DeepInfra raises $107M Series B to scale the inference cloud — read the announcement

At DeepInfra we host the best open source LLM models. We are always working hard to make our APIs simple and easy to use.
Today we are excited to announce a very easy way to quickly try our models like Llama2 70b and Mistral 7b and compare them to OpenAI's models. You only need to change the API endpoint URL and the model name to quickly see if these models are a good fit for your application.
Here is a quick example of how to use the OpenAI Python client with our models:
import openai
# Point OpenAI client to our endpoint
openai.api_base = "https://api.deepinfra.com/v1/openai"
# Just leave the API key empty. You don't need it to try our models.
openai.api_key = ""
# Your chosen model here
MODEL_DI = "meta-llama/Llama-2-70b-chat-hf"
chat_completion = openai.ChatCompletion.create(
model="meta-llama/Llama-2-70b-chat-hf",
messages=[{"role": "user", "content": "Hello world"}],
stream=True,
)
# print the chat completion
for event in chat_completion:
print(event.choices)
To make it as simple as possible you don't even have to create an account with DeepInfra to
try our models. Just pass empty string as api_key and you are good to go. We rate limit the
unauthenticated requests by IP address.
When you are ready to use our models in production, you can create an account at DeepInfra and get an API key. We offer the best pricing for the llama 2 70b model at just $1 per 1M tokens. If you need any help, just reach out to us on our Discord server.
Accelerating Reasoning Workflows with Nemotron 3 Nano on DeepInfraDeepInfra is an official launch partner for NVIDIA Nemotron 3 Nano, the newest open reasoning model in the Nemotron family. Our goal is to give developers, researchers, and teams the fastest and simplest path to using Nemotron 3 Nano from day one.
Kimi K2.6 Model Overview: Architecture, Features & Capabilities<p>Kimi K2.6 is Moonshot AI’s latest flagship open-source model, released on April 20, 2026 under a Modified MIT license. It is a native multimodal agentic model built on a 1-trillion parameter Mixture-of-Experts (MoE) architecture, with 32 billion parameters activated per token. The model is designed for long-horizon coding, autonomous execution, and multi-agent orchestration, and is […]</p>
Build a RAG App With DeepInfra and LangChain<p>Ask a base language model about your company’s refund policy and it will answer with confidence, fluency, and no idea what your policy actually says. The facts live in your PDFs, your internal wiki, and your ticket history, none of which the model has ever seen during training. Retrieval-augmented generation closes that gap by fetching […]</p>
© 2026 DeepInfra. All rights reserved.