Deep Infra

Teams use Deep Infra to plug AI into everyday work. Pick a model from the catalog, send data over an HTTP call, and receive text, audio, images, or transcripts that drop straight into your app. You can chain stages—draft copy with a language model, turn it into speech, or create visuals from a description—to automate complete workflows without touching GPU setup.

Typical scenarios include customer support assistants that compose replies, summarize long tickets, or extract key fields; content pipelines that produce outlines or variations at scale; voice features that generate natural speech for guides, notifications, or training; image workflows that deliver campaign art or product mockups from text prompts; and speech tasks that turn recordings into searchable text for analytics.

Start by trialing a hosted model, then wire the call into your backend, job runner, or data pipeline. As traffic grows, built-in autoscaling absorbs spikes while you keep the same endpoint. When you need strict control over throughput or privacy, move a fine-tuned checkpoint onto dedicated GPUs and keep requests on isolated capacity. Usage-based billing suits both batch operations and interactive features, so you can run nightly jobs or serve live users without capacity planning.

A common flow looks like this: choose a model, add an API key, post a prompt or audio buffer, parse the JSON response, and store the result. For multi-step tasks, run one endpoint per stage and pass outputs along—for example, transcribe with ASR, summarize with an LLM, then synthesize a spoken recap with TTS. This approach helps teams ship features quickly while keeping operations simple.