Deepinfra

Integrate Deepinfra models with Portkey’s AI Gateway

Portkey provides a robust and secure gateway to integrate various Large Language Models (LLMs) into applications, including Deepinfra’s hosted models.

With Portkey, take advantage of features like fast AI gateway access, observability, prompt management, and more, while securely managing API keys through Model Catalog.

Quick Start

Get Deepinfra working in 3 steps:

Python

from portkey_ai import Portkey

# 1. Install: pip install portkey-ai
# 2. Add @deepinfra provider in model catalog
# 3. Use it:

portkey = Portkey(api_key="PORTKEY_API_KEY")

response = portkey.chat.completions.create(
    model="@deepinfra/nvidia/Nemotron-4-340B-Instruct",
    messages=[{"role": "user", "content": "Say this is a test"}]
)

print(response.choices[0].message.content)

import Portkey from 'portkey-ai'

// 1. Install: npm install portkey-ai
// 2. Add @deepinfra provider in model catalog
// 3. Use it:

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY"
})

const response = await portkey.chat.completions.create({
    model: "@deepinfra/nvidia/Nemotron-4-340B-Instruct",
    messages: [{ role: "user", content: "Say this is a test" }]
})

console.log(response.choices[0].message.content)

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL

# 1. Install: pip install openai portkey-ai
# 2. Add @deepinfra provider in model catalog
# 3. Use it:

client = OpenAI(
    api_key="PORTKEY_API_KEY",  # Portkey API key
    base_url=PORTKEY_GATEWAY_URL
)

response = client.chat.completions.create(
    model="@deepinfra/nvidia/Nemotron-4-340B-Instruct",
    messages=[{"role": "user", "content": "Say this is a test"}]
)

print(response.choices[0].message.content)

import OpenAI from "openai"
import { PORTKEY_GATEWAY_URL } from "portkey-ai"

// 1. Install: npm install openai portkey-ai
// 2. Add @deepinfra provider in model catalog
// 3. Use it:

const client = new OpenAI({
    apiKey: "PORTKEY_API_KEY",  // Portkey API key
    baseURL: PORTKEY_GATEWAY_URL
})

const response = await client.chat.completions.create({
    model: "@deepinfra/nvidia/Nemotron-4-340B-Instruct",
    messages: [{ role: "user", content: "Say this is a test" }]
})

console.log(response.choices[0].message.content)

# 1. Add @deepinfra provider in model catalog
# 2. Use it:

curl https://api.portkey.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -d '{
    "model": "@deepinfra/nvidia/Nemotron-4-340B-Instruct",
    "messages": [
      { "role": "user", "content": "Say this is a test" }
    ]
  }'

Tip: You can also set provider="@deepinfra" in Portkey() and use just model="nvidia/Nemotron-4-340B-Instruct" in the request.

Add Provider in Model Catalog

1. Go to Model Catalog → Add Provider
2. Select Deepinfra
3. Choose existing credentials or create new by entering your Deepinfra API key
4. Name your provider (e.g., deepinfra-prod)

Complete Setup Guide →

See all setup options, code examples, and detailed instructions

Supported Models

Deepinfra hosts a wide range of open-source models for text generation. View the complete list:

Deepinfra Models

Browse all available models on Deepinfra

Popular models include:

• nvidia/Nemotron-4-340B-Instruct
• meta-llama/Meta-Llama-3.1-405B-Instruct
• Qwen/Qwen2.5-72B-Instruct

Next Steps

Add Metadata

Add metadata to your Deepinfra requests

Gateway Configs

Add gateway configs to your Deepinfra requests

Tracing

Trace your Deepinfra requests

Fallbacks

Setup fallback from OpenAI to Deepinfra

For complete SDK documentation:

SDK Reference

Complete Portkey SDK documentation