Deep Infra
The cheapest inference API for open-weight models — Llama, Mistral, and more
Deep Infra is an inference-focused API provider specialising in open-weight models at extremely competitive prices. Consistently among the cheapest providers for Llama 3, Mistral, and DeepSeek models, making it the go-to choice for cost-sensitive production inference.
The most price-competitive inference API for open-weight models — often 30–50% cheaper than comparable providers for the same Llama or Mistral model.
- Consistently lowest prices for open-weight models
- Wide model catalog including Llama, Mistral, DeepSeek
- OpenAI-compatible API
- Fast cold start times
- No rate limits on most models
- No proprietary models — open-weight only
- Less enterprise support than larger providers
- Smaller ecosystem than Together AI or Fireworks
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Deep Infra — Frequently Asked Questions
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Full comparison tableDeep Infra LLM pricing overview
Deep Infra is an inference API provider that hosts open-weight and third-party large language models. Deep Infra is an inference-focused API provider specialising in open-weight models at extremely competitive prices. Consistently among the cheapest providers for Llama 3, Mistral, and DeepSeek models, making it the go-to choice for cost-sensitive production inference. Common use cases include Cost-sensitive inference, Llama 3 production, DeepSeek hosting, High-volume batch processing. Headquartered in San Francisco, CA, founded 2023. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Deep Infra vs other LLM providers
Deep Infra competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. The most price-competitive inference API for open-weight models — often 30–50% cheaper than comparable providers for the same Llama or Mistral model. Use the LLM provider comparison tool to see Deep Infra token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose Deep Infra?
Deep Infra's key strengths are: Consistently lowest prices for open-weight models; Wide model catalog including Llama, Mistral, DeepSeek; OpenAI-compatible API. Limitations to consider: No proprietary models — open-weight only; Less enterprise support than larger providers. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding Deep Infra token pricing
Deep Infra charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. Prices shown are sourced from Deep Infra's public pricing page and updated daily. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Deep Infra context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Deep Infra makes sense
Managed inference APIs like Deep Infra eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Deep Infra API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Deep Infra token price data is available in the LLM price history charts.