Agent Steps Multiply Token Demand

The key implication is that inference vendors win much more from better workflows than from more end users. In a classic copilot flow, one prompt often maps to one model response. In an agent flow, the model plans, calls tools, checks results, retries, reranks documents, and may use vision or speech in the same job. That turns one visible task into dozens of billable inference steps, which is why support for tool use, structured outputs, async callbacks, embeddings, and reranking matters so much.

• DeepInfra is built for this step explosion. Its OpenAI compatible API covers chat, embeddings, streaming, structured outputs, tool calling, and reasoning controls, while its native API adds speech, OCR, classification, and other model types that agent pipelines need beyond plain text.
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• This changes the revenue unit from user seats to workflow depth. DeepInfra charges shared inference by token for language models and by execution time for many other models, so a document agent that reads files, reranks passages, calls tools, and drafts an answer can generate far more spend than a simple chat session.
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• Competitors are moving the same way. Fireworks is packaging transcription, language models, tool calling, and streaming for voice agents, and OpenAI and Anthropic both now expose official tool use and structured output primitives. The battleground is shifting from single model hosting to running whole agent loops reliably in production.
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Going forward, the biggest inference platforms will look less like chat APIs and more like operating systems for AI work. As coding agents, research agents, and multimodal business software move into production, providers that can serve many model calls, many modalities, and many retries inside one account will capture a larger share of application spend.