generation, speech, image, video, embeddings, reranking, world models, and zero-shot image classification
- GPU offering
- On-demand NVIDIA B200 instances, from single GPUs to multi-GPU configurations
- Billing
- Pay-as-you-go; token-based or inference-time pricing depending on the model
- GPU workflow
- Container launch with selectable TensorFlow, PyTorch, or CUDA base images
- Source domain
- deepinfra.com
DeepInfra Alternatives
Hyperstack
www.hyperstack.cloud
Hyperstack is a cloud GPU platform for running AI and machine learning workloads, including training, inference, data analytics, and model development. It also provides AI Studio, virtual machines, and managed Kubernetes for deploying and operating GPU-backed workloads.
Together AI
together.ai
Together AI is an AI cloud platform for inference, fine-tuning, GPU clusters, sandboxes, and managed storage.
Hyperbolic
hyperbolic.xyz
Hyperbolic is an open-access GPU and AI cloud for deploying on-demand H100, H200, B200, and other GPU capacity. It supports experimentation, training, fine-tuning, inference, and production workloads through on-demand instances, reserved clusters, and Private Cloud infrastructure.
Lambda
lambda.ai
Lambda provides cloud GPU compute for AI training, fine-tuning, inference, and prototyping. Teams can launch on-demand GPU instances, use production-ready 1-Click Clusters, or discuss reserved and single-tenant infrastructure for larger workloads.
CoreWeave
www.coreweave.com
CoreWeave is an AI-focused cloud platform that combines GPU infrastructure, storage, networking, orchestration, and operational tooling for training and serving AI workloads. It supports teams moving from model experiments to production systems, including reinforcement-learning and agent-development workflows.
Cloudflare Workers AI
developers.cloudflare.com
Cloudflare Workers AI lets developers run open-source machine learning models through serverless GPUs on Cloudflare’s global network. Models can be invoked from Workers, Pages, or applications using the Cloudflare API without managing GPU infrastructure.