Hosted model catalog
Browse more than 100 models across language, vision, speech, media-generation, embedding, reranking, and classification categories.
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.
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.
Browse more than 100 models across language, vision, speech, media-generation, embedding, reranking, and classification categories.
Use token pricing for some language models and execution-time billing for most other models, without long-term contracts or upfront costs.
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.
Select a GPU tier, name a container, choose a base image such as TensorFlow, PyTorch, or CUDA, and connect using a generated SSH command.
The site states a zero-retention policy for inputs, outputs, and user data, and identifies DeepInfra as SOC 2 and ISO 27001 certified.
DeepInfra says it operates inference-optimized infrastructure in secure, US-based data centers and can tailor inference around cost, latency, throughput, or scale.
Teams can select hosted text-generation models from families including DeepSeek, Qwen, Llama, Kimi, Gemini, and others, then pay for token usage.
Developers can evaluate or run workloads involving image, audio, and video inputs or outputs using the corresponding model categories in the catalog.
ML practitioners can launch GPU containers with PyTorch, TensorFlow, or CUDA base images for training, fine-tuning, or inference tasks.
Applications with changing demand can use usage-based model inference or add GPU capacity without committing to a long-term infrastructure contract.
DeepInfra provides hosted machine-learning model inference and on-demand GPU instances. Its catalog includes language, speech, image, video, embedding, reranking, and classification models.
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.
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.
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.
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.
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