DeepInfra

Company Overview

DeepInfra is a cost-efficient inference platform focused on making open-source AI models accessible via simple APIs.^[1] The value proposition is straightforward: run popular open-source models at the lowest possible per-token cost with minimal setup.

While competitors chase enterprise features, custom silicon, or full-stack platforms, DeepInfra focuses on doing one thing well — serving open-source models cheaply and reliably.

What It Does

• Inference APIs — OpenAI-compatible endpoints for LLMs, embedding, and image models^[2]
• Wide model selection — Llama, Mistral, Mixtral, Qwen, and many more
• Fine-tuning — Basic fine-tuning support for customization
• Embeddings — Text embedding models for RAG and search
• Serverless — No infrastructure management, pay only for usage

How It Works

Call OpenAI-compatible API endpoints with your DeepInfra API key. Models run on optimized GPU infrastructure. DeepInfra handles scaling, optimization, and hardware management. Swap your OpenAI base URL to DeepInfra and point at an open-source model — that's the migration path.

Pricing

• Per-token pricing — transparent, published rates per model
• No minimums — pay only for what you use
• Free tier — limited free tokens for experimentation
• No sales calls — self-serve pricing, no enterprise negotiation required

DeepInfra consistently offers some of the lowest per-token rates in the market for open-source models.

Strengths

• Cost leader — Among cheapest per-token pricing for open-source models
• Simplicity — Minimal setup, OpenAI-compatible, self-serve
• Transparent pricing — Published rates, no surprise bills
• Wide model selection — Quick to add new popular models
• Low barrier — Free tier, no sales process, start in minutes

Weaknesses / Risks

• No custom models — Can't deploy your own trained models
• Limited enterprise features — No SOC 2, HIPAA, or VPC deployment
• No custom silicon — Can't compete with Groq/Cerebras on raw speed
• Thin moat — API wrapper over commodity GPUs is easily replicated
• Limited fine-tuning — Basic compared to Baseten, Together AI, Fireworks
• Smaller company — Less funding and team than major competitors

Competitive Landscape

vs. Together AI: Together AI offers more features (training, clusters) and latest hardware. DeepInfra wins on simplicity and often price.

vs. Groq: Groq is faster. DeepInfra may be cheaper for some models and supports more model types.

vs. Replicate: Replicate has a community marketplace and image/video focus. DeepInfra is more LLM-focused and often cheaper.

vs. OpenRouter: Similar aggregator positioning. DeepInfra runs its own infrastructure; OpenRouter routes to providers.

Ideal User

• Budget-conscious developers running open-source LLMs
• Startups that need cheap inference without enterprise overhead
• Teams migrating from OpenAI to open-source models for cost savings
• Projects where cost per token matters more than custom features

Bottom Line

DeepInfra is the budget option for open-source model inference — no frills, low prices, simple API. It won't win on speed (Groq), features (Baseten), or research (Together AI), but for teams that just want cheap, reliable inference for popular open-source models, it delivers. The risk is thin differentiation: commodity GPU inference is a race to the bottom.

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